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272969025 | 2409.18578 | 2024-09-27 | An Enhanced Federated Prototype Learning Method under Domain Shift | Federated Learning (FL) allows collaborative machine learning training without sharing private data. Numerous studies have shown that one significant factor affecting the performance of federated learning models is the heterogeneity of data across different clients, especially when the data is sampled from various doma... | [
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272969271 | 2409.18446 | 2024-09-27 | Exploring Language Model Generalization in Low-Resource Extractive QA | In this paper, we investigate Extractive Question Answering (EQA) with Large Language Models (LLMs) under domain drift, i.e., can LLMs generalize to domains that require specific knowledge such as medicine and law in a zero-shot fashion without additional in-domain training? To this end, we devise a series of experimen... | [
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272968906 | 2409.18850 | 2024-09-27 | Two Sparse Matrices are Better than One: Sparsifying Neural Networks with Double Sparse Factorization | Neural networks are often challenging to work with due to their large size and complexity. To address this, various methods aim to reduce model size by sparsifying or decomposing weight matrices, such as magnitude pruning and low-rank or block-diagonal factorization. In this work, we present Double Sparse Factorization... | [
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272969011 | 2409.18399 | 2024-09-27 | Multimodal Trajectory Prediction for Autonomous Driving on Unstructured Roads using Deep Convolutional Network | Recently, the application of autonomous driving in open-pit mining has garnered increasing attention for achieving safe and efficient mineral transportation. Compared to urban structured roads, unstructured roads in mining sites have uneven boundaries and lack clearly defined lane markings. This leads to a lack of suff... | [
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272969440 | 2409.18901 | 2024-09-27 | Improving Visual Object Tracking through Visual Prompting | Learning a discriminative model to distinguish a target from its surrounding distractors is essential to generic visual object tracking. Dynamic target representation adaptation against distractors is challenging due to the limited discriminative capabilities of prevailing trackers. We present a new visual Prompting me... | [
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272968882 | 2409.18583 | 2024-09-27 | Hit the Sweet Spot! Span-Level Ensemble for Large Language Models | Ensembling various LLMs to unlock their complementary potential and leverage their individual strengths is highly valuable. Previous studies typically focus on two main paradigms: sample-level and token-level ensembles. Sample-level ensemble methods either select or blend fully generated outputs, which hinders dynamic ... | [
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272968730 | 2409.18953 | 2024-09-27 | UniCal: Unified Neural Sensor Calibration | Self-driving vehicles (SDVs) require accurate calibration of LiDARs and cameras to fuse sensor data accurately for autonomy. Traditional calibration methods typically leverage fiducials captured in a controlled and structured scene and compute correspondences to optimize over. These approaches are costly and require su... | [
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272986608 | 2409.19044 | 2024-09-27 | On the Inductive Bias of Stacking Towards Improving Reasoning | Given the increasing scale of model sizes, novel training strategies like gradual stacking [Gong et al., 2019, Reddi et al., 2023] have garnered interest. Stacking enables efficient training by gradually growing the depth of a model in stages and using layers from a smaller model in an earlier stage to initialize the n... | [
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272969181 | 2409.18800 | 2024-09-27 | MiniVLN: Efficient Vision-and-Language Navigation by Progressive Knowledge Distillation | In recent years, Embodied Artificial Intelligence (Embodied AI) has advanced rapidly, yet the increasing size of models conflicts with the limited computational capabilities of Embodied AI platforms. To address this challenge, we aim to achieve both high model performance and practical deployability. Specifically, we f... | [
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272969223 | 2409.18860 | 2024-09-27 | LW2G: Learning Whether to Grow for Prompt-based Continual Learning | Recent Prompt-based Continual learning (PCL) has achieved remarkable performance with pre-trained models. These approaches expand a prompt pool by adding a new set of prompts while learning and select the correct set during inference. Previous studies have revealed that learning task-wised prompt sets individually and ... | [
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272968907 | 2409.18418 | 2024-09-27 | A3: Active Adversarial Alignment for Source-Free Domain Adaptation | Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Recent works have focused on source-free UDA, where only target data is available. This is challenging as models rely on noisy pseudo-labels and struggle with distribution shifts. We propose Activ... | [
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272968954 | 2409.18442 | 2024-09-27 | Gradient-free Decoder Inversion in Latent Diffusion Models | In latent diffusion models (LDMs), denoising diffusion process efficiently takes place on latent space whose dimension is lower than that of pixel space. Decoder is typically used to transform the representation in latent space to that in pixel space. While a decoder is assumed to have an encoder as an accurate inverse... | [
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272969019 | 2409.18686 | 2024-09-27 | A Novel Unified Architecture for Low-Shot Counting by Detection and Segmentation | Low-shot object counters estimate the number of objects in an image using few or no annotated exemplars. Objects are localized by matching them to prototypes, which are constructed by unsupervised image-wide object appearance aggregation. Due to potentially diverse object appearances, the existing approaches often lead... | [
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272969033 | 2409.18572 | 2024-09-27 | Towards an active-learning approach to resource allocation for population-based damage prognosis | Damage prognosis is, arguably, one of the most difficult tasks of structural health monitoring (SHM). To address common problems of damage prognosis, a population-based SHM (PBSHM) approach is adopted in the current work. In this approach the prognosis problem is considered as an information-sharing problem where data ... | [
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272986619 | 2409.19069 | 2024-09-27 | Localizing Memorization in SSL Vision Encoders | Recent work on studying memorization in self-supervised learning (SSL) suggests that even though SSL encoders are trained on millions of images, they still memorize individual data points. While effort has been put into characterizing the memorized data and linking encoder memorization to downstream utility, little is ... | [
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272968789 | 2409.18653 | 2024-09-27 | When SAM2 Meets Video Camouflaged Object Segmentation: A Comprehensive Evaluation and Adaptation | This study investigates the application and performance of the Segment Anything Model 2 (SAM2) in the challenging task of video camouflaged object segmentation (VCOS). VCOS involves detecting objects that blend seamlessly in the surroundings for videos, due to similar colors and textures, poor light conditions, etc. Co... | [
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272969438 | 2409.18411 | 2024-09-27 | Hi-Drive: Hierarchical POMDP Planning for Safe Autonomous Driving in Diverse Urban Environments | Uncertainties in dynamic road environments pose significant challenges for behavior and trajectory planning in autonomous driving. This paper introduces Hi-Drive, a hierarchical planning algorithm addressing uncertainties at both behavior and trajectory levels using a hierarchical Partially Observable Markov Decision P... | [
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272968777 | 2409.18694 | 2024-09-27 | Learning from Pattern Completion: Self-supervised Controllable Generation | The human brain exhibits a strong ability to spontaneously associate different visual attributes of the same or similar visual scene, such as associating sketches and graffiti with real-world visual objects, usually without supervising information. In contrast, in the field of artificial intelligence, controllable gene... | [
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272968913 | 2409.18479 | 2024-09-27 | CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns | The stable periodic patterns present in time series data serve as the foundation for conducting long-horizon forecasts. In this paper, we pioneer the exploration of explicitly modeling this periodicity to enhance the performance of models in long-term time series forecasting (LTSF) tasks. Specifically, we introduce the... | [
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272987479 | 2409.19039 | 2024-09-27 | Gaussian Heritage: 3D Digitization of Cultural Heritage with Integrated Object Segmentation | The creation of digital replicas of physical objects has valuable applications for the preservation and dissemination of tangible cultural heritage. However, existing methods are often slow, expensive, and require expert knowledge. We propose a pipeline to generate a 3D replica of a scene using only RGB images (e.g. ph... | [
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272969207 | 2409.18417 | 2024-09-27 | VickreyFeedback: Cost-efficient Data Construction for Reinforcement Learning from Human Feedback | This paper addresses the cost-efficiency aspect of Reinforcement Learning from Human Feedback (RLHF). RLHF leverages datasets of human preferences over outputs of large language models (LLM)s to instill human expectations into LLMs. Although preference annotation comes with a monetized cost, the economic utility of a p... | [
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272969320 | 2409.18878 | 2024-09-27 | Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models | Accurate identification and categorization of suicidal events can yield better suicide precautions, reducing operational burden, and improving care quality in high-acuity psychiatric settings. Pre-trained language models offer promise for identifying suicidality from unstructured clinical narratives. We evaluated the p... | [
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272969123 | 2409.18397 | 2024-09-27 | Scientific Machine Learning Seismology | Scientific machine learning (SciML) is an interdisciplinary research field that integrates machine learning, particularly deep learning, with physics theory to understand and predict complex natural phenomena. By incorporating physical knowledge, SciML reduces the dependency on observational data, which is often limite... | [
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271314404 | 2409.18499 | 2024-09-27 | Fairness-aware Multiobjective Evolutionary Learning | Multiobjective evolutionary learning (MOEL) has demonstrated its advantages of training fairer machine learning models considering a predefined set of conflicting objectives, including accuracy and different fairness measures. Recent works propose to construct a representative subset of fairness measures as optimisatio... | [
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272969083 | 2409.18828 | 2024-09-27 | MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal | Electrocardiogram (ECG) is an important non-invasive method for diagnosing cardiovascular disease. However, ECG signals are susceptible to noise contamination, such as electrical interference or signal wandering, which reduces diagnostic accuracy. Various ECG denoising methods have been proposed, but most existing meth... | [
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272969367 | 2409.18644 | 2024-09-27 | Incorporating Precedents for Legal Judgement Prediction on European Court of Human Rights Cases | Inspired by the legal doctrine of stare decisis, which leverages precedents (prior cases) for informed decision-making, we explore methods to integrate them into LJP models. To facilitate precedent retrieval, we train a retriever with a fine-grained relevance signal based on the overlap ratio of alleged articles betwee... | [
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272968791 | 2409.18679 | 2024-09-27 | "Why" Has the Least Side Effect on Model Editing | Training large language models (LLMs) from scratch is an expensive endeavor, particularly as world knowledge continually evolves. To maintain relevance and accuracy of LLMs, model editing has emerged as a pivotal research area. While these methods hold promise, they can also produce unintended side effects. Their under... | [
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272986601 | 2409.19104 | 2024-09-27 | Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure | The rapid scaling of AI has spurred a growing emphasis on ethical considerations in both development and practice. This has led to the formulation of increasingly sophisticated model auditing and reporting requirements, as well as governance frameworks to mitigate potential risks to individuals and society. At this cri... | [
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272969336 | 2409.18885 | 2024-09-27 | HR-Extreme: A High-Resolution Dataset for Extreme Weather Forecasting | The application of large deep learning models in weather forecasting has led to significant advancements in the field, including higher-resolution forecasting and extended prediction periods exemplified by models such as Pangu and Fuxi. Despite these successes, previous research has largely been characterized by the ne... | [
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272969372 | 2409.18881 | 2024-09-27 | Explainable Artifacts for Synthetic Western Blot Source Attribution | Recent advancements in artificial intelligence have enabled generative models to produce synthetic scientific images that are indistinguishable from pristine ones, posing a challenge even for expert scientists habituated to working with such content. When exploited by organizations known as paper mills, which systemati... | [
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272969474 | 2409.18964 | 2024-09-27 | PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation | We present PhysGen, a novel image-to-video generation method that converts a single image and an input condition (e.g., force and torque applied to an object in the image) to produce a realistic, physically plausible, and temporally consistent video. Our key insight is to integrate model-based physical simulation with ... | [
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272987138 | 2409.19111 | 2024-09-27 | Fusion is all you need: Face Fusion for Customized Identity-Preserving Image Synthesis | Text-to-image (T2I) models have significantly advanced the development of artificial intelligence, enabling the generation of high-quality images in diverse contexts based on specific text prompts. However, existing T2I-based methods often struggle to accurately reproduce the appearance of individuals from a reference ... | [
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272969060 | 2409.18565 | 2024-09-27 | Harmonizing knowledge Transfer in Neural Network with Unified Distillation | Knowledge distillation (KD), known for its ability to transfer knowledge from a cumbersome network (teacher) to a lightweight one (student) without altering the architecture, has been garnering increasing attention. Two primary categories emerge within KD methods: feature-based, focusing on intermediate layers' feature... | [
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272988014 | 2409.19075 | 2024-09-27 | Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource Commonsense Reasoning | Meta learning has been widely used to exploit rich-resource source tasks to improve the performance of low-resource target tasks. Unfortunately, most existing meta learning approaches treat different source tasks equally, ignoring the relatedness of source tasks to the target task in knowledge transfer. To mitigate thi... | [
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272968770 | 2409.18409 | 2024-09-27 | Generative Retrieval Meets Multi-Graded Relevance | Generative retrieval represents a novel approach to information retrieval. It uses an encoder-decoder architecture to directly produce relevant document identifiers (docids) for queries. While this method offers benefits, current approaches are limited to scenarios with binary relevance data, overlooking the potential ... | [
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272987311 | 2409.19177 | 2024-09-27 | Evidence Is All You Need: Ordering Imaging Studies via Language Model Alignment with the ACR Appropriateness Criteria | Diagnostic imaging studies are an increasingly important component of the workup and management of acutely presenting patients. However, ordering appropriate imaging studies according to evidence-based medical guidelines is a challenging task with a high degree of variability between healthcare providers. To address th... | [
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272988050 | 2409.19117 | 2024-09-27 | Range-aware Positional Encoding via High-order Pretraining: Theory and Practice | Unsupervised pre-training on vast amounts of graph data is critical in real-world applications wherein labeled data is limited, such as molecule properties prediction or materials science. Existing approaches pre-train models for specific graph domains, neglecting the inherent connections within networks. This limits t... | [
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272969420 | 2409.18859 | 2024-09-27 | Challenges of Generating Structurally Diverse Graphs | For many graph-related problems, it can be essential to have a set of structurally diverse graphs. For instance, such graphs can be used for testing graph algorithms or their neural approximations. However, to the best of our knowledge, the problem of generating structurally diverse graphs has not been explored in the ... | [
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272969435 | 2409.18696 | 2024-09-27 | Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective | Time series forecasting has played a pivotal role across various industries, including finance, transportation, energy, healthcare, and climate. Due to the abundant seasonal information they contain, timestamps possess the potential to offer robust global guidance for forecasting techniques. However, existing works pri... | [
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272969228 | 2409.18764 | 2024-09-27 | Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations | We propose a novel framework that leverages Visual Question Answering (VQA) models to automate the evaluation of LLM-generated data visualizations. Traditional evaluation methods often rely on human judgment, which is costly and unscalable, or focus solely on data accuracy, neglecting the effectiveness of visual commun... | [
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272969051 | 2409.18459 | 2024-09-27 | FoodMLLM-JP: Leveraging Multimodal Large Language Models for Japanese Recipe Generation | Research on food image understanding using recipe data has been a long-standing focus due to the diversity and complexity of the data. Moreover, food is inextricably linked to people's lives, making it a vital research area for practical applications such as dietary management. Recent advancements in Multimodal Large L... | [
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272988105 | 2409.19128 | 2024-09-27 | Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models | Despite the remarkable generation capabilities of Diffusion Models (DMs), conducting training and inference remains computationally expensive. Previous works have been devoted to accelerating diffusion sampling, but achieving data-efficient diffusion training has often been overlooked. In this work, we investigate effi... | [
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272968709 | 2409.18548 | 2024-09-27 | Research on Predicting Public Opinion Event Heat Levels Based on Large Language Models | In recent years, with the rapid development of large language models, serval models such as GPT-4o have demonstrated extraordinary capabilities, surpassing human performance in various language tasks. As a result, many researchers have begun exploring their potential applications in the field of public opinion analysis... | [
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272969105 | 2409.18439 | 2024-09-27 | State-free Reinforcement Learning | In this work, we study the \textit{state-free RL} problem, where the algorithm does not have the states information before interacting with the environment. Specifically, denote the reachable state set by ${S}^\Pi := \{ s|\max_{\pi\in \Pi}q^{P, \pi}(s)>0 \}$, we design an algorithm which requires no information on the ... | [
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272969356 | 2409.18497 | 2024-09-27 | Neural Video Representation for Redundancy Reduction and Consistency Preservation | Implicit neural representation (INR) embed various signals into neural networks. They have gained attention in recent years because of their versatility in handling diverse signal types. In the context of video, INR achieves video compression by embedding video signals directly into networks and compressing them. Conve... | [
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272968810 | 2409.18533 | 2024-09-27 | Prompt-Driven Temporal Domain Adaptation for Nighttime UAV Tracking | Nighttime UAV tracking under low-illuminated scenarios has achieved great progress by domain adaptation (DA). However, previous DA training-based works are deficient in narrowing the discrepancy of temporal contexts for UAV trackers. To address the issue, this work proposes a prompt-driven temporal domain adaptation tr... | [
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272987314 | 2409.19146 | 2024-09-27 | Bound Tightening Network for Robust Crowd Counting | Crowd Counting is a fundamental topic, aiming to estimate the number of individuals in the crowded images or videos fed from surveillance cameras. Recent works focus on improving counting accuracy, while ignoring the certified robustness of counting models. In this paper, we propose a novel Bound Tightening Network (BT... | [
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{
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272987119 | 2409.19092 | 2024-09-27 | Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups | We study the problems of differentially private federated online prediction from experts against both stochastic adversaries and oblivious adversaries. We aim to minimize the average regret on $m$ clients working in parallel over time horizon $T$ with explicit differential privacy (DP) guarantees. With stochastic adver... | [
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"Federated learning",
"Privacy and security in data-centric ML",
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272987022 | 2409.19171 | 2024-09-27 | Reducing Overtreatment of Indeterminate Thyroid Nodules Using a Multimodal Deep Learning Model | Objective: Molecular testing (MT) classifies cytologically indeterminate thyroid nodules as benign or malignant with high sensitivity but low positive predictive value (PPV), only using molecular profiles, ignoring ultrasound (US) imaging and biopsy. We address this limitation by applying attention multiple instance le... | [
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272968806 | 2409.18448 | 2024-09-27 | Hierarchical Federated Learning with Multi-Timescale Gradient Correction | While traditional federated learning (FL) typically focuses on a star topology where clients are directly connected to a central server, real-world distributed systems often exhibit hierarchical architectures. Hierarchical FL (HFL) has emerged as a promising solution to bridge this gap, leveraging aggregation points at... | [
"cs.LG"
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"Federated learning",
"Deep learning theory (training dynamics, generalization, optimization convergence)"
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