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272911472 | 2409.17536 | 2024-09-26 | MUSE: Integrating Multi-Knowledge for Knowledge Graph Completion | Knowledge Graph Completion (KGC) aims to predict the missing [relation] part of (head entity)--[relation]->(tail entity) triplet. Most existing KGC methods focus on single features (e.g., relation types) or sub-graph aggregation. However, they do not fully explore the Knowledge Graph (KG) features and neglect the guida... | [
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272911210 | 2409.17602 | 2024-09-26 | Open Digital Rights Enforcement Framework (ODRE): from descriptive to enforceable policies | From centralised platforms to decentralised ecosystems, like Data Spaces, sharing data has become a paramount challenge. For this reason, the definition of data usage policies has become crucial in these domains, highlighting the necessity of effective policy enforcement mechanisms. The Open Digital Rights Language (OD... | [
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272910936 | 2409.17597 | 2024-09-26 | Unifying Dimensions: A Linear Adaptive Approach to Lightweight Image Super-Resolution | Window-based transformers have demonstrated outstanding performance in super-resolution tasks due to their adaptive modeling capabilities through local self-attention (SA). However, they exhibit higher computational complexity and inference latency than convolutional neural networks. In this paper, we first identify th... | [
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272911137 | 2409.17898 | 2024-09-26 | MC-SEMamba: A Simple Multi-channel Extension of SEMamba | Transformer-based models have become increasingly popular and have impacted speech-processing research owing to their exceptional performance in sequence modeling. Recently, a promising model architecture, Mamba, has emerged as a potential alternative to transformer-based models because of its efficient modeling of lon... | [
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272910865 | 2409.17833 | 2024-09-26 | Ordinary Differential Equations for Enhanced 12-Lead ECG Generation | In the realm of artificial intelligence, the generation of realistic training data for supervised learning tasks presents a significant challenge. This is particularly true in the synthesis of electrocardiograms (ECGs), where the objective is to develop a synthetic 12-lead ECG model. The primary complexity of this task... | [
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272910991 | 2409.17634 | 2024-09-26 | P4Q: Learning to Prompt for Quantization in Visual-language Models | Large-scale pre-trained Vision-Language Models (VLMs) have gained prominence in various visual and multimodal tasks, yet the deployment of VLMs on downstream application platforms remains challenging due to their prohibitive requirements of training samples and computing resources. Fine-tuning and quantization of VLMs ... | [
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272911267 | 2409.18000 | 2024-09-26 | Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel | Ensuring safety is a key aspect in sequential decision making problems, such as robotics or process control. The complexity of the underlying systems often makes finding the optimal decision challenging, especially when the safety-critical system is time-varying. Overcoming the problem of optimizing an unknown time-var... | [
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272911115 | 2409.17988 | 2024-09-26 | Deblur e-NeRF: NeRF from Motion-Blurred Events under High-speed or Low-light Conditions | The stark contrast in the design philosophy of an event camera makes it particularly ideal for operating under high-speed, high dynamic range and low-light conditions, where standard cameras underperform. Nonetheless, event cameras still suffer from some amount of motion blur, especially under these challenging conditi... | [
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273323373 | 2410.08224 | 2024-09-26 | A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications | In recent years, the field of electroencephalography (EEG) analysis has witnessed remarkable advancements, driven by the integration of machine learning and artificial intelligence. This survey aims to encapsulate the latest developments, focusing on emerging methods and technologies that are poised to transform our co... | [
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272910983 | 2409.18006 | 2024-09-26 | Evaluating Multilingual Long-Context Models for Retrieval and Reasoning | Recent large language models (LLMs) demonstrate impressive capabilities in handling long contexts, some exhibiting near-perfect recall on synthetic retrieval tasks. However, these evaluations have mainly focused on English text and involved a single target sentence within lengthy contexts. Our work investigates how LLM... | [
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272911265 | 2409.17729 | 2024-09-26 | Neural Implicit Representation for Highly Dynamic LiDAR Mapping and Odometry | Recent advancements in Simultaneous Localization and Mapping (SLAM) have increasingly highlighted the robustness of LiDAR-based techniques. At the same time, Neural Radiance Fields (NeRF) have introduced new possibilities for 3D scene reconstruction, exemplified by SLAM systems. Among these, NeRF-LOAM has shown notable... | [
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272910692 | 2409.17642 | 2024-09-26 | AI Delegates with a Dual Focus: Ensuring Privacy and Strategic Self-Disclosure | Large language model (LLM)-based AI delegates are increasingly utilized to act on behalf of users, assisting them with a wide range of tasks through conversational interfaces. Despite their advantages, concerns arise regarding the potential risk of privacy leaks, particularly in scenarios involving social interactions.... | [
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272911279 | 2409.17726 | 2024-09-26 | Recent advances in interpretable machine learning using structure-based protein representations | Recent advancements in machine learning (ML) are transforming the field of structural biology. For example, AlphaFold, a groundbreaking neural network for protein structure prediction, has been widely adopted by researchers. The availability of easy-to-use interfaces and interpretable outcomes from the neural network a... | [
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272910558 | 2409.17834 | 2024-09-26 | PEDRO: Parameter-Efficient Fine-tuning with Prompt DEpenDent Representation MOdification | Due to their substantial sizes, large language models (LLMs) are typically deployed within a single-backbone multi-tenant framework. In this setup, a single instance of an LLM backbone must cater to multiple users or tasks through the application of various parameter-efficient fine-tuning (PEFT) models. Despite the ava... | [
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272910888 | 2409.17557 | 2024-09-26 | Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs | Semantic- and task-oriented communication has emerged as a promising approach to reducing the latency and bandwidth requirements of next-generation mobile networks by transmitting only the most relevant information needed to complete a specific task at the receiver. This is particularly advantageous for machine-oriente... | [
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272911383 | 2409.17886 | 2024-09-26 | Upper-Body Pose-based Gaze Estimation for Privacy-Preserving 3D Gaze Target Detection | Gaze Target Detection (GTD), i.e., determining where a person is looking within a scene from an external viewpoint, is a challenging task, particularly in 3D space. Existing approaches heavily rely on analyzing the person's appearance, primarily focusing on their face to predict the gaze target. This paper presents a n... | [
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272910912 | 2409.17774 | 2024-09-26 | Faithfulness and the Notion of Adversarial Sensitivity in NLP Explanations | Faithfulness is arguably the most critical metric to assess the reliability of explainable AI. In NLP, current methods for faithfulness evaluation are fraught with discrepancies and biases, often failing to capture the true reasoning of models. We introduce Adversarial Sensitivity as a novel approach to faithfulness ev... | [
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272910569 | 2409.17539 | 2024-09-26 | Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models | Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks but their performance in complex logical reasoning tasks remains unsatisfactory. Although some prompting methods, such as Chain-of-Thought, can improve the reasoning ability of LLMs to some extent, they suffer from an unfaithful... | [
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272910929 | 2409.18057 | 2024-09-26 | LightAvatar: Efficient Head Avatar as Dynamic Neural Light Field | Recent works have shown that neural radiance fields (NeRFs) on top of parametric models have reached SOTA quality to build photorealistic head avatars from a monocular video. However, one major limitation of the NeRF-based avatars is the slow rendering speed due to the dense point sampling of NeRF, preventing them from... | [
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273233425 | 2410.07211 | 2024-09-26 | Neural Contrast: Leveraging Generative Editing for Graphic Design Recommendations | Creating visually appealing composites requires optimizing both text and background for compatibility. Previous methods have focused on simple design strategies, such as changing text color or adding background shapes for contrast. These approaches are often destructive, altering text color or partially obstructing the... | [
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272910926 | 2409.17598 | 2024-09-26 | Freeze and Learn: Continual Learning with Selective Freezing for Speech Deepfake Detection | In speech deepfake detection, one of the critical aspects is developing detectors able to generalize on unseen data and distinguish fake signals across different datasets. Common approaches to this challenge involve incorporating diverse data into the training process or fine-tuning models on unseen datasets. However, ... | [
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272910765 | 2409.17747 | 2024-09-26 | Text Image Generation for Low-Resource Languages with Dual Translation Learning | Scene text recognition in low-resource languages frequently faces challenges due to the limited availability of training datasets derived from real-world scenes. This study proposes a novel approach that generates text images in low-resource languages by emulating the style of real text images from high-resource langua... | [
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272969445 | 2409.18201 | 2024-09-26 | Loop-Diffusion: an equivariant diffusion model for designing and scoring protein loops | Predicting protein functional characteristics from structure remains a central problem in protein science, with broad implications from understanding the mechanisms of disease to designing novel therapeutics. Unfortunately, current machine learning methods are limited by scarce and biased experimental data, and physics... | [
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272910958 | 2409.17929 | 2024-09-26 | The Lou Dataset -- Exploring the Impact of Gender-Fair Language in German Text Classification | Gender-fair language, an evolving German linguistic variation, fosters inclusion by addressing all genders or using neutral forms. Nevertheless, there is a significant lack of resources to assess the impact of this linguistic shift on classification using language models (LMs), which are probably not trained on such va... | [
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272969023 | 2409.18269 | 2024-09-26 | Intrinsic Robustness of Prophet Inequality to Strategic Reward Signaling | Prophet inequality concerns a basic optimal stopping problem and states that simple threshold stopping policies -- i.e., accepting the first reward larger than a certain threshold -- can achieve tight $\frac{1}{2}$-approximation to the optimal prophet value. Motivated by its economic applications, this paper studies th... | [
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272911141 | 2409.17622 | 2024-09-26 | Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs | Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry. However, they encounter limitations in effectively capturing long-range interactions in large molecular systems. To address this challenge, we introduce Neural P$^3$M, a versatile enhancer of geometric GNNs to expand ... | [
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272969279 | 2409.18290 | 2024-09-26 | Retrospective Comparative Analysis of Prostate Cancer In-Basket Messages: Responses from Closed-Domain LLM vs. Clinical Teams | In-basket message interactions play a crucial role in physician-patient communication, occurring during all phases (pre-, during, and post) of a patient's care journey. However, responding to these patients' inquiries has become a significant burden on healthcare workflows, consuming considerable time for clinical care... | [
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272910649 | 2409.17545 | 2024-09-26 | Modulated Intervention Preference Optimization (MIPO): Keep the Easy, Refine the Difficult | Preference optimization methods typically begin training with a well-trained SFT model as a reference model. In RLHF and DPO, a regularization term is used during the preference optimization process to prevent the policy model from deviating too far from the reference model's distribution, thereby avoiding the generati... | [
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272969203 | 2409.18300 | 2024-09-26 | SOAR: Self-supervision Optimized UAV Action Recognition with Efficient Object-Aware Pretraining | We introduce SOAR, a novel Self-supervised pretraining algorithm for aerial footage captured by Unmanned Aerial Vehicles (UAVs). We incorporate human object knowledge throughout the pretraining process to enhance UAV video pretraining efficiency and downstream action recognition performance. This is in contrast to prio... | [
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272911379 | 2409.18017 | 2024-09-26 | Transferring disentangled representations: bridging the gap between synthetic and real images | Developing meaningful and efficient representations that separate the fundamental structure of the data generation mechanism is crucial in representation learning. However, Disentangled Representation Learning has not fully shown its potential on real images, because of correlated generative factors, their resolution a... | [
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272910917 | 2409.17963 | 2024-09-26 | CNCA: Toward Customizable and Natural Generation of Adversarial Camouflage for Vehicle Detectors | Prior works on physical adversarial camouflage against vehicle detectors mainly focus on the effectiveness and robustness of the attack. The current most successful methods optimize 3D vehicle texture at a pixel level. However, this results in conspicuous and attention-grabbing patterns in the generated camouflage, whi... | [
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272969466 | 2409.18289 | 2024-09-26 | Criticality and Safety Margins for Reinforcement Learning | State of the art reinforcement learning methods sometimes encounter unsafe situations. Identifying when these situations occur is of interest both for post-hoc analysis and during deployment, where it might be advantageous to call out to a human overseer for help. Efforts to gauge the criticality of different points in... | [
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272910814 | 2409.17671 | 2024-09-26 | Leveraging Anthropometric Measurements to Improve Human Mesh Estimation and Ensure Consistent Body Shapes | The basic body shape (i.e., the body shape in T-pose) of a person does not change within a single video. However, most SOTA human mesh estimation (HME) models output a slightly different, thus inconsistent basic body shape for each video frame. Furthermore, we find that SOTA 3D human pose estimation (HPE) models outper... | [
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272969109 | 2409.18199 | 2024-09-26 | LangSAMP: Language-Script Aware Multilingual Pretraining | Recent multilingual pretrained language models (mPLMs) often avoid using language embeddings -- learnable vectors assigned to individual languages. However, this places a significant burden on token representations to encode all language-specific information, which may hinder language neutrality. To address this limita... | [
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272910872 | 2409.17912 | 2024-09-26 | Atlas-Chat: Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect | We introduce Atlas-Chat, the first-ever collection of LLMs specifically developed for dialectal Arabic. Focusing on Moroccan Arabic, also known as Darija, we construct our instruction dataset by consolidating existing Darija language resources, creating novel datasets both manually and synthetically, and translating En... | [
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272910582 | 2409.18129 | 2024-09-26 | TOI-5005 b: A super-Neptune in the savanna near the ridge | The Neptunian desert and savanna have recently been found to be separated by a ridge, an overdensity of planets in the period range of $\simeq$3-5 days. These features are thought to be shaped by dynamical and atmospheric processes, but their roles are not yet well understood. Our aim was to confirm and characterize th... | [
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272910875 | 2409.17512 | 2024-09-26 | SCOMatch: Alleviating Overtrusting in Open-set Semi-supervised Learning | Open-set semi-supervised learning (OSSL) leverages practical open-set unlabeled data, comprising both in-distribution (ID) samples from seen classes and out-of-distribution (OOD) samples from unseen classes, for semi-supervised learning (SSL). Prior OSSL methods initially learned the decision boundary between ID and OO... | [
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272910534 | 2409.17990 | 2024-09-26 | Extracting Affect Aggregates from Longitudinal Social Media Data with Temporal Adapters for Large Language Models | This paper proposes temporally aligned Large Language Models (LLMs) as a tool for longitudinal analysis of social media data. We fine-tune Temporal Adapters for Llama 3 8B on full timelines from a panel of British Twitter users, and extract longitudinal aggregates of emotions and attitudes with established questionnair... | [
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272969039 | 2409.18297 | 2024-09-26 | Flat'n'Fold: A Diverse Multi-Modal Dataset for Garment Perception and Manipulation | We present Flat'n'Fold, a novel large-scale dataset for garment manipulation that addresses critical gaps in existing datasets. Comprising 1,212 human and 887 robot demonstrations of flattening and folding 44 unique garments across 8 categories, Flat'n'Fold surpasses prior datasets in size, scope, and diversity. Our da... | [
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272911180 | 2409.17503 | 2024-09-26 | Shape-intensity knowledge distillation for robust medical image segmentation | Many medical image segmentation methods have achieved impressive results. Yet, most existing methods do not take into account the shape-intensity prior information. This may lead to implausible segmentation results, in particular for images of unseen datasets. In this paper, we propose a novel approach to incorporate j... | [
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276647992 | 2409.17993 | 2024-09-26 | SSHNet: Unsupervised Cross-modal Homography Estimation via Problem Reformulation and Split Optimization | We propose a novel unsupervised cross-modal homography estimation learning framework, named Split Supervised Homography estimation Network (SSHNet). SSHNet reformulates the unsupervised cross-modal homography estimation into two supervised sub-problems, each addressed by its specialized network: a homography estimation... | [
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272910757 | 2409.18084 | 2024-09-26 | GSON: A Group-based Social Navigation Framework with Large Multimodal Model | With the increasing presence of service robots and autonomous vehicles in human environments, navigation systems need to evolve beyond simple destination reach to incorporate social awareness. This paper introduces GSON, a novel group-based social navigation framework that leverages Large Multimodal Models (LMMs) to en... | [
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272910547 | 2409.17987 | 2024-09-26 | LLM4Brain: Training a Large Language Model for Brain Video Understanding | Decoding visual-semantic information from brain signals, such as functional MRI (fMRI), across different subjects poses significant challenges, including low signal-to-noise ratio, limited data availability, and cross-subject variability. Recent advancements in large language models (LLMs) show remarkable effectiveness... | [
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272911097 | 2409.17525 | 2024-09-26 | When A Man Says He Is Pregnant: ERP Evidence for A Rational Account of Speaker-contextualized Language Comprehension | Spoken language is often, if not always, understood in a context formed by the identity of the speaker. For example, we can easily make sense of an utterance such as "I'm going to have a manicure this weekend" or "The first time I got pregnant I had a hard time" when spoken by a woman, but it would be harder to underst... | [
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272910963 | 2409.17587 | 2024-09-26 | Multimodal Banking Dataset: Understanding Client Needs through Event Sequences | Financial organizations collect a huge amount of temporal (sequential) data about clients, which is typically collected from multiple sources (modalities). Despite the urgent practical need, developing deep learning techniques suitable to handle such data is limited by the absence of large open-source multi-source real... | [
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272911462 | 2409.17568 | 2024-09-26 | Showing Many Labels in Multi-label Classification Models: An Empirical Study of Adversarial Examples | With the rapid development of Deep Neural Networks (DNNs), they have been applied in numerous fields. However, research indicates that DNNs are susceptible to adversarial examples, and this is equally true in the multi-label domain. To further investigate multi-label adversarial examples, we introduce a novel type of a... | [
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272910775 | 2409.17440 | 2024-09-26 | A Time Series is Worth Five Experts: Heterogeneous Mixture of Experts for Traffic Flow Prediction | Accurate traffic prediction faces significant challenges, necessitating a deep understanding of both temporal and spatial cues and their complex interactions across multiple variables. Recent advancements in traffic prediction systems are primarily due to the development of complex sequence-centric models. However, exi... | [
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272910944 | 2409.17946 | 2024-09-26 | Breaking PEFT Limitations: Leveraging Weak-to-Strong Knowledge Transfer for Backdoor Attacks in LLMs | Despite being widely applied due to their exceptional capabilities, Large Language Models (LLMs) have been proven to be vulnerable to backdoor attacks. These attacks introduce targeted vulnerabilities into LLMs by poisoning training samples and full-parameter fine-tuning (FPFT). However, this kind of backdoor attack is... | [
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272969487 | 2409.18330 | 2024-09-26 | DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors | Learning from previously collected data via behavioral cloning or offline reinforcement learning (RL) is a powerful recipe for scaling generalist agents by avoiding the need for expensive online learning. Despite strong generalization in some respects, agents are often remarkably brittle to minor visual variations in c... | [
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272910827 | 2409.17932 | 2024-09-26 | Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses | The sample compression theory provides generalization guarantees for predictors that can be fully defined using a subset of the training dataset and a (short) message string, generally defined as a binary sequence. Previous works provided generalization bounds for the zero-one loss, which is restrictive notably when ap... | [
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272910909 | 2409.17951 | 2024-09-26 | Spatial Hierarchy and Temporal Attention Guided Cross Masking for Self-supervised Skeleton-based Action Recognition | In self-supervised skeleton-based action recognition, the mask reconstruction paradigm is gaining interest in enhancing model refinement and robustness through effective masking. However, previous works primarily relied on a single masking criterion, resulting in the model overfitting specific features and overlooking ... | [
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272968894 | 2409.18211 | 2024-09-26 | Evaluation of Security of ML-based Watermarking: Copy and Removal Attacks | The vast amounts of digital content captured from the real world or AI-generated media necessitate methods for copyright protection, traceability, or data provenance verification. Digital watermarking serves as a crucial approach to address these challenges. Its evolution spans three generations: handcrafted, autoencod... | [
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271793906 | 2409.17958 | 2024-09-26 | The Hard Positive Truth about Vision-Language Compositionality | Several benchmarks have concluded that our best vision-language models (e.g., CLIP) are lacking in compositionality. Given an image, these benchmarks probe a model's ability to identify its associated caption amongst a set of compositional distractors. In response, a surge of recent proposals show improvements by finet... | [
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272969422 | 2409.18295 | 2024-09-26 | Enhancing Lossy Compression Through Cross-Field Information for Scientific Applications | Lossy compression is one of the most effective methods for reducing the size of scientific data containing multiple data fields. It reduces information density through prediction or transformation techniques to compress the data. Previous approaches use local information from a single target field when predicting targe... | [
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272911317 | 2409.18110 | 2024-09-26 | Open-World Evaluation for Retrieving Diverse Perspectives | We study retrieving a set of documents that covers various perspectives on a complex and contentious question (e.g., will ChatGPT do more harm than good?). We curate a Benchmark for Retrieval Diversity for Subjective questions (BERDS), where each example consists of a question and diverse perspectives associated with t... | [
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272910933 | 2409.17819 | 2024-09-26 | Inference-Time Language Model Alignment via Integrated Value Guidance | Large language models are typically fine-tuned to align with human preferences, but tuning large models is computationally intensive and complex. In this work, we introduce $\textit{Integrated Value Guidance}$ (IVG), a method that uses implicit and explicit value functions to guide language model decoding at token and ... | [
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272968919 | 2409.18624 | 2024-09-27 | Unsupervised Cognition | Unsupervised learning methods have a soft inspiration in cognition models. To this day, the most successful unsupervised learning methods revolve around clustering samples in a mathematical space. In this paper we propose a primitive-based, unsupervised learning approach for decision-making inspired by a novel cognitio... | [
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272969197 | 2409.18351 | 2024-09-27 | Tracking Software Security Topics | Software security incidents occur everyday and thousands of software security reports are announced each month. Thus, it is difficult for software security researchers, engineers, and other stakeholders to follow software security topics of their interests in real-time. In this paper, we propose, SOSK, a novel tool for... | [
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272987289 | 2409.19151 | 2024-09-27 | Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book? | Extremely low-resource (XLR) languages lack substantial corpora for training NLP models, motivating the use of all available resources such as dictionaries and grammar books. Machine Translation from One Book (Tanzer et al., 2024) suggests that prompting long-context LLMs with one grammar book enables English-Kalamang ... | [
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272969040 | 2409.18796 | 2024-09-27 | Hierarchical Federated ADMM | In this paper, we depart from the widely-used gradient descent-based hierarchical federated learning (FL) algorithms to develop a novel hierarchical FL framework based on the alternating direction method of multipliers (ADMM). Within this framework, we propose two novel FL algorithms, which both use ADMM in the top lay... | [
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272969413 | 2409.18454 | 2024-09-27 | Leveraging Long-Context Large Language Models for Multi-Document Understanding and Summarization in Enterprise Applications | The rapid increase in unstructured data across various fields has made multi-document comprehension and summarization a critical task. Traditional approaches often fail to capture relevant context, maintain logical consistency, and extract essential information from lengthy documents. This paper explores the use of Lon... | [
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272968710 | 2409.18630 | 2024-09-27 | Entropy, concentration, and learning: a statistical mechanics primer | Artificial intelligence models trained through loss minimization have demonstrated significant success, grounded in principles from fields like information theory and statistical physics. This work explores these established connections through the lens of statistical mechanics, starting from first-principles sample co... | [
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272969052 | 2409.18842 | 2024-09-27 | Classical Statistical (In-Sample) Intuitions Don't Generalize Well: A Note on Bias-Variance Tradeoffs, Overfitting and Moving from Fixed to Random Designs | The sudden appearance of modern machine learning (ML) phenomena like double descent and benign overfitting may leave many classically trained statisticians feeling uneasy -- these phenomena appear to go against the very core of statistical intuitions conveyed in any introductory class on learning from data. The histori... | [
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272969158 | 2409.18959 | 2024-09-27 | O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions | Score-based diffusion models, which generate new data by learning to reverse a diffusion process that perturbs data from the target distribution into noise, have achieved remarkable success across various generative tasks. Despite their superior empirical performance, existing theoretical guarantees are often constrain... | [
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272968998 | 2409.18664 | 2024-09-27 | How green is continual learning, really? Analyzing the energy consumption in continual training of vision foundation models | With the ever-growing adoption of AI, its impact on the environment is no longer negligible. Despite the potential that continual learning could have towards Green AI, its environmental sustainability remains relatively uncharted. In this work we aim to gain a systematic understanding of the energy efficiency of contin... | [
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272969486 | 2409.18915 | 2024-09-27 | A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs | As a popular paradigm for juggling data privacy and collaborative training, federated learning (FL) is flourishing to distributively process the large scale of heterogeneous datasets on edged clients. Due to bandwidth limitations and security considerations, it ingeniously splits the original problem into multiple subp... | [
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272986709 | 2409.19174 | 2024-09-27 | Feature Estimation of Global Language Processing in EEG Using Attention Maps | Understanding the correlation between EEG features and cognitive tasks is crucial for elucidating brain function. Brain activity synchronizes during speaking and listening tasks. However, it is challenging to estimate task-dependent brain activity characteristics with methods with low spatial resolution but high tempor... | [
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281393906 | 2410.00046 | 2024-09-27 | Mixture of Multicenter Experts in Multimodal AI for Debiased Radiotherapy Target Delineation | Clinical decision-making reflects diverse strategies shaped by regional patient populations and institutional protocols. However, most existing medical artificial intelligence (AI) models are trained on highly prevalent data patterns, which reinforces biases and fails to capture the breadth of clinical expertise. Inspi... | [
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272969233 | 2409.18852 | 2024-09-27 | Space-time 2D Gaussian Splatting for Accurate Surface Reconstruction under Complex Dynamic Scenes | Previous surface reconstruction methods either suffer from low geometric accuracy or lengthy training times when dealing with real-world complex dynamic scenes involving multi-person activities, and human-object interactions. To tackle the dynamic contents and the occlusions in complex scenes, we present a space-time 2... | [
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272969419 | 2409.18631 | 2024-09-27 | Quantum Algorithms for Drone Mission Planning | Mission planning often involves optimising the use of ISR (Intelligence, Surveillance and Reconnaissance) assets in order to achieve a set of mission objectives within allowed parameters subject to constraints. The missions of interest here, involve routing multiple UAVs visiting multiple targets, utilising sensors to ... | [
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272987576 | 2409.19060 | 2024-09-27 | CURATE: Scaling-up Differentially Private Causal Graph Discovery | Causal Graph Discovery (CGD) is the process of estimating the underlying probabilistic graphical model that represents joint distribution of features of a dataset. CGD-algorithms are broadly classified into two categories: (i) Constraint-based algorithms (outcome depends on conditional independence (CI) tests), (ii) Sc... | [
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272969275 | 2409.18491 | 2024-09-27 | Treating Brain-inspired Memories as Priors for Diffusion Model to Forecast Multivariate Time Series | Forecasting Multivariate Time Series (MTS) involves significant challenges in various application domains. One immediate challenge is modeling temporal patterns with the finite length of the input. These temporal patterns usually involve periodic and sudden events that recur across different channels. To better capture... | [
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272969212 | 2409.18747 | 2024-09-27 | Cottention: Linear Transformers With Cosine Attention | Attention mechanisms, particularly softmax attention, have been instrumental in the success of transformer-based models such as GPT. However, the quadratic memory complexity of softmax attention with respect to sequence length poses significant challenges for processing longer sequences. We introduce Cottention, a nove... | [
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270286521 | 2409.18470 | 2024-09-27 | Fairness without Sensitive Attributes via Knowledge Sharing | While model fairness improvement has been explored previously, existing methods invariably rely on adjusting explicit sensitive attribute values in order to improve model fairness in downstream tasks. However, we observe a trend in which sensitive demographic information becomes inaccessible as public concerns around d... | [
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272968957 | 2409.18868 | 2024-09-27 | Individuation in Neural Models with and without Visual Grounding | We show differences between a language-and-vision model CLIP and two text-only models - FastText and SBERT - when it comes to the encoding of individuation information. We study latent representations that CLIP provides for substrates, granular aggregates, and various numbers of objects. We demonstrate that CLIP embedd... | [
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272969383 | 2409.18543 | 2024-09-27 | Reducing Semantic Ambiguity In Domain Adaptive Semantic Segmentation Via Probabilistic Prototypical Pixel Contrast | Domain adaptation aims to reduce the model degradation on the target domain caused by the domain shift between the source and target domains. Although encouraging performance has been achieved by combining cognitive learning with the self-training paradigm, they suffer from ambiguous scenarios caused by scale, illumina... | [
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272987541 | 2409.19078 | 2024-09-27 | Differential privacy enables fair and accurate AI-based analysis of speech disorders while protecting patient data | Speech pathology has impacts on communication abilities and quality of life. While deep learning-based models have shown potential in diagnosing these disorders, the use of sensitive data raises critical privacy concerns. Although differential privacy (DP) has been explored in the medical imaging domain, its applicatio... | [
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272969074 | 2409.18647 | 2024-09-27 | HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents | Rhetorical Role Labeling (RRL) of legal documents is pivotal for various downstream tasks such as summarization, semantic case search and argument mining. Existing approaches often overlook the varying difficulty levels inherent in legal document discourse styles and rhetorical roles. In this work, we propose HiCuLR, a... | [
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272969378 | 2409.18676 | 2024-09-27 | Toward Universal and Interpretable World Models for Open-ended Learning Agents | We introduce a generic, compositional and interpretable class of generative world models that supports open-ended learning agents. This is a sparse class of Bayesian networks capable of approximating a broad range of stochastic processes, which provide agents with the ability to learn world models in a manner that may ... | [
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272988029 | 2409.19169 | 2024-09-27 | TwinCL: A Twin Graph Contrastive Learning Model for Collaborative Filtering | In the domain of recommendation and collaborative filtering, Graph Contrastive Learning (GCL) has become an influential approach. Nevertheless, the reasons for the effectiveness of contrastive learning are still not well understood. In this paper, we challenge the conventional use of random augmentations on graph struc... | [
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