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272831712 | 2409.16068 | 2024-09-24 | A decision-theoretic model for a principal-agent collaborative learning problem | In this technical note, we consider a collaborative learning framework with principal-agent setting, in which the principal at each time-step determines a set of appropriate aggregation coefficients based on how the current parameter estimates from a group of $K$ agents effectively performed in connection with a separa... | [
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272832570 | 2409.15687 | 2024-09-24 | A Comprehensive Evaluation of Large Language Models on Mental Illnesses | Large Language Models (LLMs) have shown promise in various domains, including healthcare, with significant potential to transform mental health applications by enabling scalable and accessible solutions. This study aims to provide a comprehensive evaluation of 33 LLMs, ranging from 2 billion to 405+ billion parameters,... | [
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272968969 | 2409.18807 | 2024-09-24 | LLM With Tools: A Survey | The integration of tools in augmenting large language models presents a novel approach toward enhancing the efficiency and accuracy of these models in handling specific, complex tasks. This paper delves into the methodology,challenges, and developments in the realm of teaching LLMs to use external tools, thereby pushin... | [
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272832138 | 2409.15794 | 2024-09-24 | Towards Universal Large-Scale Foundational Model for Natural Gas Demand Forecasting | In the context of global energy strategy, accurate natural gas demand forecasting is crucial for ensuring efficient resource allocation and operational planning. Traditional forecasting methods struggle to cope with the growing complexity and variability of gas consumption patterns across diverse industries and commerc... | [
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272880828 | 2409.16494 | 2024-09-24 | A Unified Hallucination Mitigation Framework for Large Vision-Language Models | Hallucination is a common problem for Large Vision-Language Models (LVLMs) with long generations which is difficult to eradicate. The generation with hallucinations is partially inconsistent with the image content. To mitigate hallucination, current studies either focus on the process of model inference or the results ... | [
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272832373 | 2409.15902 | 2024-09-24 | Konstruktor: A Strong Baseline for Simple Knowledge Graph Question Answering | While being one of the most popular question types, simple questions such as "Who is the author of Cinderella?", are still not completely solved. Surprisingly, even the most powerful modern Large Language Models are prone to errors when dealing with such questions, especially when dealing with rare entities. At the sam... | [
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272831728 | 2409.15846 | 2024-09-24 | Potential Field as Scene Affordance for Behavior Change-Based Visual Risk Object Identification | We study behavior change-based visual risk object identification (Visual-ROI), a critical framework designed to detect potential hazards for intelligent driving systems. Existing methods often show significant limitations in spatial accuracy and temporal consistency, stemming from an incomplete understanding of scene a... | [
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272880918 | 2409.16456 | 2024-09-24 | Communication and Energy Efficient Federated Learning using Zero-Order Optimization Technique | Federated learning (FL) is a popular machine learning technique that enables multiple users to collaboratively train a model while maintaining the user data privacy. A significant challenge in FL is the communication bottleneck in the upload direction, and thus the corresponding energy consumption of the devices, attri... | [
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272832483 | 2409.15868 | 2024-09-24 | Privacy Evaluation Benchmarks for NLP Models | By inducing privacy attacks on NLP models, attackers can obtain sensitive information such as training data and model parameters, etc. Although researchers have studied, in-depth, several kinds of attacks in NLP models, they are non-systematic analyses. It lacks a comprehensive understanding of the impact caused by the... | [
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272831730 | 2409.16243 | 2024-09-24 | A fast and sound tagging method for discontinuous named-entity recognition | We introduce a novel tagging scheme for discontinuous named entity recognition based on an explicit description of the inner structure of discontinuous mentions. We rely on a weighted finite state automaton for both marginal and maximum a posteriori inference. As such, our method is sound in the sense that (1) well-for... | [
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272831818 | 2409.15814 | 2024-09-24 | Interactive Example-based Explanations to Improve Health Professionals' Onboarding with AI for Human-AI Collaborative Decision Making | A growing research explores the usage of AI explanations on user's decision phases for human-AI collaborative decision-making. However, previous studies found the issues of overreliance on `wrong' AI outputs. In this paper, we propose interactive example-based explanations to improve health professionals' onboarding wi... | [
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272831828 | 2409.15682 | 2024-09-24 | Linear Contextual Bandits with Interference | Interference, a key concept in causal inference, extends the reward modeling process by accounting for the impact of one unit's actions on the rewards of others. In contextual bandit (CB) settings, where multiple units are present in the same round, potential interference can significantly affect the estimation of expe... | [
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272832031 | 2409.15893 | 2024-09-24 | Unsupervised Attention Regularization Based Domain Adaptation for Oracle Character Recognition | The study of oracle characters plays an important role in Chinese archaeology and philology. However, the difficulty of collecting and annotating real-world scanned oracle characters hinders the development of oracle character recognition. In this paper, we develop a novel unsupervised domain adaptation (UDA) method, i... | [
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276885347 | 2503.04928 | 2024-09-24 | AUTOFRAME -- A Software-driven Integration Framework for Automotive Systems | The evolution of automotive technologies towards more integrated and sophisticated systems requires a shift from traditional distributed architectures to centralized vehicle architectures. This work presents a novel framework that addresses the increasing complexity of Software Defined Vehicles (SDV) through a centrali... | [
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272832357 | 2409.15727 | 2024-09-24 | LaPose: Laplacian Mixture Shape Modeling for RGB-Based Category-Level Object Pose Estimation | While RGBD-based methods for category-level object pose estimation hold promise, their reliance on depth data limits their applicability in diverse scenarios. In response, recent efforts have turned to RGB-based methods; however, they face significant challenges stemming from the absence of depth information. On one ha... | [
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272832255 | 2409.15658 | 2024-09-24 | Long-horizon Embodied Planning with Implicit Logical Inference and Hallucination Mitigation | Long-horizon embodied planning underpins embodied AI. To accomplish long-horizon tasks, one of the most feasible ways is to decompose abstract instructions into a sequence of actionable steps. Foundation models still face logical errors and hallucinations in long-horizon planning, unless provided with highly relevant e... | [
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272831710 | 2409.15867 | 2024-09-24 | In-Context Ensemble Learning from Pseudo Labels Improves Video-Language Models for Low-Level Workflow Understanding | A Standard Operating Procedure (SOP) defines a low-level, step-by-step written guide for a business software workflow. SOP generation is a crucial step towards automating end-to-end software workflows. Manually creating SOPs can be time-consuming. Recent advancements in large video-language models offer the potential f... | [
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272832163 | 2409.15892 | 2024-09-24 | Symmetries and Expressive Requirements for Learning General Policies | State symmetries play an important role in planning and generalized planning. In the first case, state symmetries can be used to reduce the size of the search; in the second, to reduce the size of the training set. In the case of general planning, however, it is also critical to distinguish non-symmetric states, i.e., ... | [
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272831705 | 2409.15723 | 2024-09-24 | Federated Large Language Models: Current Progress and Future Directions | Large language models are rapidly gaining popularity and have been widely adopted in real-world applications. While the quality of training data is essential, privacy concerns arise during data collection. Federated learning offers a solution by allowing multiple clients to collaboratively train LLMs without sharing lo... | [
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273707767 | 2410.23298 | 2024-09-24 | Trajectory Prediction for Autonomous Driving using Agent-Interaction Graph Embedding | Trajectory prediction module in an autonomous driving system is crucial for the decision-making and safety of the autonomous agent car and its surroundings. This work presents a novel scheme called AiGem (Agent-Interaction Graph Embedding) to predict traffic vehicle trajectories around the autonomous car. AiGem tackles... | [
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272832029 | 2409.16191 | 2024-09-24 | HelloBench: Evaluating Long Text Generation Capabilities of Large Language Models | In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks (e.g., long-context understanding), and many benchmarks have been proposed. However, we observe that long text generation capabilities are not well investigated. Therefore, we introduce the Hierarchical Long Text Ge... | [
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272832154 | 2409.15774 | 2024-09-24 | Bi-Level Belief Space Search for Compliant Part Mating Under Uncertainty | The problem of mating two parts with low clearance remains difficult for autonomous robots. We present bi-level belief assembly (BILBA), a model-based planner that computes a sequence of compliant motions which can leverage contact with the environment to reduce uncertainty and perform challenging assembly tasks with l... | [
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272832063 | 2409.16071 | 2024-09-24 | Learning with Confidence: Training Better Classifiers from Soft Labels | In supervised machine learning, models are typically trained using data with hard labels, i.e., definite assignments of class membership. This traditional approach, however, does not take the inherent uncertainty in these labels into account. We investigate whether incorporating label uncertainty, represented as discre... | [
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272832338 | 2409.15810 | 2024-09-24 | Hyperbolic Image-and-Pointcloud Contrastive Learning for 3D Classification | 3D contrastive representation learning has exhibited remarkable efficacy across various downstream tasks. However, existing contrastive learning paradigms based on cosine similarity fail to deeply explore the potential intra-modal hierarchical and cross-modal semantic correlations about multi-modal data in Euclidean sp... | [
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272831670 | 2409.16077 | 2024-09-24 | Leveraging Mixture of Experts for Improved Speech Deepfake Detection | Speech deepfakes pose a significant threat to personal security and content authenticity. Several detectors have been proposed in the literature, and one of the primary challenges these systems have to face is the generalization over unseen data to identify fake signals across a wide range of datasets. In this paper, w... | [
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272881322 | 2409.16382 | 2024-09-24 | Towards Synthetic Data Generation for Improved Pain Recognition in Videos under Patient Constraints | Recognizing pain in video is crucial for improving patient-computer interaction systems, yet traditional data collection in this domain raises significant ethical and logistical challenges. This study introduces a novel approach that leverages synthetic data to enhance video-based pain recognition models, providing an ... | [
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272831752 | 2409.15745 | 2024-09-24 | ManiNeg: Manifestation-guided Multimodal Pretraining for Mammography Classification | Breast cancer is a significant threat to human health. Contrastive learning has emerged as an effective method to extract critical lesion features from mammograms, thereby offering a potent tool for breast cancer screening and analysis. A crucial aspect of contrastive learning involves negative sampling, where the sele... | [
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272832639 | 2409.15825 | 2024-09-24 | 60 Data Points are Sufficient to Fine-Tune LLMs for Question-Answering | Large language models (LLMs) encode extensive world knowledge through pre-training on massive datasets, which can then be fine-tuned for the question-answering (QA) task. However, effective strategies for fine-tuning LLMs for the QA task remain largely unexplored. To address this gap, we categorize supervised fine-tuni... | [
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272881155 | 2409.16444 | 2024-09-24 | Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain | The accelerated expansion of the Internet of Things (IoT) has raised critical challenges associated with privacy, security, and data integrity, specifically in infrastructures such as smart cities or smart manufacturing. Blockchain technology provides immutable, scalable, and decentralized solutions to address these ch... | [
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272832280 | 2409.15755 | 2024-09-24 | Stage-Wise Reward Shaping for Acrobatic Robots: A Constrained Multi-Objective Reinforcement Learning Approach | As the complexity of tasks addressed through reinforcement learning (RL) increases, the definition of reward functions also has become highly complicated. We introduce an RL method aimed at simplifying the reward-shaping process through intuitive strategies. Initially, instead of a single reward function composed of va... | [
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272832464 | 2409.15939 | 2024-09-24 | Self-supervised Shape Completion via Involution and Implicit Correspondences | 3D shape completion is traditionally solved using supervised training or by distribution learning on complete shape examples. Recently self-supervised learning approaches that do not require any complete 3D shape examples have gained more interests. In this paper, we propose a non-adversarial self-supervised approach f... | [
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272831913 | 2409.15627 | 2024-09-24 | ModCube: Modular, Self-Assembling Cubic Underwater Robot | This paper presents a low-cost, centralized modular underwater robot platform, ModCube, which can be used to study swarm coordination for a wide range of tasks in underwater environments. A ModCube structure consists of multiple ModCube robots. Each robot can move in six DoF with eight thrusters and can be rigidly conn... | [
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272831716 | 2409.15931 | 2024-09-24 | Automatic Registration of SHG and H&E Images with Feature-based Initial Alignment and Intensity-based Instance Optimization: Contribution to the COMULIS Challenge | The automatic registration of noninvasive second-harmonic generation microscopy to hematoxylin and eosin slides is a highly desired, yet still unsolved problem. The task is challenging because the second-harmonic images contain only partial information, in contrast to the stained H&E slides that provide more informatio... | [
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272831676 | 2409.15690 | 2024-09-24 | A Survey of Stance Detection on Social Media: New Directions and Perspectives | In modern digital environments, users frequently express opinions on contentious topics, providing a wealth of information on prevailing attitudes. The systematic analysis of these opinions offers valuable insights for decision-making in various sectors, including marketing and politics. As a result, stance detection h... | [
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272832506 | 2409.16182 | 2024-09-24 | TiM4Rec: An Efficient Sequential Recommendation Model Based on Time-Aware Structured State Space Duality Model | The Sequential Recommendation modeling paradigm is shifting from Transformer to Mamba architecture, which comprises two generations: Mamba1, based on the State Space Model (SSM), and Mamba2, based on State Space Duality (SSD). Although SSD offers superior computational efficiency compared to SSM, it suffers performance... | [
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272832035 | 2409.15654 | 2024-09-24 | Cambricon-LLM: A Chiplet-Based Hybrid Architecture for On-Device Inference of 70B LLM | Deploying advanced large language models on edge devices, such as smartphones and robotics, is a growing trend that enhances user data privacy and network connectivity resilience while preserving intelligent capabilities. However, such a task exhibits single-batch computing with incredibly low arithmetic intensity, whi... | [
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272881374 | 2409.16441 | 2024-09-24 | A novel open-source ultrasound dataset with deep learning benchmarks for spinal cord injury localization and anatomical segmentation | While deep learning has catalyzed breakthroughs across numerous domains, its broader adoption in clinical settings is inhibited by the costly and time-intensive nature of data acquisition and annotation. To further facilitate medical machine learning, we present an ultrasound dataset of 10,223 Brightness-mode (B-mode) ... | [
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272832415 | 2409.16143 | 2024-09-24 | Seeing Faces in Things: A Model and Dataset for Pareidolia | The human visual system is well-tuned to detect faces of all shapes and sizes. While this brings obvious survival advantages, such as a better chance of spotting unknown predators in the bush, it also leads to spurious face detections. ``Face pareidolia'' describes the perception of face-like structure among otherwise ... | [
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272880951 | 2409.16461 | 2024-09-24 | Strategies for Improving NL-to-FOL Translation with LLMs: Data Generation, Incremental Fine-Tuning, and Verification | Logical reasoning is a fundamental task in natural language processing that presents significant challenges to Large Language Models (LLMs). The inherent characteristics of logical reasoning makes it well-suited for symbolic representations such as first-order logic (FOL). Research in symbolic logical reasoning explore... | [
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272831782 | 2409.15953 | 2024-09-24 | Mind the Prompt: A Novel Benchmark for Prompt-based Class-Agnostic Counting | Recently, object counting has shifted towards class-agnostic counting (CAC), which counts instances of arbitrary object classes never seen during model training. With advancements in robust vision-and-language foundation models, there is a growing interest in prompt-based CAC, where object categories are specified usin... | [
"cs.CV"
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"Large multimodal model evaluation",
"Multimodality and language grounding",
"Open-vocabulary / open-task vision-language models",
"Visual-prompt understanding in multimodal models"
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