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21 Nov

Regional Ocean Forecasting with Hierarchical Graph Neural Networks

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[Submitted on 15 Oct 2024 (v1), last revised 20 Nov 2024 (this version, v2)] View a PDF of the paper titled Regional Ocean Forecasting with Hierarchical Graph Neural Networks, by Daniel Holmberg and 2 other authors View PDF HTML (experimental) Abstract:Accurate ocean forecasting systems are vital for understanding marine dynamics, which play a crucial role in environmental management and climate adaptation strategies. Traditional numerical solvers, while effective, are computationally expensive and time-consuming. Recent advancements in machine learning have revolutionized weather forecasting, offering fast and energy-efficient alternatives. Building on these advancements, we introduce SeaCast, a neural network designed for high-resolution, medium-range…

21 Nov

F$^3$OCUS — Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics

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21 Nov

Executable QR codes with Machine Learning for Industrial Applications

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol

21 Nov

Generalization on the Unseen, Logic Reasoning and Degree Curriculum

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[Submitted on 30 Jan 2023 (v1), last revised 20 Nov 2024 (this version, v3)] View a PDF of the paper titled Generalization on the Unseen, Logic Reasoning and Degree Curriculum, by Emmanuel Abbe and 3 other authors View PDF HTML (experimental) Abstract:This paper considers the learning of logical (Boolean) functions with a focus on the generalization on the unseen (GOTU) setting, a strong case of out-of-distribution generalization. This is motivated by the fact that the rich combinatorial nature of data in certain reasoning tasks (e.g., arithmetic/logic) makes representative data sampling challenging, and learning successfully under GOTU gives a first vignette…

21 Nov

VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation

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arXiv:2411.11919v1 Announce Type: new Abstract: Given the higher information load processed by large vision-language models (LVLMs) compared to single-modal LLMs, detecting LVLM hallucinations requires more human and time expense, and thus rise a wider safety concerns. In this paper, we introduce VL-Uncertainty, the first uncertainty-based framework for detecting hallucinations in LVLMs. Different from most existing methods that require ground-truth or pseudo annotations, VL-Uncertainty utilizes uncertainty as an intrinsic metric. We measure uncertainty by analyzing the prediction variance across semantically equivalent but perturbed prompts, including visual and textual data. When LVLMs are highly confident, they provide consistent responses to semantically equivalent…

21 Nov

Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models

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[Submitted on 30 Sep 2024 (v1), last revised 20 Nov 2024 (this version, v2)] View a PDF of the paper titled Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models, by Luohe Shi and 4 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and Parameter-Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting LLMs to downstream tasks. ICL typically constructs a few-shot learning scenario, either manually or by setting up a Retrieval-Augmented Generation (RAG) system, helping models quickly grasp domain knowledge or question-answering patterns without…

21 Nov

Procurement Auctions via Approximately Optimal Submodular Optimization

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arXiv:2411.13513v1 Announce Type: cross Abstract: We study procurement auctions, where an auctioneer seeks to acquire services from strategic sellers with private costs. The quality of services is measured by a submodular function known to the auctioneer. Our goal is to design computationally efficient procurement auctions that (approximately) maximize the difference between the quality of the acquired services and the total cost of the sellers, while ensuring incentive compatibility (IC), individual rationality (IR) for sellers, and non-negative surplus (NAS) for the auctioneer. Our contributions are twofold: (i) we provide an improved analysis of existing algorithms for non-positive submodular function maximization, and…

21 Nov

FCC: Fully Connected Correlation for Few-Shot Segmentation

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arXiv:2411.11917v1 Announce Type: new Abstract: Few-shot segmentation (FSS) aims to segment the target object in a query image using only a small set of support images and masks. Therefore, having strong prior information for the target object using the support set is essential for guiding the initial training of FSS, which leads to the success of few-shot segmentation in challenging cases, such as when the target object shows considerable variation in appearance, texture, or scale across the support and query images. Previous methods have tried to obtain prior information by creating correlation maps from pixel-level correlation on final-layer or same-layer…

21 Nov

TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs

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[Submitted on 14 Jun 2024 (v1), last revised 20 Nov 2024 (this version, v2)] View a PDF of the paper titled TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs, by Zhuofeng Li and 8 other authors View PDF HTML (experimental) Abstract:Text-Attributed Graphs (TAGs) augment graph structures with natural language descriptions, facilitating detailed depictions of data and their interconnections across various real-world settings. However, existing TAG datasets predominantly feature textual information only at the nodes, with edges typically represented by mere binary or categorical attributes. This lack of rich textual edge annotations significantly limits the exploration of contextual relationships between…

21 Nov

ZNorm: Z-Score Gradient Normalization Accelerating Skip-Connected Network Training without Architectural Modification

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[Submitted on 2 Aug 2024 (v1), last revised 20 Nov 2024 (this version, v5)] View a PDF of the paper titled ZNorm: Z-Score Gradient Normalization Accelerating Skip-Connected Network Training without Architectural Modification, by Juyoung Yun View PDF HTML (experimental) Abstract:The rapid advancements in deep learning necessitate better training methods for deep neural networks (DNNs). As models grow in complexity, vanishing and exploding gradients impede performance, particularly in skip-connected architectures like Deep Residual Networks. We propose Z-Score Normalization for Gradient Descent (ZNorm), an innovative technique that adjusts only the gradients without modifying the network architecture to accelerate training and improve model…