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272986613 | 2409.19218 | 2024-09-28 | A Characterization of List Regression | There has been a recent interest in understanding and characterizing the sample complexity of list learning tasks, where the learning algorithm is allowed to make a short list of $k$ predictions, and we simply require one of the predictions to be correct. This includes recent works characterizing the PAC sample complex... | [
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272986936 | 2409.19359 | 2024-09-28 | Quantum delegated and federated learning via quantum homomorphic encryption | Quantum learning models hold the potential to bring computational advantages over the classical realm. As powerful quantum servers become available on the cloud, ensuring the protection of clients' private data becomes crucial. By incorporating quantum homomorphic encryption schemes, we present a general framework that... | [
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272987870 | 2409.19429 | 2024-09-28 | Fast Encoding and Decoding for Implicit Video Representation | Despite the abundant availability and content richness for video data, its high-dimensionality poses challenges for video research. Recent advancements have explored the implicit representation for videos using neural networks, demonstrating strong performance in applications such as video compression and enhancement. ... | [
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272987034 | 2409.19403 | 2024-09-28 | Restore Anything with Masks: Leveraging Mask Image Modeling for Blind All-in-One Image Restoration | All-in-one image restoration aims to handle multiple degradation types using one model. This paper proposes a simple pipeline for all-in-one blind image restoration to Restore Anything with Masks (RAM). We focus on the image content by utilizing Mask Image Modeling to extract intrinsic image information rather than dis... | [
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272986930 | 2409.19239 | 2024-09-28 | Zorro: A Flexible and Differentiable Parametric Family of Activation Functions That Extends ReLU and GELU | Even in recent neural network architectures such as Transformers and Extended LSTM (xLSTM), and traditional ones like Convolutional Neural Networks, Activation Functions are an integral part of nearly all neural networks. They enable more effective training and capture nonlinear data patterns. More than 400 functions h... | [
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272987727 | 2409.19483 | 2024-09-28 | MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation | Segmentation of anatomical structures and pathological regions in medical images is essential for modern clinical diagnosis, disease research, and treatment planning. While significant advancements have been made in deep learning-based segmentation techniques, many of these methods still suffer from limitations in data... | [
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272986851 | 2409.19305 | 2024-09-28 | EEPNet: Efficient Edge Pixel-based Matching Network for Cross-Modal Dynamic Registration between LiDAR and Camera | Multisensor fusion is essential for autonomous vehicles to accurately perceive, analyze, and plan their trajectories within complex environments. This typically involves the integration of data from LiDAR sensors and cameras, which necessitates high-precision and real-time registration. Current methods for registering ... | [
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272987694 | 2409.19243 | 2024-09-28 | Jointly modelling the evolution of social structure and language in online communities | Group interactions take place within a particular socio-temporal context, which should be taken into account when modelling interactions in online communities. We propose a method for jointly modelling community structure and language over time. Our system produces dynamic word and user representations that can be used... | [
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272987676 | 2409.19435 | 2024-09-28 | Simulation-based inference with the Python Package sbijax | Neural simulation-based inference (SBI) describes an emerging family of methods for Bayesian inference with intractable likelihood functions that use neural networks as surrogate models. Here we introduce sbijax, a Python package that implements a wide variety of state-of-the-art methods in neural simulation-based infe... | [
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272986895 | 2409.19242 | 2024-09-28 | SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement | Automating the creation of scientific diagrams from academic papers can significantly streamline the development of tutorials, presentations, and posters, thereby saving time and accelerating the process. Current text-to-image models struggle with generating accurate and visually appealing diagrams from long-context in... | [
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272987379 | 2409.19430 | 2024-09-28 | 'Simulacrum of Stories': Examining Large Language Models as Qualitative Research Participants | The recent excitement around generative models has sparked a wave of proposals suggesting the replacement of human participation and labor in research and development--e.g., through surveys, experiments, and interviews--with synthetic research data generated by large language models (LLMs). We conducted interviews with... | [
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272987910 | 2409.19390 | 2024-09-28 | Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based model | The fifth-generation (5G) offers advanced services, supporting applications such as intelligent transportation, connected healthcare, and smart cities within the Internet of Things (IoT). However, these advancements introduce significant security challenges, with increasingly sophisticated cyber-attacks. This paper pro... | [
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272988068 | 2409.19458 | 2024-09-28 | Scalable Fine-tuning from Multiple Data Sources: A First-Order Approximation Approach | We study the problem of fine-tuning a language model (LM) for a target task by optimally using the information from $n$ auxiliary tasks. This problem has broad applications in NLP, such as targeted instruction tuning and data selection in chain-of-thought fine-tuning. The key challenge of this problem is that not all a... | [
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272987700 | 2409.19391 | 2024-09-28 | Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training | Deep Multi-agent Reinforcement Learning (MARL) relies on neural networks with numerous parameters in multi-agent scenarios, often incurring substantial computational overhead. Consequently, there is an urgent need to expedite training and enable model compression in MARL. This paper proposes the utilization of dynamic ... | [
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272987714 | 2409.19375 | 2024-09-28 | DOTA: Distributional Test-Time Adaptation of Vision-Language Models | Vision-language foundation models (VLMs), such as CLIP, exhibit remarkable performance across a wide range of tasks. However, deploying these models can be unreliable when significant distribution gaps exist between training and test data, while fine-tuning for diverse scenarios is often costly. Cache-based test-time a... | [
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272987526 | 2409.19417 | 2024-09-28 | Subject Data Auditing via Source Inference Attack in Cross-Silo Federated Learning | Source Inference Attack (SIA) in Federated Learning (FL) aims to identify which client used a target data point for local model training. It allows the central server to audit clients' data usage. In cross-silo FL, a client (silo) collects data from multiple subjects (e.g., individuals, writers, or devices), posing a r... | [
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272988128 | 2409.19291 | 2024-09-28 | CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling | Contrastive Language-Image Pre-training (CLIP) has become a cornerstone in multimodal intelligence. However, recent studies discovered that CLIP can only encode one aspect of the feature space, leading to substantial information loss and indistinctive features. To mitigate this issue, this paper introduces a novel stra... | [
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272987892 | 2409.19407 | 2024-09-28 | Brain-JEPA: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking | We introduce Brain-JEPA, a brain dynamics foundation model with the Joint-Embedding Predictive Architecture (JEPA). This pioneering model achieves state-of-the-art performance in demographic prediction, disease diagnosis/prognosis, and trait prediction through fine-tuning. Furthermore, it excels in off-the-shelf evalua... | [
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273351297 | 2410.10833 | 2024-09-29 | Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning | Federated learning (FL) enables edge devices to collaboratively train a machine learning model without sharing their raw data. Due to its privacy-protecting benefits, FL has been deployed in many real-world applications. However, deploying FL over mobile edge networks with constrained resources such as power, bandwidth... | [
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272987735 | 2409.19680 | 2024-09-29 | Instruction Embedding: Latent Representations of Instructions Towards Task Identification | Instruction data is crucial for improving the capability of Large Language Models (LLMs) to align with human-level performance. Recent research LIMA demonstrates that alignment is essentially a process where the model adapts instructions' interaction style or format to solve various tasks, leveraging pre-trained knowle... | [
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272986865 | 2409.19490 | 2024-09-29 | KineDepth: Utilizing Robot Kinematics for Online Metric Depth Estimation | Depth perception is essential for a robot's spatial and geometric understanding of its environment, with many tasks traditionally relying on hardware-based depth sensors like RGB-D or stereo cameras. However, these sensors face practical limitations, including issues with transparent and reflective objects, high costs,... | [
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272987304 | 2409.19806 | 2024-09-29 | PALM: Few-Shot Prompt Learning for Audio Language Models | Audio-Language Models (ALMs) have recently achieved remarkable success in zero-shot audio recognition tasks, which match features of audio waveforms with class-specific text prompt features, inspired by advancements in Vision-Language Models (VLMs). Given the sensitivity of zero-shot performance to the choice of hand-c... | [
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272987643 | 2409.19510 | 2024-09-29 | Making LLMs Better Many-to-Many Speech-to-Text Translators with Curriculum Learning | Multimodal Large Language Models (MLLMs) have achieved significant success in Speech-to-Text Translation (S2TT) tasks. While most existing research has focused on English-centric translation directions, the exploration of many-to-many translation is still limited by the scarcity of parallel data. To address this, we pr... | [
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272987452 | 2409.19569 | 2024-09-29 | Fully Aligned Network for Referring Image Segmentation | This paper focuses on the Referring Image Segmentation (RIS) task, which aims to segment objects from an image based on a given language description. The critical problem of RIS is achieving fine-grained alignment between different modalities to recognize and segment the target object. Recent advances using the attenti... | [
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272987518 | 2409.19727 | 2024-09-29 | Investigating the Effect of Network Pruning on Performance and Interpretability | Deep Neural Networks (DNNs) are often over-parameterized for their tasks and can be compressed quite drastically by removing weights, a process called pruning. We investigate the impact of different pruning techniques on the classification performance and interpretability of GoogLeNet. We systematically apply unstructu... | [
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272987179 | 2409.19777 | 2024-09-29 | Automatic debiasing of neural networks via moment-constrained learning | Causal and nonparametric estimands in economics and biostatistics can often be viewed as the mean of a linear functional applied to an unknown outcome regression function. Naively learning the regression function and taking a sample mean of the target functional results in biased estimators, and a rich debiasing litera... | [
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272987125 | 2409.19521 | 2024-09-29 | GenTel-Safe: A Unified Benchmark and Shielding Framework for Defending Against Prompt Injection Attacks | Large Language Models (LLMs) like GPT-4, LLaMA, and Qwen have demonstrated remarkable success across a wide range of applications. However, these models remain inherently vulnerable to prompt injection attacks, which can bypass existing safety mechanisms, highlighting the urgent need for more robust attack detection me... | [
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272988113 | 2409.19541 | 2024-09-29 | Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization | Language models frequently inherit societal biases from their training data. Numerous techniques have been proposed to mitigate these biases during both the pre-training and fine-tuning stages. However, fine-tuning a pre-trained debiased language model on a downstream task can reintroduce biases into the model. Additio... | [
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272987873 | 2409.19790 | 2024-09-29 | Analysis on Riemann Hypothesis with Cross Entropy Optimization and Reasoning | In this paper, we present a novel framework for the analysis of Riemann Hypothesis [27], which is composed of three key components: a) probabilistic modeling with cross entropy optimization and reasoning; b) the application of the law of large numbers; c) the application of mathematical inductions. The analysis is main... | [
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272987031 | 2409.19635 | 2024-09-29 | Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptation | Time-Series (TS) data has grown in importance with the rise of Internet of Things devices like sensors, but its labeling remains costly and complex. While Unsupervised Domain Adaptation (UDAs) offers an effective solution, growing data privacy concerns have led to the development of Source-Free UDA (SFUDAs), enabling m... | [
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272987107 | 2409.19650 | 2024-09-29 | Grounding 3D Scene Affordance From Egocentric Interactions | Grounding 3D scene affordance aims to locate interactive regions in 3D environments, which is crucial for embodied agents to interact intelligently with their surroundings. Most existing approaches achieve this by mapping semantics to 3D instances based on static geometric structure and visual appearance. This passive ... | [
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272987770 | 2409.19548 | 2024-09-29 | Meta Learning to Rank for Sparsely Supervised Queries | Supervisory signals are a critical resource for training learning to rank models. In many real-world search and retrieval scenarios, these signals may not be readily available or could be costly to obtain for some queries. The examples include domains where labeling requires professional expertise, applications with st... | [
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272987631 | 2409.19594 | 2024-09-29 | MASKDROID: Robust Android Malware Detection with Masked Graph Representations | Android malware attacks have posed a severe threat to mobile users, necessitating a significant demand for the automated detection system. Among the various tools employed in malware detection, graph representations (e.g., function call graphs) have played a pivotal role in characterizing the behaviors of Android apps.... | [
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272987938 | 2409.19801 | 2024-09-29 | CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells | The task of automated code review has recently gained a lot of attention from the machine learning community. However, current review comment evaluation metrics rely on comparisons with a human-written reference for a given code change (also called a diff). Furthermore, code review is a one-to-many problem, like genera... | [
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272987593 | 2409.19507 | 2024-09-29 | A Critical Look at Meta-evaluating Summarisation Evaluation Metrics | Effective summarisation evaluation metrics enable researchers and practitioners to compare different summarisation systems efficiently. Estimating the effectiveness of an automatic evaluation metric, termed meta-evaluation, is a critically important research question. In this position paper, we review recent meta-evalu... | [
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272987064 | 2409.19817 | 2024-09-29 | Calibrating Language Models with Adaptive Temperature Scaling | The effectiveness of large language models (LLMs) is not only measured by their ability to generate accurate outputs but also by their calibration-how well their confidence scores reflect the probability of their outputs being correct. While unsupervised pre-training has been shown to yield LLMs with well-calibrated co... | [
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272987450 | 2409.19798 | 2024-09-29 | Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data | We consider the problem of a training data proof, where a data creator or owner wants to demonstrate to a third party that some machine learning model was trained on their data. Training data proofs play a key role in recent lawsuits against foundation models trained on web-scale data. Many prior works suggest to insta... | [
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272987662 | 2409.19545 | 2024-09-29 | Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting | Labor market forecasting on talent demand and supply is essential for business management and economic development. With accurate and timely forecasts, employers can adapt their recruitment strategies to align with the evolving labor market, and employees can have proactive career path planning according to future dema... | [
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281841021 | 2409.19509 | 2024-09-29 | Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning | Federated Learning (FL) provides a privacy-preserving framework for training machine learning models on mobile edge devices. Traditional FL algorithms, e.g., FedAvg, impose a heavy communication workload on these devices. To mitigate this issue, Hierarchical Federated Edge Learning (HFEL) has been proposed, leveraging ... | [
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272987600 | 2409.19623 | 2024-09-29 | MCDDPM: Multichannel Conditional Denoising Diffusion Model for Unsupervised Anomaly Detection in Brain MRI | Detecting anomalies in brain MRI scans using supervised deep learning methods presents challenges due to anatomical diversity and labor-intensive requirement of pixel-level annotations. Generative models like Denoising Diffusion Probabilistic Model (DDPM) and their variants like pDDPM, mDDPM, cDDPM have recently emerge... | [
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272988100 | 2409.19759 | 2024-09-29 | Balancing Cost and Effectiveness of Synthetic Data Generation Strategies for LLMs | As large language models (LLMs) are applied to more use cases, creating high quality, task-specific datasets for fine-tuning becomes a bottleneck for model improvement. Using high quality human data has been the most common approach to unlock model performance, but is prohibitively expensive in many scenarios. Several ... | [
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272987867 | 2409.19764 | 2024-09-29 | Spiking Transformer with Spatial-Temporal Attention | Spike-based Transformer presents a compelling and energy-efficient alternative to traditional Artificial Neural Network (ANN)-based Transformers, achieving impressive results through sparse binary computations. However, existing spike-based transformers predominantly focus on spatial attention while neglecting crucial ... | [
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272987702 | 2409.19501 | 2024-09-29 | Learning Frame-Wise Emotion Intensity for Audio-Driven Talking-Head Generation | Human emotional expression is inherently dynamic, complex, and fluid, characterized by smooth transitions in intensity throughout verbal communication. However, the modeling of such intensity fluctuations has been largely overlooked by previous audio-driven talking-head generation methods, which often results in static... | [
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272987900 | 2409.19641 | 2024-09-29 | fCOP: Focal Length Estimation from Category-level Object Priors | In the realm of computer vision, the perception and reconstruction of the 3D world through vision signals heavily rely on camera intrinsic parameters, which have long been a subject of intense research within the community. In practical applications, without a strong scene geometry prior like the Manhattan World assump... | [
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278871688 | 2410.10836 | 2024-09-29 | Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction | Reconstructing 3D cone beam computed tomography (CBCT) images from a limited set of projections is an important inverse problem in many imaging applications from medicine to inertial confinement fusion (ICF). The performance of traditional methods such as filtered back projection (FBP) and model-based regularization is... | [
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272988159 | 2409.19754 | 2024-09-29 | Offline Signature Verification Based on Feature Disentangling Aided Variational Autoencoder | Offline handwritten signature verification systems are used to verify the identity of individuals, through recognizing their handwritten signature image as genuine signatures or forgeries. The main tasks of signature verification systems include extracting features from signature images and training a classifier for cl... | [
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272988067 | 2409.19600 | 2024-09-29 | An Unbiased Risk Estimator for Partial Label Learning with Augmented Classes | Partial Label Learning (PLL) is a typical weakly supervised learning task, which assumes each training instance is annotated with a set of candidate labels containing the ground-truth label. Recent PLL methods adopt identification-based disambiguation to alleviate the influence of false positive labels and achieve prom... | [
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272987343 | 2409.19647 | 2024-09-29 | Fine-Tuning Hybrid Physics-Informed Neural Networks for Vehicle Dynamics Model Estimation | Accurate dynamic modeling is critical for autonomous racing vehicles, especially during high-speed and agile maneuvers where precise motion prediction is essential for safety. Traditional parameter estimation methods face limitations such as reliance on initial guesses, labor-intensive fitting procedures, and complex t... | [
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272988077 | 2409.19518 | 2024-09-29 | KODA: A Data-Driven Recursive Model for Time Series Forecasting and Data Assimilation using Koopman Operators | Approaches based on Koopman operators have shown great promise in forecasting time series data generated by complex nonlinear dynamical systems (NLDS). Although such approaches are able to capture the latent state representation of a NLDS, they still face difficulty in long term forecasting when applied to real world d... | [
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272987216 | 2409.19702 | 2024-09-29 | RNG: Relightable Neural Gaussians | 3D Gaussian Splatting (3DGS) has shown impressive results for the novel view synthesis task, where lighting is assumed to be fixed. However, creating relightable 3D assets, especially for objects with ill-defined shapes (fur, fabric, etc.), remains a challenging task. The decomposition between light, geometry, and mate... | [
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272986689 | 2409.19823 | 2024-09-29 | OrganiQ: Mitigating Classical Resource Bottlenecks of Quantum Generative Adversarial Networks on NISQ-Era Machines | Driven by swift progress in hardware capabilities, quantum machine learning has emerged as a research area of interest. Recently, quantum image generation has produced promising results. However, prior quantum image generation techniques rely on classical neural networks, limiting their quantum potential and image qual... | [
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272988055 | 2409.19605 | 2024-09-29 | The Crucial Role of Samplers in Online Direct Preference Optimization | Direct Preference Optimization (DPO) has emerged as a stable, scalable, and efficient solution for language model alignment. Despite its empirical success, the optimization properties, particularly the impact of samplers on its convergence rates, remain under-explored. In this paper, we provide a rigorous analysis of D... | [
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272987150 | 2409.19540 | 2024-09-29 | LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models | The widespread adoption of large-scale pre-training techniques has significantly advanced the development of medical foundation models, enabling them to serve as versatile tools across a broad range of medical tasks. However, despite their strong generalization capabilities, medical foundation models pre-trained on lar... | [
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272987852 | 2409.19610 | 2024-09-29 | Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method | Integrating pretrained vision-language foundation models like CLIP into federated learning has attracted significant attention for enhancing generalization across diverse tasks. Typically, federated learning of vision-language models employs prompt learning to reduce communication and computational costs, i.e., prompt-... | [
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272987111 | 2409.19572 | 2024-09-29 | Mitigating the Negative Impact of Over-association for Conversational Query Production | Conversational query generation aims at producing search queries from dialogue histories, which are then used to retrieve relevant knowledge from a search engine to help knowledge-based dialogue systems. Trained to maximize the likelihood of gold queries, previous models suffer from the data hunger issue, and they tend... | [
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272987661 | 2409.19611 | 2024-09-29 | Learning Attentional Mixture of LoRAs for Language Model Continual Learning | Fine-tuning large language models (LLMs) with Low-Rank adaption (LoRA) is widely acknowledged as an effective approach for continual learning for new tasks. However, it often suffers from catastrophic forgetting when dealing with multiple tasks sequentially. To this end, we propose Attentional Mixture of LoRAs (AM-LoRA... | [
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272987153 | 2409.19638 | 2024-09-29 | BadHMP: Backdoor Attack against Human Motion Prediction | Precise future human motion prediction over sub-second horizons from past observations is crucial for various safety-critical applications. To date, only a few studies have examined the vulnerability of skeleton-based neural networks to evasion and backdoor attacks. In this paper, we propose BadHMP, a novel backdoor at... | [
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272987443 | 2409.19788 | 2024-09-29 | Exploring Adversarial Robustness in Classification tasks using DNA Language Models | DNA Language Models, such as GROVER, DNABERT2 and the Nucleotide Transformer, operate on DNA sequences that inherently contain sequencing errors, mutations, and laboratory-induced noise, which may significantly impact model performance. Despite the importance of this issue, the robustness of DNA language models remains... | [
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272986783 | 2409.19723 | 2024-09-29 | Revealing Personality Traits: A New Benchmark Dataset for Explainable Personality Recognition on Dialogues | Personality recognition aims to identify the personality traits implied in user data such as dialogues and social media posts. Current research predominantly treats personality recognition as a classification task, failing to reveal the supporting evidence for the recognized personality. In this paper, we propose a nov... | [
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272987156 | 2409.19508 | 2024-09-29 | Transforming Scholarly Landscapes: Influence of Large Language Models on Academic Fields beyond Computer Science | Large Language Models (LLMs) have ushered in a transformative era in Natural Language Processing (NLP), reshaping research and extending NLP's influence to other fields of study. However, there is little to no work examining the degree to which LLMs influence other research fields. This work empirically and systematica... | [
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272986836 | 2409.19526 | 2024-09-29 | Efficient Backdoor Defense in Multimodal Contrastive Learning: A Token-Level Unlearning Method for Mitigating Threats | Multimodal contrastive learning uses various data modalities to create high-quality features, but its reliance on extensive data sources on the Internet makes it vulnerable to backdoor attacks. These attacks insert malicious behaviors during training, which are activated by specific triggers during inference, posing si... | [
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272987346 | 2409.19494 | 2024-09-29 | OptiGrasp: Optimized Grasp Pose Detection Using RGB Images for Warehouse Picking Robots | In warehouse environments, robots require robust picking capabilities to manage a wide variety of objects. Effective deployment demands minimal hardware, strong generalization to new products, and resilience in diverse settings. Current methods often rely on depth sensors for structural information, which suffer from h... | [
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272986611 | 2409.19656 | 2024-09-29 | Multimodal Misinformation Detection by Learning from Synthetic Data with Multimodal LLMs | Detecting multimodal misinformation, especially in the form of image-text pairs, is crucial. Obtaining large-scale, high-quality real-world fact-checking datasets for training detectors is costly, leading researchers to use synthetic datasets generated by AI technologies. However, the generalizability of detectors trai... | [
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272987595 | 2409.19561 | 2024-09-29 | Unifying back-propagation and forward-forward algorithms through model predictive control | We introduce a Model Predictive Control (MPC) framework for training deep neural networks, systematically unifying the Back-Propagation (BP) and Forward-Forward (FF) algorithms. At the same time, it gives rise to a range of intermediate training algorithms with varying look-forward horizons, leading to a performance-ef... | [
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272987026 | 2409.19581 | 2024-09-29 | DiMB-RE: Mining the Scientific Literature for Diet-Microbiome Associations | Objective: To develop a corpus annotated for diet-microbiome associations from the biomedical literature and train natural language processing (NLP) models to identify these associations, thereby improving the understanding of their role in health and disease, and supporting personalized nutrition strategies. Materials... | [
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272987258 | 2409.19552 | 2024-09-29 | OmniXAS: A Universal Deep-Learning Framework for Materials X-ray Absorption Spectra | X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents significant challenges, often requiring extensive, computationally intensive simulations, as well as significant domain expertise. These limita... | [
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272987820 | 2409.19735 | 2024-09-29 | Scrambled text: training Language Models to correct OCR errors using synthetic data | OCR errors are common in digitised historical archives significantly affecting their usability and value. Generative Language Models (LMs) have shown potential for correcting these errors using the context provided by the corrupted text and the broader socio-cultural context, a process called Context Leveraging OCR Cor... | [
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272987548 | 2409.19713 | 2024-09-29 | Generating peak-aware pseudo-measurements for low-voltage feeders using metadata of distribution system operators | Distribution system operators (DSOs) must cope with new challenges such as the reconstruction of distribution grids along climate neutrality pathways or the ability to manage and control consumption and generation in the grid. In order to meet the challenges, measurements within the distribution grid often form the bas... | [
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272987288 | 2409.19563 | 2024-09-29 | CLIP-based Camera-Agnostic Feature Learning for Intra-camera Person Re-Identification | Contrastive Language-Image Pre-Training (CLIP) model excels in traditional person re-identification (ReID) tasks due to its inherent advantage in generating textual descriptions for pedestrian images. However, applying CLIP directly to intra-camera supervised person re-identification (ICS ReID) presents challenges. ICS... | [
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272987869 | 2409.19580 | 2024-09-29 | High Quality Human Image Animation using Regional Supervision and Motion Blur Condition | Recent advances in video diffusion models have enabled realistic and controllable human image animation with temporal coherence. Although generating reasonable results, existing methods often overlook the need for regional supervision in crucial areas such as the face and hands, and neglect the explicit modeling for mo... | [
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272986926 | 2409.19505 | 2024-09-29 | The Nature of NLP: Analyzing Contributions in NLP Papers | Natural Language Processing (NLP) is an established and dynamic field. Despite this, what constitutes NLP research remains debated. In this work, we address the question by quantitatively examining NLP research papers. We propose a taxonomy of research contributions and introduce NLPContributions, a dataset of nearly $... | [
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272987080 | 2409.19624 | 2024-09-29 | Storynizor: Consistent Story Generation via Inter-Frame Synchronized and Shuffled ID Injection | Recent advances in text-to-image diffusion models have spurred significant interest in continuous story image generation. In this paper, we introduce Storynizor, a model capable of generating coherent stories with strong inter-frame character consistency, effective foreground-background separation, and diverse pose var... | [
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272986661 | 2409.19753 | 2024-09-29 | CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge Graph Question Answering | Recent studies have explored the use of Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) for Knowledge Graph Question Answering (KGQA). They typically require rewriting retrieved subgraphs into natural language formats comprehensible to LLMs. However, when tackling complex questions, the knowledge... | [
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272987679 | 2409.19684 | 2024-09-29 | MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generation | Medicine is inherently multimodal and multitask, with diverse data modalities spanning text, imaging. However, most models in medical field are unimodal single tasks and lack good generalizability and explainability. In this study, we introduce MedViLaM, a unified vision-language model towards a generalist model for me... | [
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272987882 | 2409.19592 | 2024-09-29 | DiffCP: Ultra-Low Bit Collaborative Perception via Diffusion Model | Collaborative perception (CP) is emerging as a promising solution to the inherent limitations of stand-alone intelligence. However, current wireless communication systems are unable to support feature-level and raw-level collaborative algorithms due to their enormous bandwidth demands. In this paper, we propose DiffCP,... | [
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273022618 | 2410.00059 | 2024-09-29 | IDEA: An Inverse Domain Expert Adaptation Based Active DNN IP Protection Method | Illegitimate reproduction, distribution and derivation of Deep Neural Network (DNN) models can inflict economic loss, reputation damage and even privacy infringement. Passive DNN intellectual property (IP) protection methods such as watermarking and fingerprinting attempt to prove the ownership upon IP violation, but t... | [
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272987023 | 2409.19804 | 2024-09-29 | Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems | Retrieval-Augmented Generation (RAG) has recently gained significant attention for its enhanced ability to integrate external knowledge sources into open-domain question answering (QA) tasks. However, it remains unclear how these models address fairness concerns, particularly with respect to sensitive attributes such a... | [
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272988102 | 2409.19663 | 2024-09-29 | Identifying Knowledge Editing Types in Large Language Models | Knowledge editing has emerged as an efficient technique for updating the knowledge of large language models (LLMs), attracting increasing attention in recent years. However, there is a lack of effective measures to prevent the malicious misuse of this technique, which could lead to harmful edits in LLMs. These maliciou... | [
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