有害
发表于 2025-3-23 11:31:02
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盘旋
发表于 2025-3-23 15:33:34
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捐助
发表于 2025-3-23 21:23:23
,Learning Energy-Based Models with Adversarial Training,nergy function that models the support of the data distribution, and the learning process is closely related to MCMC-based maximum likelihood learning of EBMs. We further propose improved techniques for generative modeling with AT, and demonstrate that this new approach is capable of generating dive
剥皮
发表于 2025-3-24 00:00:46
,Adversarial Label Poisoning Attack on Graph Neural Networks via Label Propagation,ever, labeling graph data for training is a challenging task, and inaccurate labels may mislead the training process to erroneous GNN models for node classification. In this paper, we consider label poisoning attacks on training data, where the labels of input data are modified by an adversary befor
airborne
发表于 2025-3-24 03:51:57
,Revisiting Outer Optimization in Adversarial Training, optimization. This paper aims to analyze this choice by investigating the overlooked role of outer optimization in AT. Our exploratory evaluations reveal that AT induces higher gradient norm and variance compared to NT. This phenomenon hinders the outer optimization in AT since the convergence rate
theta-waves
发表于 2025-3-24 08:52:37
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atrophy
发表于 2025-3-24 13:36:42
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芦笋
发表于 2025-3-24 16:44:51
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Debility
发表于 2025-3-24 21:59:08
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大门在汇总
发表于 2025-3-25 00:21:35
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