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280016481 | 2409.19850 | 2024-09-30 | SATA: Spatial Autocorrelation Token Analysis for Enhancing the Robustness of Vision Transformers | Over the past few years, vision transformers (ViTs) have consistently demonstrated remarkable performance across various visual recognition tasks. However, attempts to enhance their robustness have yielded limited success, mainly focusing on different training strategies, input patch augmentation, or network structural... | [
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272987879 | 2409.20310 | 2024-09-30 | A SSM is Polymerized from Multivariate Time Series | For multivariate time series (MTS) tasks, previous state space models (SSMs) followed the modeling paradigm of Transformer-based methods. However, none of them explicitly model the complex dependencies of MTS: the Channel Dependency variations with Time (CDT). In view of this, we delve into the derivation of SSM, which... | [
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273022849 | 2410.00204 | 2024-09-30 | OpenAnimals: Revisiting Person Re-Identification for Animals Towards Better Generalization | This paper addresses the challenge of animal re-identification, an emerging field that shares similarities with person re-identification but presents unique complexities due to the diverse species, environments and poses. To facilitate research in this domain, we introduce OpenAnimals, a flexible and extensible codebas... | [
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272988136 | 2409.20138 | 2024-09-30 | Constraint Guided Model Quantization of Neural Networks | Deploying neural networks on the edge has become increasingly important as deep learning is being applied in an increasing amount of applications. At the edge computing hardware typically has limited resources disallowing to run neural networks with high complexity. To reduce the complexity of neural networks a wide ra... | [
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272986684 | 2409.20502 | 2024-09-30 | COLLAGE: Collaborative Human-Agent Interaction Generation using Hierarchical Latent Diffusion and Language Models | We propose a novel framework COLLAGE for generating collaborative agent-object-agent interactions by leveraging large language models (LLMs) and hierarchical motion-specific vector-quantized variational autoencoders (VQ-VAEs). Our model addresses the lack of rich datasets in this domain by incorporating the knowledge a... | [
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272986985 | 2409.19945 | 2024-09-30 | One Shot GANs for Long Tail Problem in Skin Lesion Dataset using novel content space assessment metric | Long tail problems frequently arise in the medical field, particularly due to the scarcity of medical data for rare conditions. This scarcity often leads to models overfitting on such limited samples. Consequently, when training models on datasets with heavily skewed classes, where the number of samples varies signific... | [
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272987187 | 2409.19960 | 2024-09-30 | TROPE: TRaining-Free Object-Part Enhancement for Seamlessly Improving Fine-Grained Zero-Shot Image Captioning | Zero-shot inference, where pre-trained models perform tasks without specific training data, is an exciting emergent ability of large models like CLIP. Although there has been considerable exploration into enhancing zero-shot abilities in image captioning (IC) for popular datasets such as MSCOCO and Flickr8k, these appr... | [
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272987478 | 2409.20441 | 2024-09-30 | Instance-adaptive Zero-shot Chain-of-Thought Prompting | Zero-shot Chain-of-Thought (CoT) prompting emerges as a simple and effective strategy for enhancing the performance of large language models (LLMs) in real-world reasoning tasks. Nonetheless, the efficacy of a singular, task-level prompt uniformly applied across the whole of instances is inherently limited since one pr... | [
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273186186 | 2410.03734 | 2024-09-30 | Accent conversion using discrete units with parallel data synthesized from controllable accented TTS | The goal of accent conversion (AC) is to convert speech accents while preserving content and speaker identity. Previous methods either required reference utterances during inference, did not preserve speaker identity well, or used one-to-one systems that could only be trained for each non-native accent. This paper pres... | [
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272986647 | 2409.20293 | 2024-09-30 | Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision | Foundation models such as the recently introduced Segment Anything Model (SAM) have achieved remarkable results in image segmentation tasks. However, these models typically require user interaction through handcrafted prompts such as bounding boxes, which limits their deployment to downstream tasks. Adapting these mode... | [
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272987066 | 2409.20361 | 2024-09-30 | Rotated Runtime Smooth: Training-Free Activation Smoother for accurate INT4 inference | Large language models have demonstrated promising capabilities upon scaling up parameters. However, serving large language models incurs substantial computation and memory movement costs due to their large scale. Quantization methods have been employed to reduce service costs and latency. Nevertheless, outliers in acti... | [
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272987589 | 2409.20075 | 2024-09-30 | BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain | Retrieval Augmented Generation (RAG) system is important in domains such as e-commerce, which has many long-tail entities and frequently updated information. Most existing works adopt separate modules for retrieval and generation, which may be suboptimal since the retrieval task and the generation task cannot benefit f... | [
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272987385 | 2409.20187 | 2024-09-30 | Choosing DAG Models Using Markov and Minimal Edge Count in the Absence of Ground Truth | We give a novel nonparametric pointwise consistent statistical test (the Markov Checker) of the Markov condition for directed acyclic graph (DAG) or completed partially directed acyclic graph (CPDAG) models given a dataset. We also introduce the Cross-Algorithm Frugality Search (CAFS) for rejecting DAG models that eith... | [
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273023225 | 2410.00168 | 2024-09-30 | SSR: Alignment-Aware Modality Connector for Speech Language Models | Fusing speech into pre-trained language model (SpeechLM) usually suffers from inefficient encoding of long-form speech and catastrophic forgetting of pre-trained text modality. We propose SSR-Connector (Segmented Speech Representation Connector) for better modality fusion. Leveraging speech-text alignments, our approac... | [
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272988095 | 2409.19872 | 2024-09-30 | Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration | The swift advancement in Multimodal LLMs (MLLMs) also presents significant challenges for effective knowledge editing. Current methods, including intrinsic knowledge editing and external knowledge resorting, each possess strengths and weaknesses, struggling to balance the desired properties of reliability, generality, ... | [
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272987236 | 2409.19984 | 2024-09-30 | CONTESTS: a Framework for Consistency Testing of Span Probabilities in Language Models | Although language model scores are often treated as probabilities, their reliability as probability estimators has mainly been studied through calibration, overlooking other aspects. In particular, it is unclear whether language models produce the same value for different ways of assigning joint probabilities to word s... | [
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272987523 | 2409.20534 | 2024-09-30 | End-to-End Conformal Calibration for Optimization Under Uncertainty | Machine learning can significantly improve performance for decision-making under uncertainty in a wide range of domains. However, ensuring robustness guarantees requires well-calibrated uncertainty estimates, which can be difficult to achieve in high-capacity prediction models such as deep neural networks. Moreover, in... | [
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272987709 | 2409.19890 | 2024-09-30 | Universal Medical Image Representation Learning with Compositional Decoders | Visual-language models have advanced the development of universal models, yet their application in medical imaging remains constrained by specific functional requirements and the limited data. Current general-purpose models are typically designed with task-specific branches and heads, which restricts the shared feature... | [
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273023318 | 2410.00229 | 2024-09-30 | Stochastic Inverse Problem: stability, regularization and Wasserstein gradient flow | Inverse problems in physical or biological sciences often involve recovering an unknown parameter that is random. The sought-after quantity is a probability distribution of the unknown parameter, that produces data that aligns with measurements. Consequently, these problems are naturally framed as stochastic inverse pr... | [
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272987890 | 2409.19877 | 2024-09-30 | Contrastive Token Learning with Similarity Decay for Repetition Suppression in Machine Translation | For crosslingual conversation and trade, Neural Machine Translation (NMT) is pivotal yet faces persistent challenges with monotony and repetition in generated content. Traditional solutions that rely on penalizing text redundancy or token reoccurrence have shown limited efficacy, particularly for lengthy article and e-... | [
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272987317 | 2409.20556 | 2024-09-30 | Inverse Painting: Reconstructing The Painting Process | Given an input painting, we reconstruct a time-lapse video of how it may have been painted. We formulate this as an autoregressive image generation problem, in which an initially blank "canvas" is iteratively updated. The model learns from real artists by training on many painting videos. Our approach incorporates text... | [
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272987327 | 2409.20092 | 2024-09-30 | Continuous-Time Linear Positional Embedding for Irregular Time Series Forecasting | Irregularly sampled time series forecasting, characterized by non-uniform intervals, is prevalent in practical applications. However, previous research have been focused on regular time series forecasting, typically relying on transformer architectures. To extend transformers to handle irregular time series, we tackle ... | [
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272986933 | 2409.20424 | 2024-09-30 | World to Code: Multi-modal Data Generation via Self-Instructed Compositional Captioning and Filtering | Recent advances in Vision-Language Models (VLMs) and the scarcity of high-quality multi-modal alignment data have inspired numerous researches on synthetic VLM data generation. The conventional norm in VLM data construction uses a mixture of specialists in caption and OCR, or stronger VLM APIs and expensive human annot... | [
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272986917 | 2409.20412 | 2024-09-30 | Conformal Prediction for Dose-Response Models with Continuous Treatments | Understanding the dose-response relation between a continuous treatment and the outcome for an individual can greatly drive decision-making, particularly in areas like personalized drug dosing and personalized healthcare interventions. Point estimates are often insufficient in these high-risk environments, highlighting... | [
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272987906 | 2409.19942 | 2024-09-30 | CycleCrash: A Dataset of Bicycle Collision Videos for Collision Prediction and Analysis | Self-driving research often underrepresents cyclist collisions and safety. To address this, we present CycleCrash, a novel dataset consisting of 3,000 dashcam videos with 436,347 frames that capture cyclists in a range of critical situations, from collisions to safe interactions. This dataset enables 9 different cyclis... | [
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273022838 | 2410.00193 | 2024-09-30 | Do Vision-Language Models Really Understand Visual Language? | Visual language is a system of communication that conveys information through symbols, shapes, and spatial arrangements. Diagrams are a typical example of a visual language depicting complex concepts and their relationships in the form of an image. The symbolic nature of diagrams presents significant challenges for bui... | [
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273022747 | 2410.00171 | 2024-09-30 | Basis-to-Basis Operator Learning Using Function Encoders | We present Basis-to-Basis (B2B) operator learning, a novel approach for learning operators on Hilbert spaces of functions based on the foundational ideas of function encoders. We decompose the task of learning operators into two parts: learning sets of basis functions for both the input and output spaces and learning a... | [
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272987425 | 2409.20296 | 2024-09-30 | PersonalLLM: Tailoring LLMs to Individual Preferences | As LLMs become capable of complex tasks, there is growing potential for personalized interactions tailored to the subtle and idiosyncratic preferences of the user. We present a public benchmark, PersonalLLM, focusing on adapting LLMs to provide maximal benefits for a particular user. Departing from existing alignment b... | [
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272988131 | 2409.20563 | 2024-09-30 | DressRecon: Freeform 4D Human Reconstruction from Monocular Video | We present a method to reconstruct time-consistent human body models from monocular videos, focusing on extremely loose clothing or handheld object interactions. Prior work in human reconstruction is either limited to tight clothing with no object interactions, or requires calibrated multi-view captures or personalized... | [
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273186075 | 2410.03729 | 2024-09-30 | Certifying Guidance & Control Networks: Uncertainty Propagation to an Event Manifold | We perform uncertainty propagation on an event manifold for Guidance & Control Networks (G&CNETs), aiming to enhance the certification tools for neural networks in this field. This work utilizes three previously solved optimal control problems with varying levels of dynamics nonlinearity and event manifold complexity. ... | [
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272986640 | 2409.20305 | 2024-09-30 | Mixed-Precision Embeddings for Large-Scale Recommendation Models | Embedding techniques have become essential components of large databases in the deep learning era. By encoding discrete entities, such as words, items, or graph nodes, into continuous vector spaces, embeddings facilitate more efficient storage, retrieval, and processing in large databases. Especially in the domain of r... | [
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273022631 | 2410.00184 | 2024-09-30 | Volumetric Conditional Score-based Residual Diffusion Model for PET/MR Denoising | PET imaging is a powerful modality offering quantitative assessments of molecular and physiological processes. The necessity for PET denoising arises from the intrinsic high noise levels in PET imaging, which can significantly hinder the accurate interpretation and quantitative analysis of the scans. With advances in d... | [
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272987858 | 2409.20383 | 2024-09-30 | Beyond Derivative Pathology of PINNs: Variable Splitting Strategy with Convergence Analysis | Physics-informed neural networks (PINNs) have recently emerged as effective methods for solving partial differential equations (PDEs) in various problems. Substantial research focuses on the failure modes of PINNs due to their frequent inaccuracies in predictions. However, most are based on the premise that minimizing ... | [
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273023227 | 2410.00271 | 2024-09-30 | GalaxiesML: a dataset of galaxy images, photometry, redshifts, and structural parameters for machine learning | We present a dataset built for machine learning applications consisting of galaxy photometry, images, spectroscopic redshifts, and structural properties. This dataset comprises 286,401 galaxy images and photometry from the Hyper-Suprime-Cam Survey PDR2 in five imaging filters ($g,r,i,z,y$) with spectroscopically confir... | [
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272986742 | 2409.20530 | 2024-09-30 | Dual Encoder GAN Inversion for High-Fidelity 3D Head Reconstruction from Single Images | 3D GAN inversion aims to project a single image into the latent space of a 3D Generative Adversarial Network (GAN), thereby achieving 3D geometry reconstruction. While there exist encoders that achieve good results in 3D GAN inversion, they are predominantly built on EG3D, which specializes in synthesizing near-frontal... | [
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272986643 | 2409.20568 | 2024-09-30 | Continuously Improving Mobile Manipulation with Autonomous Real-World RL | We present a fully autonomous real-world RL framework for mobile manipulation that can learn policies without extensive instrumentation or human supervision. This is enabled by 1) task-relevant autonomy, which guides exploration towards object interactions and prevents stagnation near goal states, 2) efficient policy l... | [
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272987991 | 2409.19841 | 2024-09-30 | Counter-Current Learning: A Biologically Plausible Dual Network Approach for Deep Learning | Despite its widespread use in neural networks, error backpropagation has faced criticism for its lack of biological plausibility, suffering from issues such as the backward locking problem and the weight transport problem. These limitations have motivated researchers to explore more biologically plausible learning algo... | [
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272988058 | 2409.19862 | 2024-09-30 | Learning Multimodal Latent Generative Models with Energy-Based Prior | Multimodal generative models have recently gained significant attention for their ability to learn representations across various modalities, enhancing joint and cross-generation coherence. However, most existing works use standard Gaussian or Laplacian distributions as priors, which may struggle to capture the diverse... | [
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272987608 | 2409.19967 | 2024-09-30 | Magnet: We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function | Text-to-image diffusion models particularly Stable Diffusion, have revolutionized the field of computer vision. However, the synthesis quality often deteriorates when asked to generate images that faithfully represent complex prompts involving multiple attributes and objects. While previous studies suggest that blended... | [
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272987887 | 2409.19833 | 2024-09-30 | HazyDet: Open-Source Benchmark for Drone-View Object Detection with Depth-Cues in Hazy Scenes | Object detection from aerial platforms under adverse atmospheric conditions, particularly haze, is paramount for robust drone autonomy. Yet, this domain remains largely underexplored, primarily hindered by the absence of specialized benchmarks. To bridge this gap, we present \textit{HazyDet}, the first, large-scale ben... | [
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272987220 | 2409.19991 | 2024-09-30 | Robust Multi-view Co-expression Network Inference | Unraveling the co-expression of genes across studies enhances the understanding of cellular processes. Inferring gene co-expression networks from transcriptome data presents many challenges, including spurious gene correlations, sample correlations, and batch effects. To address these complexities, we introduce a robus... | [
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272987272 | 2409.20325 | 2024-09-30 | Old Optimizer, New Norm: An Anthology | Deep learning optimizers are often motivated through a mix of convex and approximate second-order theory. We select three such methods -- Adam, Shampoo and Prodigy -- and argue that each method can instead be understood as a squarely first-order method without convexity assumptions. In fact, after switching off exponen... | [
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273023013 | 2410.00067 | 2024-09-30 | Ranking the Top-K Realizations of Stochastically Known Event Logs | Various kinds of uncertainty can occur in event logs, e.g., due to flawed recording, data quality issues, or the use of probabilistic models for activity recognition. Stochastically known event logs make these uncertainties transparent by encoding multiple possible realizations for events. However, the number of realiz... | [
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273023016 | 2410.00262 | 2024-09-30 | ImmersePro: End-to-End Stereo Video Synthesis Via Implicit Disparity Learning | We introduce \textit{ImmersePro}, an innovative framework specifically designed to transform single-view videos into stereo videos. This framework utilizes a novel dual-branch architecture comprising a disparity branch and a context branch on video data by leveraging spatial-temporal attention mechanisms. \textit{Immer... | [
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272986857 | 2409.20287 | 2024-09-30 | Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation | Convolutional neural networks (CNNs) achieve prevailing results in segmentation tasks nowadays and represent the state-of-the-art for image-based analysis. However, the understanding of the accurate decision-making process of a CNN is rather unknown. The research area of explainable artificial intelligence (xAI) primar... | [
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272986845 | 2409.20547 | 2024-09-30 | Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributions | Sampling from high-dimensional, multi-modal distributions remains a fundamental challenge across domains such as statistical Bayesian inference and physics-based machine learning. In this paper, we propose Annealing Flow (AF), a method built on Continuous Normalizing Flow (CNF) for sampling from high-dimensional and mu... | [
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272986834 | 2409.19835 | 2024-09-30 | MoCoLSK: Modality Conditioned High-Resolution Downscaling for Land Surface Temperature | Land Surface Temperature (LST) is a critical parameter for environmental studies, but directly obtaining high spatial resolution LST data remains challenging due to the spatio-temporal trade-off in satellite remote sensing. Guided LST downscaling has emerged as an alternative solution to overcome these limitations, but... | [
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272986636 | 2409.20063 | 2024-09-30 | Q-Bench-Video: Benchmarking the Video Quality Understanding of LMMs | With the rising interest in research on Large Multi-modal Models (LMMs) for video understanding, many studies have emphasized general video comprehension capabilities, neglecting the systematic exploration into video quality understanding. To address this oversight, we introduce Q-Bench-Video in this paper, a new bench... | [
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273022546 | 2410.00117 | 2024-09-30 | An Overview of the Burer-Monteiro Method for Certifiable Robot Perception | This paper presents an overview of the Burer-Monteiro method (BM), a technique that has been applied to solve robot perception problems to certifiable optimality in real-time. BM is often used to solve semidefinite programming relaxations, which can be used to perform global optimization for non-convex perception probl... | [
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273022738 | 2410.00218 | 2024-09-30 | T-KAER: Transparency-enhanced Knowledge-Augmented Entity Resolution Framework | Entity resolution (ER) is the process of determining whether two representations refer to the same real-world entity and plays a crucial role in data curation and data cleaning. Recent studies have introduced the KAER framework, aiming to improve pre-trained language models by augmenting external knowledge. However, id... | [
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272986703 | 2409.20206 | 2024-09-30 | SetPINNs: Set-based Physics-informed Neural Networks | Physics-Informed Neural Networks (PINNs) solve partial differential equations using deep learning. However, conventional PINNs perform pointwise predictions that neglect dependencies within a domain, which may result in suboptimal solutions. We introduce SetPINNs, a framework that effectively captures local dependencie... | [
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273026034 | 2410.00923 | 2024-09-30 | On the topology and geometry of population-based SHM | Population-Based Structural Health Monitoring (PBSHM), aims to leverage information across populations of structures in order to enhance diagnostics on those with sparse data. The discipline of transfer learning provides the mechanism for this capability. One recent paper in PBSHM proposed a geometrical view in which t... | [
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272986911 | 2409.20089 | 2024-09-30 | Robust LLM safeguarding via refusal feature adversarial training | Large language models (LLMs) are vulnerable to adversarial attacks that can elicit harmful responses. Defending against such attacks remains challenging due to the opacity of jailbreaking mechanisms and the high computational cost of training LLMs robustly. We demonstrate that adversarial attacks share a universal mech... | [
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273022727 | 2410.00267 | 2024-09-30 | KPCA-CAM: Visual Explainability of Deep Computer Vision Models using Kernel PCA | Deep learning models often function as black boxes, providing no straightforward reasoning for their predictions. This is particularly true for computer vision models, which process tensors of pixel values to generate outcomes in tasks such as image classification and object detection. To elucidate the reasoning of the... | [
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272987118 | 2409.20054 | 2024-09-30 | Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis | We investigate zero-shot cross-lingual news sentiment detection, aiming to develop robust sentiment classifiers that can be deployed across multiple languages without target-language training data. We introduce novel evaluation datasets in several less-resourced languages, and experiment with a range of approaches incl... | [
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272987704 | 2409.19998 | 2024-09-30 | Do Influence Functions Work on Large Language Models? | Influence functions are important for quantifying the impact of individual training data points on a model's predictions. Although extensive research has been conducted on influence functions in traditional machine learning models, their application to large language models (LLMs) has been limited. In this work, we con... | [
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272986953 | 2409.20429 | 2024-09-30 | HELPD: Mitigating Hallucination of LVLMs by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding | Large Vision-Language Models (LVLMs) have shown remarkable performance on many visual-language tasks. However, these models still suffer from multimodal hallucination, which means the generation of objects or content that violates the images. Many existing work detects hallucination by directly judging whether an objec... | [
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272987165 | 2409.19946 | 2024-09-30 | Illustrious: an Open Advanced Illustration Model | In this work, we share the insights for achieving state-of-the-art quality in our text-to-image anime image generative model, called Illustrious. To achieve high resolution, dynamic color range images, and high restoration ability, we focus on three critical approaches for model improvement. First, we delve into the si... | [
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273185618 | 2410.03737 | 2024-09-30 | Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN | As wireless networks grow to support more complex applications, the Open Radio Access Network (O-RAN) architecture, with its smart RAN Intelligent Controller (RIC) modules, becomes a crucial solution for real-time network data collection, analysis, and dynamic management of network resources including radio resource bl... | [
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272987798 | 2409.20500 | 2024-09-30 | FreeMask: Rethinking the Importance of Attention Masks for Zero-Shot Video Editing | Text-to-video diffusion models have made remarkable advancements. Driven by their ability to generate temporally coherent videos, research on zero-shot video editing using these fundamental models has expanded rapidly. To enhance editing quality, structural controls are frequently employed in video editing. Among these... | [
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272987148 | 2409.19986 | 2024-09-30 | SuperPose: Improved 6D Pose Estimation with Robust Tracking and Mask-Free Initialization | We developed a robust solution for real-time 6D object detection in industrial applications by integrating FoundationPose, SAM2, and LightGlue, eliminating the need for retraining. Our approach addresses two key challenges: the requirement for an initial object mask in the first frame in FoundationPose and issues with ... | [
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272987108 | 2409.19911 | 2024-09-30 | Replace Anyone in Videos | The field of controllable human-centric video generation has witnessed remarkable progress, particularly with the advent of diffusion models. However, achieving precise and localized control over human motion in videos, such as replacing or inserting individuals while preserving desired motion patterns, still remains a... | [
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273022639 | 2410.00210 | 2024-09-30 | End-to-end Piano Performance-MIDI to Score Conversion with Transformers | The automated creation of accurate musical notation from an expressive human performance is a fundamental task in computational musicology. To this end, we present an end-to-end deep learning approach that constructs detailed musical scores directly from real-world piano performance-MIDI files. We introduce a modern tr... | [
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272987074 | 2409.19839 | 2024-09-30 | ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities | Forecasts of future events are essential inputs into informed decision-making. Machine learning (ML) systems have the potential to deliver forecasts at scale, but there is no framework for evaluating the accuracy of ML systems on a standardized set of forecasting questions. To address this gap, we introduce ForecastBen... | [
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273022566 | 2410.00270 | 2024-09-30 | Real-time Diverse Motion In-betweening with Space-time Control | In this work, we present a data-driven framework for generating diverse in-betweening motions for kinematic characters. Our approach injects dynamic conditions and explicit motion controls into the procedure of motion transitions. Notably, this integration enables a finer-grained spatial-temporal control by allowing us... | [
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