Datasets:

Vidushee
/

iclr-papers-with-code-1k

Error code:   DatasetGenerationError
Exception:    ArrowTypeError
Message:      ("Expected bytes, got a 'int' object", 'Conversion failed for column ryxmrpNtvH with type object')
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Missing a name for object member. in row 0
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 331, in _generate_tables
                  pa_table = pa.Table.from_pandas(df, preserve_index=False)
                File "pyarrow/table.pxi", line 4796, in pyarrow.lib.Table.from_pandas
                File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 651, in dataframe_to_arrays
                  arrays = [convert_column(c, f)
                            ~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 639, in convert_column
                  raise e
                File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 633, in convert_column
                  result = pa.array(col, type=type_, from_pandas=True, safe=safe)
                File "pyarrow/array.pxi", line 365, in pyarrow.lib.array
                File "pyarrow/array.pxi", line 91, in pyarrow.lib._ndarray_to_array
                  check_status(NdarrayToArrow(pool, values, mask, from_pandas,
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowTypeError: ("Expected bytes, got a 'int' object", 'Conversion failed for column ryxmrpNtvH with type object')
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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forum string	year int64	status string	domain string	title string	abstract string	avg_score float64	std_score float64	num_reviews int64	code_url string	keywords list	primary_area string	openreview_url string	kw_domain string	kw_conf int64	domain_llm string	feasibility string	feasibility_reason string	est_peak_vram_gb int64	est_model_params string
rJe4_xSFDB	2,020	accepted_poster	CV	Lipschitz constant estimation of Neural Networks via sparse polynomial optimization	We introduce LiPopt, a polynomial optimization framework for computing increasingly tighter upper bound on the Lipschitz constant of neural networks. The underlying optimization problems boil down to either linear (LP) or semidefinite (SDP) programming. We show how to use the sparse connectivity of a network, to signif...	6.667	0.943	3	https://drive.google.com/drive/folders/1bkj0H6Thgd9sjRloyq9NBP0uO0v704E9?usp=sharing	[ "robust networks", "Lipschitz constant", "polynomial optimization" ]	null	https://openreview.net/forum?id=rJe4_xSFDB	CV	1	null	null		null	unknown
r1g6ogrtDr	2,020	accepted_poster	CV	Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring in Data	Equivariance is a nice property to have as it produces much more parameter efficient neural architectures and preserves the structure of the input through the feature mapping. Even though some combinations of transformations might never appear (e.g. an upright face with a horizontal nose), current equivariant architect...	6.667	0.943	3	https://www.dropbox.com/sh/2gghao89strdotw/AAAYJ6XclnfeoS3AfN9Z-n5Wa?dl=0	[ "Equivariant Neural Networks", "Attention Mechanisms", "Deep Learning" ]	null	https://openreview.net/forum?id=r1g6ogrtDr	CV	2	null	null		null	unknown
S1xsG0VYvB	2,020	rejected	CV	Understanding the functional and structural differences across excitatory and inhibitory neurons	One of the most fundamental organizational principles of the brain is the separation of excitatory (E) and inhibitory (I) neurons. In addition to their opposing effects on post-synaptic neurons, E and I cells tend to differ in their selectivity and connectivity. Although many such differences have been characterized ex...	6.667	0.943	3	https://anonymous.4open.science/repository/d0ae905f-4171-42b0-94b5-abf03d6414aa	[ "Neuroscience" ]	null	https://openreview.net/forum?id=S1xsG0VYvB	CV	1	null	null		null	unknown
Hye1RJHKwB	2,020	accepted_poster	CV	Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators	Generative adversarial networks (GANs) have shown great success in applications such as image generation and inpainting. However, they typically require large datasets, which are often not available, especially in the context of prediction tasks such as image segmentation that require labels. Therefore, methods such as...	6.667	0.943	3	https://www.dropbox.com/s/gtc7m7pc4n2yt05/source.zip?dl=1	[ "Adversarial Learning", "Semi-supervised Learning", "Image generation", "Image segmentation", "Missing Data" ]	null	https://openreview.net/forum?id=Hye1RJHKwB	CV	10	null	null		null	unknown
H1gB4RVKvB	2,020	accepted_poster	CV	Recurrent neural circuits for contour detection	We introduce a deep recurrent neural network architecture that approximates visual cortical circuits (Mély et al., 2018). We show that this architecture, which we refer to as the 𝜸-net, learns to solve contour detection tasks with better sample efficiency than state-of-the-art feedforward networks, while also exhibiti...	6.667	0.943	3	https://mega.nz/#F!DrA12KCT!4BC_rfjqN5pXBbCl9Ay1DA	[ "Contextual illusions", "visual cortex", "recurrent feedback", "neural circuits" ]	null	https://openreview.net/forum?id=H1gB4RVKvB	CV	4	null	null		null	unknown
SJxDDpEKvH	2,020	accepted_poster	CV	Counterfactuals uncover the modular structure of deep generative models	Deep generative models can emulate the perceptual properties of complex image datasets, providing a latent representation of the data. However, manipulating such representation to perform meaningful and controllable transformations in the data space remains challenging without some form of supervision. While previous w...	6.333	2.357	3	https://www.dropbox.com/sh/4qnjictmh4a2soq/AAAa5brzPDlt69QOc9n2K4uOa?dl=0	[ "generative models", "causality", "counterfactuals", "representation learning", "disentanglement", "generalization", "unsupervised learning" ]	null	https://openreview.net/forum?id=SJxDDpEKvH	CV	2	null	null		null	unknown
SJgIPJBFvH	2,020	accepted_poster	CV	Fantastic Generalization Measures and Where to Find Them	Generalization of deep networks has been intensely researched in recent years, resulting in a number of theoretical bounds and empirically motivated measures. However, most papers proposing such measures only study a small set of models, leaving open the question of whether these measures are truly useful in practice. ...	6.333	2.357	3	https://drive.google.com/open?id=1_6oUG94d0C3x7x2Vd935a2QqY-OaAWAM	[ "Generalization", "correlation", "experiments" ]	null	https://openreview.net/forum?id=SJgIPJBFvH	CV	1	null	null		null	unknown
rJlnxkSYPS	2,020	accepted_poster	CV	Unsupervised Clustering using Pseudo-semi-supervised Learning	In this paper, we propose a framework that leverages semi-supervised models to improve unsupervised clustering performance. To leverage semi-supervised models, we first need to automatically generate labels, called pseudo-labels. We find that prior approaches for generating pseudo-labels hurt clustering performance bec...	6	0	3	https://drive.google.com/open?id=1rvlTYnSDD9UVAy2FkKilM4fGSE75v7Id	[ "Unsupervised Learning", "Unsupervised Clustering", "Deep Learning" ]	null	https://openreview.net/forum?id=rJlnxkSYPS	CV	2	null	null		null	unknown
HyezmlBKwr	2,020	rejected	CV	Test-Time Training for Out-of-Distribution Generalization	We introduce a general approach, called test-time training, for improving the performance of predictive models when test and training data come from different distributions. Test-time training turns a single unlabeled test instance into a self-supervised learning problem, on which we update the model parameters before ...	6	0	3	https://drive.google.com/open?id=1xw-NylSnEjyHs67TXAptviOsx4YuSuZZ	[ "out-of-distribution", "distribution shifts" ]	null	https://openreview.net/forum?id=HyezmlBKwr	CV	1	CV	fits40	ResNet classification with test-time training on CIFAR/ImageNet fits	16	25M
HJlTpCEKvS	2,020	rejected	CV	Which Tasks Should Be Learned Together in Multi-task Learning?	Many computer vision applications require solving multiple tasks in real-time. A neural network can be trained to solve multiple tasks simultaneously using 'multi-task learning'. This saves computation at inference time as only a single network needs to be evaluated. Unfortunately, this often leads to inferior overall ...	6	0	3	https://anonymous.4open.science/r/6cd16de7-0d82-454f-86ef-b540591cd782/	[ "multi-task learning", "Computer Vision" ]	null	https://openreview.net/forum?id=HJlTpCEKvS	CV	2	CV	fits40	Per-network Taskonomy encoder-decoder training fits single GPU	24	50M
HJe_Z04Yvr	2,020	accepted_poster	CV	Adjustable Real-time Style Transfer	Artistic style transfer is the problem of synthesizing an image with content similar to a given image and style similar to another. Although recent feed-forward neural networks can generate stylized images in real-time, these models produce a single stylization given a pair of style/content images, and the user doesn't...	6	0	3	https://goo.gl/PVWQ9K	[ "Image Style Transfer", "Deep Learning" ]	null	https://openreview.net/forum?id=HJe_Z04Yvr	CV	4	null	null		null	unknown
SkxUrTVKDH	2,020	rejected	CV	Split LBI for Deep Learning: Structural Sparsity via Differential Inclusion Paths	Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error. However, compressive networks are desired in many real world applications and direct training of small networks may be trapped in local optima. ...	5.667	2.055	3	https://anonymous.4open.science/repository/d22bbbc8-50d5-4e60-b2e8-4ded4e93db63/Split_LBI_code	[]	null	https://openreview.net/forum?id=SkxUrTVKDH	CV	1	null	null		null	unknown
HkxJHlrFvr	2,020	rejected	CV	Angular Visual Hardness	The mechanisms behind human visual systems and convolutional neural networks (CNNs) are vastly different. Hence, it is expected that they have different notions of ambiguity or hardness. In this paper, we make a surprising discovery: there exists a (nearly) universal score function for CNNs whose correlation with huma...	5.667	3.3	3	https://drive.google.com/drive/folders/1AqAhFI93cGT4uut05c5rQWxCEeeVA_TG?usp=sharing	[ "angular similarity", "self-training", "hard samples mining" ]	null	https://openreview.net/forum?id=HkxJHlrFvr	CV	7	null	null		null	unknown
HkgtJRVFPS	2,020	rejected	CV	Topological Autoencoders	We propose a novel approach for preserving topological structures of the input space in latent representations of autoencoders. Using persistent homology, a technique from topological data analysis, we calculate topological signatures of both the input and latent space to derive a topological loss term. Under weak theo...	5.667	2.055	3	https://osf.io/abuce/?view_only=f16d65d3f73e4918ad07cdd08a1a0d4b	[ "Topology", "Deep Learning", "Autoencoders", "Persistent Homology", "Representation Learning", "Dimensionality Reduction", "Topological Machine Learning", "Topological Data Analysis" ]	null	https://openreview.net/forum?id=HkgtJRVFPS	CV	1	null	null		null	unknown
BygacxrFwS	2,020	rejected	CV	Fractional Graph Convolutional Networks (FGCN) for Semi-Supervised Learning	Due to high utility in many applications, from social networks to blockchain to power grids, deep learning on non-Euclidean objects such as graphs and manifolds continues to gain an ever increasing interest. Most currently available techniques are based on the idea of performing a convolution operation in the spectral...	5.667	2.055	3	https://www.dropbox.com/sh/ajtz6inf677nkcv/AACXkFRZjRrCkxYkxJDNfks0a?dl=0.	[ "convolutional networks", "node classification", "Levy flight", "graph-based semi-supervised learning", "local graph topology" ]	null	https://openreview.net/forum?id=BygacxrFwS	CV	3	null	null		null	unknown
BJena3VtwS	2,020	rejected	CV	The Visual Task Adaptation Benchmark	Representation learning promises to unlock deep learning for the long tail of vision tasks without expansive labelled datasets. Yet, the absence of a unified yardstick to evaluate general visual representations hinders progress. Many sub-fields promise representations, but each has different evaluation protocols that a...	5.667	2.055	3	https://www.dropbox.com/s/4ph8hfcom9xm15z/task_adaptation.zip?dl=0	[ "representation learning", "self-supervised learning", "benchmark", "large-scale study" ]	null	https://openreview.net/forum?id=BJena3VtwS	CV	7	null	null		null	unknown
ryg7vA4tPB	2,020	rejected	CV	Rigging the Lottery: Making All Tickets Winners	Sparse neural networks have been shown to yield computationally efficient networks with improved inference times. There is a large body of work on training dense networks to yield sparse networks for inference (Molchanov et al., 2017;Zhu & Gupta, 2018; Louizos et al., 2017; Li et al., 2016; Guo et al., 2016). This li...	5	1.414	3	https://drive.google.com/file/d/1XdexLVd2_PkgUu8mjkjKQpA2zvsaiqKe/view?usp=sharing	[ "sparse training", "sparsity", "pruning", "lottery tickets", "imagenet", "resnet", "mobilenet", "efficiency", "optimization", "local minima" ]	null	https://openreview.net/forum?id=ryg7vA4tPB	CV	2	null	null		null	unknown
SylR6n4tPS	2,020	rejected	CV	Learning to Generate Grounded Visual Captions without Localization Supervision	When automatically generating a sentence description for an image or video, it often remains unclear how well the generated caption is grounded, or if the model hallucinates based on priors in the dataset and/or the language model. The most common way of relating image regions with words in caption models is through an...	5	1.414	3	https://www.dropbox.com/s/569iz5opptn3s8a/cyclical-grounding-code.zip?dl=1	[ "image captioning", "video captioning", "self-supervised learning", "visual grounding" ]	null	https://openreview.net/forum?id=SylR6n4tPS	CV	11	null	null		null	unknown
S1lF8xHYwS	2,020	rejected	CV	Unsupervised Domain Adaptation through Self-Supervision	This paper addresses unsupervised domain adaptation, the setting where labeled training data is available on a source domain, but the goal is to have good performance on a target domain with only unlabeled data. Like much of previous work, we seek to align the learned representations of the source and target domains wh...	5	1.414	3	https://drive.google.com/open?id=1fVAUS_0VNnqK8Z3e_YyD-8fugB-CAe3W	[ "unsupervised domain adaptation" ]	null	https://openreview.net/forum?id=S1lF8xHYwS	CV	1	null	null		null	unknown
S1gmvyHFDS	2,020	rejected	CV	Provenance detection through learning transformation-resilient watermarking	Advancements in deep generative models have made it possible to synthesize images, videos and audio signals that are hard to distinguish from natural signals, creating opportunities for potential abuse of these capabilities. This motivates the problem of tracking the provenance of signals, i.e., being able to determine...	5	2.944	3	https://drive.google.com/open?id=1c-qqHfTr3uMQSIuTR_Z8qZv0uTVrkH5m	[ "watermarking", "provenance detection" ]	null	https://openreview.net/forum?id=S1gmvyHFDS	CV	2	null	null		null	unknown
HyxFF34FPr	2,020	rejected	CV	FoveaBox: Beyound Anchor-based Object Detection	We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are a...	5	1.414	3	https://drive.google.com/file/d/1Iwe3Vfbunv5NaLFFn0i_fsfNT-XP0Zmx/view?usp=sharing	[]	null	https://openreview.net/forum?id=HyxFF34FPr	CV	3	null	null		null	unknown
HyeqPJHYvH	2,020	rejected	CV	Stochastic Latent Residual Video Prediction	Video prediction is a challenging task: models have to account for the inherent uncertainty of the future. Most works in the literature are based on stochastic image-autoregressive recurrent networks, raising several performance and applicability issues. An alternative is to use fully latent temporal models which untie...	5	1.414	3	https://sites.google.com/view/srvp/	[ "stochastic video prediction", "variational autoencoder", "residual dynamics" ]	null	https://openreview.net/forum?id=HyeqPJHYvH	CV	6	null	null		null	unknown
HklvmlrKPB	2,020	rejected	CV	Improving Sequential Latent Variable Models with Autoregressive Flows	We propose an approach for sequence modeling based on autoregressive normalizing flows. Each autoregressive transform, acting across time, serves as a moving reference frame for modeling higher-level dynamics. This technique provides a simple, general-purpose method for improving sequence modeling, with connections to ...	5	1.414	3	https://anonymous.4open.science/r/f02199f7-86d2-45ee-ad23-3f13f769ee10/	[ "Autoregressive Flows", "Sequence Modeling", "Latent Variable Models", "Video Modeling", "Variational Inference" ]	null	https://openreview.net/forum?id=HklvmlrKPB	CV	2	null	null		null	unknown
H1lBj2VFPS	2,020	accepted_poster	CV	Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware	With the proliferation of specialized neural network processors that operate on low-precision integers, the performance of Deep Neural Network inference becomes increasingly dependent on the result of quantization. Despite plenty of prior work on the quantization of weights or activations for neural networks, there is ...	5	1.414	3	https://anonymous.4open.science/r/c05a5b6a-1d0c-4201-926f-e7b52034f7a5/	[ "quantization", "integer-arithmetic-only DNN accelerator", "acceleration" ]	null	https://openreview.net/forum?id=H1lBj2VFPS	CV	1	CV	fits40	Quantization-aware training of ResNet/MobileNet/YOLOv2 on single GPU	20	25M
H1eKT1SFvH	2,020	rejected	CV	Towards Effective 2-bit Quantization: Pareto-optimal Bit Allocation for Deep CNNs Compression	State-of-the-art quantization methods can compress deep neural networks down to 4 bits without losing accuracy. However, when it comes to 2 bits, the performance drop is still noticeable. One problem in these methods is that they assign equal bit rate to quantize weights and activations in all layers, which is not reas...	5	2.944	3	https://www.dropbox.com/sh/x4k4vf2kj7waix4/AADbfNvhr0Vl82YnfIc3OCJPa?dl=0	[]	null	https://openreview.net/forum?id=H1eKT1SFvH	CV	1	null	null		null	unknown
BygZARVFDH	2,020	rejected	CV	Compositional Visual Generation with Energy Based Models	Humans are able to both learn quickly and rapidly adapt their knowledge. One major component is the ability to incrementally combine many simple concepts to accelerates the learning process. We show that energy based models are a promising class of models towards exhibiting these properties by directly combining probab...	5	1.414	3	https://drive.google.com/file/d/138w7Oj8rQl_e40_RfZJq2WKWb41NgKn3	[ "Compositional Generation", "Energy Based Model", "Compositionality", "Generative Models" ]	null	https://openreview.net/forum?id=BygZARVFDH	CV	1	null	null		null	unknown
ByeSYa4KPS	2,020	rejected	CV	Sparse Networks from Scratch: Faster Training without Losing Performance	We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance levels. We accomplish this by developing sparse momentum, an algorithm which uses exponentially smoothed gradients (momentum) to...	5	1.414	3	https://www.dropbox.com/s/wes0wtt75iad4j4/sparse_learning.zip?dl=0	[ "sparse learning", "sparse networks", "sparsity", "efficient deep learning", "efficient training" ]	null	https://openreview.net/forum?id=ByeSYa4KPS	CV	2	null	null		null	unknown
BJx-ZeSKDB	2,020	rejected	CV	Compositional Embeddings: Joint Perception and Comparison of Class Label Sets	We explore the idea of compositional set embeddings that can be used to infer not just a single class, but the set of classes associated with the input data (e.g., image, video, audio signal). This can be useful, for example, in multi-object detection in images, or multi-speaker diarization (one-shot learning) in audio...	5	1.414	3	https://drive.google.com/open?id=1zjsK9DP3CUqwcVSNwDPshIxOV5hQwFxt	[ "Embedding", "One-shot Learning", "Compositional Representation" ]	null	https://openreview.net/forum?id=BJx-ZeSKDB	CV	3	null	null		null	unknown
BJl2_nVFPB	2,020	accepted_poster	CV	Automatically Discovering and Learning New Visual Categories with Ranking Statistics	We tackle the problem of discovering novel classes in an image collection given labelled examples of other classes. This setting is similar to semi-supervised learning, but significantly harder because there are no labelled examples for the new classes. The challenge, then, is to leverage the information contained in t...	5	1.414	3	http://www.robots.ox.ac.uk/~vgg/research/auto_novel/	[ "deep learning", "classification", "novel classes", "transfer learning", "clustering", "incremental learning" ]	null	https://openreview.net/forum?id=BJl2_nVFPB	CV	3	CV	fits40	ResNet-backbone self-supervised clustering on standard image benchmarks	18	25M
B1xIj3VYvr	2,020	accepted_poster	CV	Weakly Supervised Clustering by Exploiting Unique Class Count	A weakly supervised learning based clustering framework is proposed in this paper. As the core of this framework, we introduce a novel multiple instance learning task based on a bag level label called unique class count (ucc), which is the number of unique classes among all instances inside the bag. In this task, no an...	5	2.944	3	http://bit.ly/uniqueclasscount	[ "weakly supervised clustering", "weakly supervised learning", "multiple instance learning" ]	null	https://openreview.net/forum?id=B1xIj3VYvr	CV	1	CV	fits40	Small MIL/UNet classifier on modest datasets; single-GPU scale	15	30M
B1l1qnEFwH	2,020	rejected	CV	Deep Audio Prior	Deep convolutional neural networks are known to specialize in distilling compact and robust prior from a large amount of data. We are interested in applying deep networks in the absence of training dataset. In this paper, we introduce deep audio prior (DAP) which leverages the structure of a network and the temporal ...	5	1.414	3	https://iclr-dap.github.io/Deep-Audio-Prior/	[ "deep audio prior", "blind sound separation", "deep learning", "audio representation" ]	null	https://openreview.net/forum?id=B1l1qnEFwH	CV	1	null	null		null	unknown
B1l0wp4tvr	2,020	rejected	CV	Information Plane Analysis of Deep Neural Networks via Matrix--Based Renyi's Entropy and Tensor Kernels	Analyzing deep neural networks (DNNs) via information plane (IP) theory has gained tremendous attention recently as a tool to gain insight into, among others, their generalization ability. However, it is by no means obvious how to estimate mutual information (MI) between each hidden layer and the input/desired output, ...	5	1.414	3	https://anonymous.4open.science/r/d1ad771a-aa55-4585-b056-2b07b098f51e/	[ "information plane", "information theory", "deep neural networks", "entropy", "mutual information", "tensor kernels" ]	null	https://openreview.net/forum?id=B1l0wp4tvr	CV	3	null	null		null	unknown
B1eY_pVYvB	2,020	accepted_poster	CV	Efficient and Information-Preserving Future Frame Prediction and Beyond	Applying resolution-preserving blocks is a common practice to maximize information preservation in video prediction, yet their high memory consumption greatly limits their application scenarios. We propose CrevNet, a Conditionally Reversible Network that uses reversible architectures to build a bijective two-way autoen...	5	1.414	3	https://drive.google.com/file/d/1koVpH2RhkOl4_Xm_q8Iy1FuX3zQxC9gd/view?usp=sharing	[ "self-supervised learning", "generative pre-training", "video prediction", "reversible architecture" ]	null	https://openreview.net/forum?id=B1eY_pVYvB	CV	3	CV	fits40	Reversible network explicitly designed for low memory video prediction	12	20M
BJevJCVYvB	2,020	rejected	CV	Training Neural Networks for and by Interpolation	In modern supervised learning, many deep neural networks are able to interpolate the data: the empirical loss can be driven to near zero on all samples simultaneously. In this work, we explicitly exploit this interpolation property for the design of a new optimization algorithm for deep learning. Specifically, we use i...	4.75	2.165	4	https://anonymous.4open.science/repository/14f2b37d-2bef-4b3f-b47c-dd257ce75543	[ "optimization", "adaptive learning-rate", "Polyak step-size", "Newton-Raphson" ]	null	https://openreview.net/forum?id=BJevJCVYvB	CV	1	null	null		null	unknown
rJx7wlSYvB	2,020	rejected	CV	Differentiable Bayesian Neural Network Inference for Data Streams	While deep neural networks (NNs) do not provide the confidence of its prediction, Bayesian neural network (BNN) can estimate the uncertainty of the prediction. However, BNNs have not been widely used in practice due to the computational cost of predictive inference. This prohibitive computational cost is a hindrance e...	4.667	2.357	3	https://anonymous.4open.science/r/dbnn/	[ "Bayesian neural network", "approximate predictive inference", "data stream", "histogram" ]	null	https://openreview.net/forum?id=rJx7wlSYvB	CV	1	null	null		null	unknown
rJel41BtDH	2,020	rejected	CV	Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning	Semi-supervised learning, i.e. jointly learning from labeled an unlabeled samples, is an active research topic due to its key role on relaxing human annotation constraints. In the context of image classification, recent advances to learn from unlabeled samples are mainly focused on consistency regularization methods th...	4.667	2.357	3	https://drive.google.com/file/d/1qaVu1RU4pvXewwxNvTXDvQrahFnLTPl3/view?usp=sharing	[ "Semi-supervised learning", "pseudo-labeling", "deep semi-supervised learning", "confirmation bias", "image classification" ]	null	https://openreview.net/forum?id=rJel41BtDH	CV	4	null	null		null	unknown
HJx7uJStPH	2,020	rejected	CV	Music Source Separation in the Waveform Domain	Source separation for music is the task of isolating contributions, or stems, from different instruments recorded individually and arranged together to form a song.Such components include voice, bass, drums and any other accompaniments. While end-to-end models that directly generate the waveform are state-of-the-art in...	4.667	2.357	3	https://www.dropbox.com/sh/o0gps94s120v7l4/AABS5vDfuuRjgY_zDjdSm_Fsa?dl=1	[ "source separation", "audio synthesis", "deep learning" ]	null	https://openreview.net/forum?id=HJx7uJStPH	CV	1	null	null		null	unknown
HJeIX6EKvr	2,020	rejected	CV	Leveraging inductive bias of neural networks for learning without explicit human annotations	Classification problems today are typically solved by first collecting examples along with candidate labels, second obtaining clean labels from workers, and third training a large, overparameterized deep neural network on the clean examples. The second, labeling step is often the most expensive one as it requires manu...	4.5	1.5	2	https://www.dropbox.com/sh/z3kt1rpk61idg0m/AAA--xI-5QWnArath3aM-ztha?dl=0	[ "dataset construction", "deep learning", "candidate examples" ]	null	https://openreview.net/forum?id=HJeIX6EKvr	CV	1	null	null		null	unknown
BJlgt2EYwr	2,020	rejected	CV	Stabilizing DARTS with Amended Gradient Estimation on Architectural Parameters	Differentiable neural architecture search has been a popular methodology of exploring architectures for deep learning. Despite the great advantage of search efficiency, it often suffers weak stability, which obstacles it from being applied to a large search space or being flexibly adjusted to different scenarios. This ...	4.5	1.5	4	https://www.dropbox.com/sh/j4rfzi6586iw3me/AAB1bnUMid-5DLzaEGxmQAkCa?dl=0	[ "Neural Architecture Search", "DARTS", "Stability" ]	null	https://openreview.net/forum?id=BJlgt2EYwr	CV	2	null	null		null	unknown
rylqmxBKvH	2,020	rejected	CV	Unsupervised Spatiotemporal Data Inpainting	We tackle the problem of inpainting occluded area in spatiotemporal sequences, such as cloud occluded satellite observations, in an unsupervised manner. We place ourselves in the setting where there is neither access to paired nor unpaired training data. We consider several cases in which the underlying information of ...	4	1.414	3	https://sites.google.com/view/unsup-video-inpaiting/	[ "Deep Learning", "Adversarial", "MAP", "GAN", "neural networks", "video" ]	null	https://openreview.net/forum?id=rylqmxBKvH	CV	2	null	null		null	unknown
rygPm64tDH	2,020	rejected	CV	Learning Explainable Models Using Attribution Priors	Two important topics in deep learning both involve incorporating humans into the modeling process: Model priors transfer information from humans to a model by regularizing the model's parameters; Model attributions transfer information from a model to humans by explaining the model's behavior. Previous work has taken i...	4	2.944	3	https://www.dropbox.com/sh/xvt3vqv8xjb5nwh/AACgt-0OxiefImjVXX5UJSuua?dl=0	[ "Deep Learning", "Interpretability", "Attributions", "Explanations", "Biology", "Health", "Computational Biology" ]	null	https://openreview.net/forum?id=rygPm64tDH	CV	1	null	null		null	unknown
rJgE9CEYPS	2,020	rejected	CV	Discriminability Distillation in Group Representation Learning	Learning group representation is a commonly concerned issue in tasks where the basic unit is a group, set or sequence. The computer vision community tries to tackle it by aggregating the elements in a group based on an indicator either defined by human such as the quality or saliency of an element, or generated by a bl...	4	2.121	4	https://www.dropbox.com/sh/j4gx4d8qebawl1i/AAAuKPircw50mbKHE03svpBda?dl=0	[]	null	https://openreview.net/forum?id=rJgE9CEYPS	CV	2	null	null		null	unknown
rJeBJJBYDB	2,020	rejected	CV	Chart Auto-Encoders for Manifold Structured Data	Auto-encoding and generative models have made tremendous successes in image and signal representation learning and generation. These models, however, generally employ the full Euclidean space or a bounded subset (such as $[0,1]^l$) as the latent space, whose trivial geometry is often too simplistic to meaningfully ref...	4	1.414	3	https://anonymous.4open.science/r/a40668ab-7542-4028-8709-694142a985da/	[ "Auto-encoder", "differential manifolds", "multi-charted latent space" ]	null	https://openreview.net/forum?id=rJeBJJBYDB	CV	1	null	null		null	unknown
r1xF7lSYDS	2,020	rejected	CV	Transferable Recognition-Aware Image Processing	Recent progress in image recognition has stimulated the deployment of vision systems (e.g. image search engines) at an unprecedented scale. As a result, visual data are now often consumed not only by humans but also by machines. Meanwhile, existing image processing methods only optimize for better human perception, whe...	4	2.944	3	https://drive.google.com/open?id=1aMJwg7UrJ9f1aQHYBC10ZZWcZDwFYmex	[ "Image Recognition", "Image Processing" ]	null	https://openreview.net/forum?id=r1xF7lSYDS	CV	11	null	null		null	unknown
Syx7WyBtwB	2,020	rejected	CV	Interpretations are useful: penalizing explanations to align neural networks with prior knowledge	For an explanation of a deep learning model to be effective, it must provide both insight into a model and suggest a corresponding action in order to achieve some objective. Too often, the litany of proposed explainable deep learning methods stop at the first step, providing practitioners with insight into a...	4	1.414	3	https://drive.google.com/drive/folders/16XHi-Onen2gjOvRx3qIUP1Z-3SvrAY4P?usp=sharing	[ "explainability", "deep learning", "interpretability", "computer vision" ]	null	https://openreview.net/forum?id=Syx7WyBtwB	CV	1	null	null		null	unknown
SJeUm1HtDH	2,020	rejected	CV	Swoosh! Rattle! Thump! - Actions that Sound	Truly intelligent agents need to capture the interplay of all their senses to build a rich physical understanding of their world. In robotics, we have seen tremendous progress in using visual and tactile perception; however we have often ignored a key sense: sound. This is primarily due to lack of data that captures th...	4	1.414	3	https://sites.google.com/view/iclr2020-sound-action	[ "Sound", "Action", "Audio Representations" ]	null	https://openreview.net/forum?id=SJeUm1HtDH	CV	3	null	null		null	unknown
HyxgBerKwB	2,020	rejected	CV	GraphQA: Protein Model Quality Assessment using Graph Convolutional Network	Proteins are ubiquitous molecules whose function in biological processes is determined by their 3D structure. Experimental identification of a protein's structure can be time-consuming, prohibitively expensive, and not always possible. Alternatively, protein folding can be modeled using computational methods, which ho...	4	1.414	3	https://anonymous.4open.science/r/94d976ce-9166-4379-b3c4-3c982752a931/	[ "Protein Quality Assessment", "Graph Networks", "Representation Learning" ]	null	https://openreview.net/forum?id=HyxgBerKwB	CV	1	null	null		null	unknown
HJxRMlrtPH	2,020	rejected	CV	Verification of Generative-Model-Based Visual Transformations	Generative networks are promising models for specifying visual transformations. Unfortunately, certification of generative models is challenging as one needs to capture sufficient non-convexity so to produce precise bounds on the output. Existing verification methods either fail to scale to generative networks or do no...	4	1.414	3	https://www.dropbox.com/s/np89rh2q8hzr1pj/approxline_submit.tar.gz?dl=0	[ "robustness certification", "formal verification", "robustness analysis", "latent space interpolations" ]	null	https://openreview.net/forum?id=HJxRMlrtPH	CV	2	null	null		null	unknown
HJxJ2h4tPr	2,020	rejected	CV	HighRes-net: Multi-Frame Super-Resolution by Recursive Fusion	Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views....	4	2.944	3	https://anonymous.4open.science/r/b3404d0d-e541-4f52-bbe9-f84f2a52972e/	[ "multi-frame super-resolution", "super-resolution", "remote sensing", "fusion", "de-aliasing", "deep learning", "registration" ]	null	https://openreview.net/forum?id=HJxJ2h4tPr	CV	5	null	null		null	unknown
HJeFmkBtvB	2,020	rejected	CV	Annealed Denoising score matching: learning Energy based model in high-dimensional spaces	Energy based models outputs unmormalized log-probability values given datasamples. Such a estimation is essential in a variety of application problems suchas sample generation, denoising, sample restoration, outlier detection, Bayesianreasoning, and many more. However, standard maximum likelihood training isc...	4	1.414	3	https://anonymous.4open.science/r/b85d6f62-99f6-458b-a101-3d35583ac11d/	[ "Energy based models", "score matching", "annealing", "likelihood", "generative model", "unsupervised learning" ]	null	https://openreview.net/forum?id=HJeFmkBtvB	CV	1	null	null		null	unknown
BygSZAVKvr	2,020	rejected	CV	Energy-Aware Neural Architecture Optimization with Fast Splitting Steepest Descent	Designing energy-efficient networks is of critical importance for enabling state-of-the-art deep learning in mobile and edge settings where the computation and energy budgets are highly limited. Recently, Wu et al. (2019) framed the search of efficient neural architectures into a continuous splitting process: it iterat...	4	1.414	3	https://drive.google.com/drive/u/1/folders/1RpnXTQUaiia6skdsmhBGqIX-Ex9CjJ3Y	[ "Neural architecture optimization", "splitting steepest descent" ]	null	https://openreview.net/forum?id=BygSZAVKvr	CV	1	null	null		null	unknown
Byg9AR4YDB	2,020	rejected	CV	Exploring Cellular Protein Localization Through Semantic Image Synthesis	Cell-cell interactions have an integral role in tumorigenesis as they are critical in governing immune responses. As such, investigating specific cell-cell interactions has the potential to not only expand upon the understanding of tumorigenesis, but also guide clinical management of patient responses to cancer immunot...	4	1.414	3	https://drive.google.com/file/d/1AEr6eSOum79JZRht6aFkd12EXynCGFOT/view?usp=sharing	[ "Computational biology", "image synthesis", "GANs", "exploring multiplex images", "attention", "interpretability" ]	null	https://openreview.net/forum?id=Byg9AR4YDB	CV	6	null	null		null	unknown
BJe7h34YDS	2,020	rejected	CV	Understanding and Stabilizing GANs' Training Dynamics with Control Theory	Generative adversarial networks~(GANs) have made significant progress on realistic image generation but often suffer from instability during the training process. Most previous analyses mainly focus on the equilibrium that GANs achieve, whereas a gap exists between such theoretical analyses and practical implementation...	4	1.414	3	https://anonymous.4open.science/r/a5e02628-d2d6-43b4-b645-5752fb87637a/	[ "Generative Adversarial Nets", "Stability Analysis", "Control Theory" ]	null	https://openreview.net/forum?id=BJe7h34YDS	CV	2	null	null		null	unknown
B1lLw6EYwB	2,020	accepted_poster	CV	Gap-Aware Mitigation of Gradient Staleness	Cloud computing is becoming increasingly popular as a platform for distributed training of deep neural networks. Synchronous stochastic gradient descent (SSGD) suffers from substantial slowdowns due to stragglers if the environment is non-dedicated, as is common in cloud computing. Asynchronous SGD (ASGD) methods are i...	4	1.414	3	https://drive.google.com/drive/folders/1z1e_GI-6FZyfROIftoLHqz1X7xvNczWs?usp=sharing	[ "distributed", "asynchronous", "large scale", "gradient staleness", "staleness penalization", "sgd", "deep learning", "neural networks", "optimization" ]	null	https://openreview.net/forum?id=B1lLw6EYwB	CV	2	Other	toobig	Distributed async training scaling to many workers, ImageNet	null	unknown
SkeXL0NKwH	2,020	rejected	CV	Low Rank Training of Deep Neural Networks for Emerging Memory Technology	The recent success of neural networks for solving difficult decision tasks has incentivized incorporating smart decision making "at the edge." However, this work has traditionally focused on neural network inference, rather than training, due to memory and compute limitations, especially in emerging non-volatile memory...	3.75	1.299	4	https://anonymous.4open.science/r/77ebbbb0-45c7-4944-a594-3dd742b7ca07/	[ "low rank training", "kronecker sum", "emerging memory", "non-volatile memory", "rram", "reram", "federated learning" ]	null	https://openreview.net/forum?id=SkeXL0NKwH	CV	1	null	null		null	unknown
BklSwn4tDH	2,020	rejected	CV	Prestopping: How Does Early Stopping Help Generalization Against Label Noise?	Noisy labels are very common in real-world training data, which lead to poor generalization on test data because of overfitting to the noisy labels. In this paper, we claim that such overfitting can be avoided by "early stopping" training a deep neural network before the noisy labels are severely memorized. Then, we re...	3.75	1.299	4	https://bit.ly/2l3g9Jx	[ "noisy label", "label noise", "robustness", "deep learning", "early stopping" ]	null	https://openreview.net/forum?id=BklSwn4tDH	CV	1	null	null		null	unknown
ryeQmCVYPS	2,020	rejected	CV	Defective Convolutional Layers Learn Robust CNNs	Robustness of convolutional neural networks has recently been highlighted by the adversarial examples, i.e., inputs added with well-designed perturbations which are imperceptible to humans but can cause the network to give incorrect outputs. Recent research suggests that the noises in adversarial examples break the tex...	3.333	2.055	3	https://drive.google.com/open?id=1ovRyQhW4jKKbxH8QeCOCOCcaaXA4eb8_	[ "adversarial examples", "robust machine learning", "cnn structure", "deep feature representations" ]	null	https://openreview.net/forum?id=ryeQmCVYPS	CV	10	null	null		null	unknown
rkerLaVtDr	2,020	rejected	CV	A General Upper Bound for Unsupervised Domain Adaptation	In this work, we present a novel upper bound of target error to address the problem for unsupervised domain adaptation. Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks. Furthermore, Ben-David et al. (2010) provide an upper bound for target error whe...	3.333	2.055	3	https://drive.google.com/open?id=1XGOFQAjsCg9gTGSfDuUs-cd2dt9cKGak	[ "unsupervised domain adaptation", "upper bound", "joint error", "hypothesis space constraint", "cross margin discrepancy" ]	null	https://openreview.net/forum?id=rkerLaVtDr	CV	1	null	null		null	unknown
rJl05AVtwB	2,020	rejected	CV	Chordal-GCN: Exploiting sparsity in training large-scale graph convolutional networks	Despite the impressive success of graph convolutional networks (GCNs) on numerous applications, training on large-scale sparse networks remains challenging. Current algorithms require large memory space for storing GCN outputs as well as all the intermediate embeddings. Besides, most of these algorithms involves either...	3.333	2.055	3	https://www.dropbox.com/s/0vby5gbu9qkbigr/chordal-gcn.zip?dl=0	[ "graph convolutional network", "semi-supervised learning" ]	null	https://openreview.net/forum?id=rJl05AVtwB	CV	3	null	null		null	unknown
SyxD7lrFPH	2,020	rejected	CV	Frequency Pooling: Shift-Equivalent and Anti-Aliasing Down Sampling	Convolutional layer utilizes the shift-equivalent prior of images which makes it a great success for image processing. However, commonly used down sampling methods in convolutional neural networks (CNNs), such as max-pooling, average-pooling, and strided-convolution, are not shift-equivalent. This destroys the shift-eq...	3.333	2.055	3	https://anonymous.4open.science/r/87040761-4ef7-4a02-99d5-b82ec65e1a11/	[ "pooling", "anti-aliasing", "shift-equivalent", "frequency" ]	null	https://openreview.net/forum?id=SyxD7lrFPH	CV	4	null	null		null	unknown
S1efAp4YvB	2,020	rejected	CV	Interpreting video features: a comparison of 3D convolutional networks and convolutional LSTM networks	A number of techniques for interpretability have been presented for deep learning in computer vision, typically with the goal of understanding what it is that the networks have actually learned underneath a given classification decision. However, when it comes to deep video architectures, interpretability is still in i...	3.333	2.055	3	https://www.dropbox.com/sh/48kva9cw7keobi5/AABdrhuwzSwohA-Jq-TIsG80a?dl=0	[ "interpretability", "spatiotemporal", "video", "features", "saliency", "temporal" ]	null	https://openreview.net/forum?id=S1efAp4YvB	CV	9	null	null		null	unknown
SJgvl6EFwH	2,020	rejected	CV	InfoCNF: Efficient Conditional Continuous Normalizing Flow Using Adaptive Solvers	Continuous Normalizing Flows (CNFs) have emerged as promising deep generative models for a wide range of tasks thanks to their invertibility and exact likelihood estimation. However, conditioning CNFs on signals of interest for conditional image generation and downstream predictive tasks is inefficient due to the high-...	3.25	1.785	4	https://sites.google.com/view/infocnf-iclr/	[ "continuous normalizing flows", "conditioning", "adaptive solvers", "gating networks" ]	null	https://openreview.net/forum?id=SJgvl6EFwH	CV	1	null	null		null	unknown
rJlwAa4YwS	2,020	rejected	CV	Lattice Representation Learning	We introduce the notion of \emph{lattice representation learning}, in which the representation for some object of interest (e.g. a sentence or an image) is a lattice point in an Euclidean space. Our main contribution is a result for replacing an objective function which employs lattice quantization with an objective fu...	3	0	3	https://www.dropbox.com/s/y6pvq34xh7tkory/ICLR_SUBMISSION.zip?dl=0	[ "lattices", "representation learning", "coding theory", "lossy source coding", "information theory" ]	null	https://openreview.net/forum?id=rJlwAa4YwS	CV	1	Other	fits40	Lattice representation learning on seq2seq encoder/decoder; small scale	null	unknown
rJgCOySYwH	2,020	rejected	CV	Function Feature Learning of Neural Networks	We present a Function Feature Learning (FFL) method that can measure the similarity of non-convex neural networks. The function feature representation provides crucial insights into the understanding of the relations between different local solutions of identical neural networks. Unlike existing methods that use neuron...	3	0	2	https://anonymous.4open.science/r/74e46ebe-4023-4a85-86b6-19ee20c5070a/	[]	null	https://openreview.net/forum?id=rJgCOySYwH	CV	4	Other	light	Neural network similarity analysis on small MLP/CNN/RNN	null	unknown
SkxHRySFvr	2,020	rejected	CV	LEARNING TO IMPUTE: A GENERAL FRAMEWORK FOR SEMI-SUPERVISED LEARNING	Recent semi-supervised learning methods have shown to achieve comparable results to their supervised counterparts while using only a small portion of labels in image classification tasks thanks to their regularization strategies. In this paper, we take a more direct approach for semi-supervised learning and propose lea...	3	0	3	https://anonymous.4open.science/r/a4721095-8266-4038-9cc6-8791ef61c610/	[ "Semi-supervised Learning", "Meta-Learning", "Learning to label" ]	null	https://openreview.net/forum?id=SkxHRySFvr	CV	2	CV	fits40	CIFAR-scale semi-supervised with WideResNet meta-learning fits single GPU	12	36M
Skg3104FDS	2,020	rejected	CV	First-Order Preconditioning via Hypergradient Descent	Standard gradient-descent methods are susceptible to a range of issues that can impede training, such as high correlations and different scaling in parameter space. These difficulties can be addressed by second-order approaches that apply a preconditioning matrix to the gradient to improve convergence. Unfortunately, ...	3	0	3	https://drive.google.com/file/d/1vhB4fxDuxaYJcNP6ioEJQf4CLHxhy1ka/view?usp=sharing	[ "optimization", "deep learning", "hypgergradient" ]	null	https://openreview.net/forum?id=Skg3104FDS	CV	1	Other	light	First-order preconditioning optimizer on visual classification; small	null	unknown
SJlVn6NKPB	2,020	rejected	CV	Representation Learning for Remote Sensing: An Unsupervised Sensor Fusion Approach	In the application of machine learning to remote sensing, labeled data is often scarce or expensive, which impedes the training of powerful models like deep convolutional neural networks. Although unlabeled data is abundant, recent self-supervised learning approaches are ill-suited to the remote sensing domain. In addi...	3	0	3	https://storage.cloud.google.com/public-published-datasets/csf_code.zip	[ "unsupervised learning", "representation learning", "deep learning", "remote sensing", "sensor fusion" ]	null	https://openreview.net/forum?id=SJlVn6NKPB	CV	3	CV	toobig	Self-supervised pretraining on 47M remote-sensing image triplets	null	unknown
SJlOq34Kwr	2,020	rejected	CV	Unsupervised Intuitive Physics from Past Experiences	We consider the problem of learning models of intuitive physics from raw, unlabelled visual input. Differently from prior work, in addition to learning general physical principles, we are also interested in learning ``on the fly'' physical properties specific to new environments, based on a small number of environment-...	3	0	3	https://drive.google.com/file/d/1Lr4sB_WOlSQ5qAfBzkfqVBrYDXe9KgxE/view?usp=sharing	[ "Intuitive physics", "Deep learning" ]	null	https://openreview.net/forum?id=SJlOq34Kwr	CV	1	CV	fits40	Video prediction CNN, unsupervised, moderate scale fits comfortably	12	50M
S1xipR4FPB	2,020	rejected	CV	Teacher-Student Compression with Generative Adversarial Networks	More accurate machine learning models often demand more computation and memory at test time, making them difficult to deploy on CPU- or memory-constrained devices. Teacher-student compression (TSC), also known as distillation, alleviates this burden by training a less expensive student model to mimic the expensive teac...	3	0	3	https://drive.google.com/drive/folders/1IovL_rAVKVnpG2enqAgfPMilOUH-n8tu?usp=sharing	[]	null	https://openreview.net/forum?id=S1xipR4FPB	CV	2	CV	fits40	GAN plus distillation on tabular/CIFAR-scale images, small footprint	10	30M
S1x0CnEtvB	2,020	rejected	CV	AutoGrow: Automatic Layer Growing in Deep Convolutional Networks	Depth is a key component of Deep Neural Networks (DNNs), however, designing depth is heuristic and requires many human efforts. We propose AutoGrow to automate depth discovery in DNNs: starting from a shallow seed architecture, AutoGrow grows new layers if the growth improves the accuracy; otherwise, stops growing and ...	3	0	3	https://drive.google.com/file/d/1C_kdg7Ffb4EE1WKpRO7W2t9f83WueuvY/view?usp=sharing	[ "Growing", "depth", "neural networks", "automation" ]	null	https://openreview.net/forum?id=S1x0CnEtvB	CV	2	CV	fits40	ResNet ImageNet training fits single 40GB GPU memory-wise	20	25M
S1gyl6Vtvr	2,020	rejected	CV	MaskConvNet: Training Efficient ConvNets from Scratch via Budget-constrained Filter Pruning	In this paper, we propose a framework, called MaskConvNet, for ConvNets filter pruning. MaskConvNet provides elegant support for training budget-aware pruned networks from scratch, by adding a simple mask module to a ConvNet architecture. MaskConvNet enjoys several advantages - (1) Flexible, the mask module can be inte...	3	0	3	https://www.dropbox.com/s/c4zi3n7h1bexl12/maskconv-iclr-code.zip?dl=0	[ "Structured Pruning", "Sparsity Regularization", "Budget-Aware" ]	null	https://openreview.net/forum?id=S1gyl6Vtvr	CV	2	CV	fits40	ConvNet pruning on CIFAR/ImageNet fits single GPU comfortably	20	25M
Hke0lRNYwS	2,020	rejected	CV	Convolutional Bipartite Attractor Networks	In human perception and cognition, a fundamental operation that brains perform is interpretation: constructing coherent neural states from noisy, incomplete, and intrinsically ambiguous evidence. The problem of interpretation is well matched to an early and often overlooked architecture, the attractor network---a recur...	3	0	3	https://drive.google.com/drive/folders/1CYmmxBQhW9v47rJ2em2MrUZo3JWvmJ8F?usp=sharing	[ "attractor network", "recurrent network", "energy function", "convolutional network", "image completion", "super-resolution" ]	null	https://openreview.net/forum?id=Hke0lRNYwS	CV	7	CV	fits40	Convolutional recurrent attractor net for image completion, moderate size	14	20M
BylT8RNKPH	2,020	rejected	CV	A Base Model Selection Methodology for Efficient Fine-Tuning	While the accuracy of image classification achieves significant improvement with deep Convolutional Neural Networks (CNN), training a deep CNN is a time-consuming task because it requires a large amount of labeled data and takes a long time to converge even with high performance computing resources. Fine-tuning, one of...	3	0	3	https://www.dropbox.com/s/ry1gh8bf4yy4qe7/iclr2020_code_submission.tar.gz?dl=0	[ "transfer learning", "fine-tuning", "parameter transfer" ]	null	https://openreview.net/forum?id=BylT8RNKPH	CV	8	CV	fits40	CNN finetuning transferability metrics, small classification models	10	25M
BkxzsT4Yvr	2,020	rejected	CV	Deep Gradient Boosting -- Layer-wise Input Normalization of Neural Networks	Stochastic gradient descent (SGD) has been the dominant optimization method for training deep neural networks due to its many desirable properties. One of the more remarkable and least understood quality of SGD is that it generalizes relatively well on unseen data even when the neural network has millions of parameters...	3	0	3	https://gofile.io/?c=S3giCL	[ "sgd", "dgb", "boosting", "batch norm", "input norm" ]	null	https://openreview.net/forum?id=BkxzsT4Yvr	CV	2	CV	fits40	ResNet ImageNet training with normalization layers fits single GPU	20	25M
BkxX30EFPS	2,020	rejected	CV	Perceptual Generative Autoencoders	Modern generative models are usually designed to match target distributions directly in the data space, where the intrinsic dimensionality of data can be much lower than the ambient dimensionality. We argue that this discrepancy may contribute to the difficulties in training generative models. We therefore propose to m...	3	0	3	https://bit.ly/2U0kRYL	[]	null	https://openreview.net/forum?id=BkxX30EFPS	CV	1	CV	fits40	VAE/autoencoder generative on CIFAR-10 and CelebA, small footprint	10	20M
BkxDxJHFDr	2,020	rejected	CV	Power up! Robust Graph Convolutional Network based on Graph Powering	Graph convolutional networks (GCNs) are powerful tools for graph-structured data. However, they have been recently shown to be vulnerable to topological attacks. To enhance adversarial robustness, we go beyond spectral graph theory to robust graph theory. By challenging the classical graph Laplacian, we propose a new c...	3	0	3	https://www.dropbox.com/sh/p36pzx1ock2iamo/AABEr7FtM5nqwC4i9nICLIsta?dl=0	[ "graph mining", "graph neural network", "adversarial robustness" ]	null	https://openreview.net/forum?id=BkxDxJHFDr	CV	2	Graph	light	Robust GCN on small graph benchmarks, adversarial robustness	null	unknown
B1eX_a4twH	2,020	rejected	CV	Superseding Model Scaling by Penalizing Dead Units and Points with Separation Constraints	In this article, we study a proposal that enables to train extremely thin (4 or 8 neurons per layer) and relatively deep (more than 100 layers) feedforward networks without resorting to any architectural modification such as Residual or Dense connections, data normalization or model scaling. We accomplish that by allev...	3	0	3	https://www.dropbox.com/s/kl96825sae12zkc/sep_cons.zip?dl=0	[ "Dead Point", "Dead Unit", "Model Scaling", "Separation Constraints", "Dying ReLU", "Constant Width", "Deep Neural Networks", "Backpropagation" ]	null	https://openreview.net/forum?id=B1eX_a4twH	CV	1	CV	light	Thin networks on toy, MNIST, CIFAR-10; small scale	null	unknown
rkgQL6VFwr	2,020	rejected	CV	Learning Generative Image Object Manipulations from Language Instructions	The use of adequate feature representations is essential for achieving high performance in high-level human cognitive tasks in computational modeling. Recent developments in deep convolutional and recurrent neural networks architectures enable learning powerful feature representations from both images and natural langu...	2.333	0.943	3	https://www.dropbox.com/s/fkaapcpbk06t8zi/pytorch.zip?dl=0	[]	null	https://openreview.net/forum?id=rkgQL6VFwr	CV	4	null	null		null	unknown
r1e8qpVKPS	2,020	rejected	CV	Role of two learning rates in convergence of model-agnostic meta-learning	Model-agnostic meta-learning (MAML) is known as a powerful meta-learning method. However, MAML is notorious for being hard to train because of the existence of two learning rates. Therefore, in this paper, we derive the conditions that inner learning rate $\alpha$ and meta-learning rate $\beta$ must satisfy for MAML to...	2.333	0.943	3	https://drive.google.com/file/d/1Seej9xI03F7_2wh4deDTBk_4aFyb2otb/view?usp=sharing	[ "meta-learning", "convergence" ]	null	https://openreview.net/forum?id=r1e8qpVKPS	CV	1	null	null		null	unknown
SkxV7kHKvr	2,020	rejected	CV	TWIN GRAPH CONVOLUTIONAL NETWORKS: GCN WITH DUAL GRAPH SUPPORT FOR SEMI-SUPERVISED LEARNING	Graph Neural Networks as a combination of Graph Signal Processing and Deep Convolutional Networks shows great power in pattern recognition in non-Euclidean domains. In this paper, we propose a new method to deploy two pipelines based on the duality of a graph to improve accuracy. By exploring the primal graph and its d...	2.333	0.943	3	https://drive.google.com/file/d/1lWt4Mvpq_czCIC8isdgVZ0qENBLDqQcX/view?usp=sharing	[ "Graph", "Neural Networks", "Deep Learning", "semi-supervised learning" ]	null	https://openreview.net/forum?id=SkxV7kHKvr	CV	2	null	null		null	unknown
SkeKtyHYPS	2,020	rejected	CV	Data Augmentation in Training CNNs: Injecting Noise to Images	Noise injection is a fundamental tool for data augmentation, and yet there is no widely accepted procedure to incorporate it with learning frameworks. This study analyzes the effects of adding or applying different noise models of varying magnitudes to Convolutional Neural Network (CNN) architectures. Noise models that...	2.333	0.943	3	https://drive.google.com/open?id=1GwQFo2QtW_O6AebR-ZGUYqlv6ItVx2_2	[ "deep learning", "data augmentation", "convolutional neural networks", "noise", "image processing", "SSIM" ]	null	https://openreview.net/forum?id=SkeKtyHYPS	CV	5	null	null		null	unknown
HklliySFDS	2,020	rejected	CV	Continual Learning with Gated Incremental Memories for Sequential Data Processing	The ability to learn over changing task distributions without forgetting previous knowledge, also known as continual learning, is a key enabler for scalable and trustworthy deployments of adaptive solutions. While the importance of continual learning is largely acknowledged in machine vision and reinforcement learning ...	2.333	0.943	3	https://drive.google.com/open?id=1L2_y35Zy5xahmxQRqGDCGonPiHeyuJlp	[ "continual learning", "recurrent neural networks", "progressive networks", "gating autoencoders", "sequential data processing" ]	null	https://openreview.net/forum?id=HklliySFDS	CV	1	null	null		null	unknown
H1xTup4KPr	2,020	rejected	CV	Needles in Haystacks: On Classifying Tiny Objects in Large Images	In some important computer vision domains, such as medical or hyperspectral imaging, we care about the classification of tiny objects in large images. However, most Convolutional Neural Networks (CNNs) for image classification were developed using biased datasets that contain large objects, in mostly central image posi...	2.333	0.943	3	https://anonymousfiles.io/k3Ax0cz6/	[ "computer vision", "CNNs", "small objects", "low signal-to-noise image classification" ]	null	https://openreview.net/forum?id=H1xTup4KPr	CV	9	null	null		null	unknown
H1eqOnNYDH	2,020	rejected	CV	Data augmentation instead of explicit regularization	Modern deep artificial neural networks have achieved impressive results through models with orders of magnitude more parameters than training examples which control overfitting with the help of regularization. Regularization can be implicit, as is the case of stochastic gradient descent and parameter sharing in convolu...	2.333	0.943	3	https://www.dropbox.com/sh/ki0syyl0bvp29rl/AABmmbqC-Ft86rzJ5WyUTo4da?dl=0	[ "data augmentation", "implicit regularization", "explicit regularization", "object recognition", "convolutional neural networks" ]	null	https://openreview.net/forum?id=H1eqOnNYDH	CV	2	null	null		null	unknown
Bkel1krKPS	2,020	rejected	CV	Attention on Abstract Visual Reasoning	Attention mechanisms have been boosting the performance of deep learning models on a wide range of applications, ranging from speech understanding to program induction. However, despite experiments from psychology which suggest that attention plays an essential role in visual reasoning, the full potential of attention...	2.333	0.943	3	https://drive.google.com/file/d/19fNqoqULy1rPOf38YQ2OsOFkDlzhec-i/view?usp=sharing	[ "Transformer Networks", "Self-Attention", "Wild Relation Networks", "Procedurally Generated Matrices" ]	null	https://openreview.net/forum?id=Bkel1krKPS	CV	5	null	null		null	unknown
SJeW-A4tDS	2,020	rejected	CV	Detecting malicious PDF using CNN	Malicious PDF files represent one of the biggest threats to computer security. To detect them, significant research has been done using handwritten signatures or machine learning based on manual feature extraction. Those approaches are both time-consuming, requires significant prior knowledge and the list of features h...	1.667	0.943	3	https://anonymous.4open.science/r/e553967d-5dfc-4a99-8624-f47c6b5fac5e/	[ "Cybersecurity", "Convolutional Neural Network", "Malware" ]	null	https://openreview.net/forum?id=SJeW-A4tDS	CV	5	null	null		null	unknown
HJxhWa4KDr	2,020	rejected	CV	MMD GAN with Random-Forest Kernels	In this paper, we propose a novel kind of kernel, random forest kernel, to enhance the empirical performance of MMD GAN. Different from common forests with deterministic routings, a probabilistic routing variant is used in our innovated random-forest kernel, which is possible to merge with the CNN frameworks. Our propo...	1.667	0.943	3	http://anonymous.4open.science/repository/a233b01f-f072-430d-8b3d-3871804c58f1	[ "GANs", "MMD", "kernel", "random forest", "unbiased gradients" ]	null	https://openreview.net/forum?id=HJxhWa4KDr	CV	3	null	null		null	unknown
H1MOqeHYvB	2,020	rejected	CV	At Your Fingertips: Automatic Piano Fingering Detection	Automatic Piano Fingering is a hard task which computers can learn using data. As data collection is hard and expensive, we propose to automate this process by automatically extracting fingerings from public videos and MIDI files, using computer-vision techniques. Running this process on 90 videos results in the larges...	1.667	0.943	3	https://drive.google.com/file/d/1kDPZSA7ppOaup9Q1Dab7bW4OXNh9mAQA/view?usp=sharing	[ "piano", "fingering", "dataset" ]	null	https://openreview.net/forum?id=H1MOqeHYvB	CV	3	null	null		null	unknown
Bylthp4Yvr	2,020	rejected	CV	Dropout: Explicit Forms and Capacity Control	We investigate the capacity control provided by dropout in various machine learning problems. First, we study dropout for matrix sensing, where it induces a data-dependent regularizer that, in expectation, equals the weighted trace-norm of the product of the factors. In deep learning, we show that the data-dependent re...	1.5	0.866	4	https://www.dropbox.com/s/inptu0exz9iz4cb/c75_drop.py?dl=0	[]	null	https://openreview.net/forum?id=Bylthp4Yvr	CV	1	null	null		null	unknown
HklPzxHFwB	2,020	rejected	CV	Zero-Shot Policy Transfer with Disentangled Attention	Domain adaptation is an open problem in deep reinforcement learning (RL). Often, agents are asked to perform in environments where data is difficult to obtain. In such settings, agents are trained in similar environments, such as simulators, and are then transferred to the original environment. The gap between visual o...	1	0	3	https://drive.google.com/open?id=1KRaAjLofmAGpk2OhsmjUaXG1-DExkpOy	[ "Transfer Learning", "Reinforcement Learning", "Attention", "Domain Adaptation", "Representation Learning", "Feature Extraction" ]	null	https://openreview.net/forum?id=HklPzxHFwB	CV	3	RL	light	Visual Cartpole and DeepMind Lab RL; small environments	null	unknown
ryeHuJBtPH	2,020	accepted_poster	Graph	Hyper-SAGNN: a self-attention based graph neural network for hypergraphs	Graph representation learning for hypergraphs can be utilized to extract patterns among higher-order interactions that are critically important in many real world problems. Current approaches designed for hypergraphs, however, are unable to handle different types of hypergraphs and are typically not generic for various...	8	0	2	https://drive.google.com/drive/folders/1kIOc4SlAJllUJsrr2OnZ4izIQIw2JexU?usp=sharing	[ "graph neural network", "hypergraph", "representation learning" ]	null	https://openreview.net/forum?id=ryeHuJBtPH	Graph	5	null	null		null	unknown
S1esMkHYPr	2,020	accepted_poster	Graph	GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation	Molecular graph generation is a fundamental problem for drug discovery and has been attracting growing attention. The problem is challenging since it requires not only generating chemically valid molecular structures but also optimizing their chemical properties in the meantime. Inspired by the recent progress in deep ...	6	0	3	http://bit.ly/2lCkfsr	[ "Molecular graph generation", "deep generative models", "normalizing flows", "autoregressive models" ]	null	https://openreview.net/forum?id=S1esMkHYPr	Graph	3	null	null		null	unknown
BJepcaEtwB	2,020	rejected	Graph	Meta-Graph: Few shot Link Prediction via Meta Learning	We consider the task of few shot link prediction, where the goal is to predict missing edges across multiple graphs using only a small sample of known edges. We show that current link prediction methods are generally ill-equipped to handle this task---as they cannot effectively transfer knowledge between graphs in a mu...	6	0	3	https://anonymous.4open.science/r/2212328f-4954-4798-b0f2-dbd75005c9ae/	[ "Meta Learning", "Link Prediction", "Graph Representation Learning", "Graph Neural Networks" ]	null	https://openreview.net/forum?id=BJepcaEtwB	Graph	8	Graph	light	Meta-learning few-shot link prediction on graph benchmarks	null	unknown
SJetQpEYvB	2,020	accepted_poster	Graph	LEARNING EXECUTION THROUGH NEURAL CODE FUSION	As the performance of computer systems stagnates due to the end of Moore’s Law, there is a need for new models that can understand and optimize the execution of general purpose code. While there is a growing body of work on using Graph Neural Networks (GNNs) to learn static representations of source code, these represe...	5.667	2.055	3	https://www.dropbox.com/s/yrjhx8ifowdktwh/ncf_code.zip?dl=0	[ "code understanding", "graph neural networks", "learning program execution", "execution traces", "program performance" ]	null	https://openreview.net/forum?id=SJetQpEYvB	Graph	3	null	null		null	unknown
SJgCEpVtvr	2,020	rejected	Graph	DYNAMIC SELF-TRAINING FRAMEWORK FOR GRAPH CONVOLUTIONAL NETWORKS	Graph neural networks (GNN) such as GCN, GAT, MoNet have achieved state-of-the-art results on semi-supervised learning on graphs. However, when the number of labeled nodes is very small, the performances of GNNs downgrade dramatically. Self-training has proved to be effective for resolving this issue, however, the perf...	5	1.414	3	https://anonymous.4open.science/r/f7efb5cb-adfc-4f47-908a-edc4025c18d8/	[ "self-training", "semi-supervised learning", "graph convolutional networks" ]	null	https://openreview.net/forum?id=SJgCEpVtvr	Graph	3	null	null		null	unknown
H1eF3kStPS	2,020	rejected	Graph	Redundancy-Free Computation Graphs for Graph Neural Networks	Graph Neural Networks (GNNs) are based on repeated aggregations of information across nodes’ neighbors in a graph. However, because common neighbors are shared between different nodes, this leads to repeated and inefficient computations.We propose Hierarchically Aggregated computation Graphs (HAGs), a new GNN graph re...	5	1.414	3	https://www.dropbox.com/sh/pce8j67byr6bq83/AACh0UKkua0toPM_ZjtTTVava?dl=0	[ "Graph Neural Networks", "Runtime Performance" ]	null	https://openreview.net/forum?id=H1eF3kStPS	Graph	9	null	null		null	unknown
BygZK2VYvB	2,020	rejected	Graph	Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization	Graph Neural Networks (GNNs) broadly follow the scheme that the representation vector of each node is updated recursively using the message from neighbor nodes, where the message of a neighbor is usually pre-processed with a parameterized transform matrix. To make better use of edge features, we propose the Edge Inform...	5	2.121	4	https://drive.google.com/file/d/1HtOWRuLBcuggsSIrEjHC-1-Lq8W-8KYb/view?usp=sharing	[ "Graph Neural Network", "Edge Feature", "Mutual Information" ]	null	https://openreview.net/forum?id=BygZK2VYvB	Graph	5	null	null		null	unknown
ByeDl1BYvH	2,020	rejected	Graph	Global graph curvature	Recently, non-Euclidean spaces became popular for embedding structured data. However, determining suitable geometry and, in particular, curvature for a given dataset is still an open problem. In this paper, we define a notion of global graph curvature, specifically catered to the problem of embedding graphs, and analyz...	5	1.414	3	https://drive.google.com/open?id=12i5LD6yTyFRDrgkMJEencDAI0Au6Isrn	[ "graph curvature", "graph embedding", "hyperbolic space", "distortion", "Ollivier curvature", "Forman curvature" ]	null	https://openreview.net/forum?id=ByeDl1BYvH	Graph	2	null	null		null	unknown
ryeEr0EFvS	2,020	rejected	Graph	A Hierarchy of Graph Neural Networks Based on Learnable Local Features	Graph neural networks (GNNs) are a powerful tool to learn representations on graphs by iteratively aggregating features from node neighbourhoods. Many variant models have been proposed, but there is limited understanding on both how to compare different architectures and how to construct GNNs systematically. Here, we p...	4.667	2.357	3	https://anonymous.4open.science/r/13513fff-1a5f-42a6-857f-f5d39051b565/	[ "Graph Neural Networks", "Hierarchy", "Weisfeiler-Lehman", "Discriminative Power" ]	null	https://openreview.net/forum?id=ryeEr0EFvS	Graph	4	null	null		null	unknown
BkxfshNYwB	2,020	rejected	Graph	Mincut Pooling in Graph Neural Networks	The advance of node pooling operations in Graph Neural Networks (GNNs) has lagged behind the feverish design of new message-passing techniques, and pooling remains an important and challenging endeavor for the design of deep architectures. In this paper, we propose a pooling operation for GNNs that leverages a differen...	4.667	2.357	3	https://www.dropbox.com/s/n4376n70uvwxjhj/ICLR_code_mincut.zip?dl=0	[ "Graph Neural Networks", "Pooling", "Graph Cuts", "Spectral Clustering" ]	null	https://openreview.net/forum?id=BkxfshNYwB	Graph	3	null	null		null	unknown

End of preview.

ICLR Papers with Accessible Code

A dataset of 1,051 papers from ICLR (2020-2026) with verified code repositories and complete peer reviews from all reviewers.

Dataset Summary

This dataset contains rejected and borderline-accepted papers from ICLR (International Conference on Learning Representations) with accessible code and full peer review text.

Contents:

1,051 papers total
3,900 reviews (average 3.71 per paper)
944 rejected (90%) + 107 poster-tier accepted (10%)
100% review text completeness
Years: 2020-2026

Dataset Structure

Files

File	Size	Description
`all_1051_papers.jsonl`	2 MB	Metadata only (fast filtering)
`all_1051_papers.csv`	200 KB	Spreadsheet format
`all_1051_papers_full_reviews.json`	29 MB	Full reviews keyed by forum ID
`all_1051_papers_with_reviews.jsonl`	28 MB	Complete dataset (metadata + reviews)

Data Fields

Paper Metadata

forum (string): OpenReview forum ID (unique identifier)
year (int): ICLR year (2020-2026)
status (string): "rejected" or "accepted_poster"
title (string): Paper title
abstract (string): Full abstract
domain (string): NLP | CV | Graph | TimeSeries | RL | Other
domain_llm (string): LLM-confirmed domain (354 papers)
feasibility (string): "light" (CPU) | "fits40" (≤40GB GPU) | "toobig" (>40GB) | null
avg_score (float): Average reviewer rating (0-10)
std_score (float): Standard deviation of ratings
num_reviews (int): Number of reviews (2-8)
code_url (string): Code repository URL
openreview_url (string): Direct OpenReview link

Reviews (per paper)

id (string): Review ID
reviewer_signature (list): Anonymous reviewer identifier
rating (int/string): Overall rating (1-10 or text)
confidence (int): Reviewer confidence (1-5)
soundness (int): Technical soundness (1-5)
presentation (int): Presentation quality (1-5)
contribution (int): Contribution score (1-5)
summary (string): Review summary
review_text (string): Full review body
strengths (string): Paper strengths
weaknesses (string): Paper weaknesses
questions (string): Questions for authors

Dataset Statistics

Papers by Domain

Domain	Papers	%
NLP	265	25.2%
Other	247	23.5%
CV	210	20.0%
RL	140	13.3%
TimeSeries	102	9.7%
Graph	87	8.3%

Papers by Year

Year	Papers	%
2020	291	27.7%
2021	2	0.2%
2022	5	0.5%
2023	16	1.5%
2024	81	7.7%
2025	188	17.9%
2026	468	44.5%

Reviews per Paper

Reviews	Papers	%
2	11	1.0%
3	384	36.5%
4	575	54.7%
5	68	6.5%
6+	13	1.2%

Compute Feasibility (354 classified)

Feasibility	Papers	Description
toobig	137	Requires >40GB GPU
light	116	CPU or minimal GPU
fits40	101	Single ≤40GB GPU
unclassified	697	Not yet classified

Usable (light + fits40): 217 papers (20.6% of total)

Usage Examples

Load metadata only (fast)

from datasets import load_dataset

dataset = load_dataset("Vidushee/iclr-papers-with-code-1k", data_files="all_1051_papers.jsonl")
papers = dataset['train']

# Filter by domain
nlp_papers = papers.filter(lambda x: x['domain'] == 'NLP')

Load complete dataset with reviews

from datasets import load_dataset

dataset = load_dataset("Vidushee/iclr-papers-with-code-1k", data_files="all_1051_papers_with_reviews.jsonl")
papers = dataset['train']

# Access reviews
paper = papers[0]
print(f"Title: {paper['title']}")
print(f"Reviews: {len(paper['reviews'])}")
for review in paper['reviews']:
    print(f"  Rating: {review['rating']}")
    print(f"  Summary: {review['summary'][:100]}...")

Filter by compute feasibility

# Get papers that run on CPU or single GPU
usable_papers = papers.filter(lambda x: x['feasibility'] in ['light', 'fits40'])

Filter by review scores

# High-scoring rejected papers
high_score = papers.filter(lambda x: x['status'] == 'rejected' and x['avg_score'] >= 6.0)

# Low-variance papers (consistent reviews)
low_variance = papers.filter(lambda x: x['std_score'] <= 1.0)

Use Cases

1. AI-Assisted Paper Revision

Train models to:

Generate constructive review feedback
Suggest paper improvements based on reviewer comments
Predict review scores from paper content

2. Code Availability Research

Analyze:

Correlation between code availability and acceptance
Impact of code quality on review scores
Reproducibility in ML research

3. Peer Review Analysis

Study:

Review bias and variance
Reviewer agreement patterns
Common rejection reasons by domain

4. Rejected Paper Analysis

Identify high-quality rejected papers
Find papers with addressable weaknesses
Discover overlooked research contributions

Limitations

Code Link Expiration: Links verified in July 2026 may become unavailable over time
Incomplete Classification: Only 33.7% have compute feasibility estimates
Year Bias: 89% from 2024-2026
Domain Tagging: Keyword-based domain field can be noisy; use domain_llm when available
Status Bias: Excludes spotlight/oral tier acceptances (only rejected + poster tier)
No Full Review Text in CSV: Only review metadata in CSV; use JSONL files for full text

Citation

@dataset{iclr_papers_with_code_2026,
  title = {ICLR Papers with Accessible Code: 1K Papers with Full Peer Reviews},
  author = {Sharma, Karun},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/Vidushee/iclr-papers-with-code-1k}
}

Individual papers should be cited via their OpenReview links.

License

Reviews: OpenReview public data (fair use for research)
Paper metadata: Public ICLR submissions
Code URLs: Links to public repositories
Dataset compilation: CC-BY-4.0

Individual papers retain their original licenses. This dataset provides metadata and reviews, not the papers themselves.

Data Source

Data collected from OpenReview.net public ICLR submissions (2020-2026). All papers have verified code repositories accessible at time of collection (July 2026).

Acknowledgments

OpenReview.net for making peer reviews public
ICLR for transparent review process
Authors who released code for their papers

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Total file size:

62.6 MB