insincerity
发表于 2025-3-23 13:01:57
Diagnosis of Childhood Autism Using Multi-modal Functional Connectivity via Dynamic Hypergraph LearnASD). In recent years, some studies have hypothesized the stationary assumption and revealed the relevance of the time-varying anomaly in FC to the autistic traits. While most existing work focus on exploring properties of static FC (sFC) and dynamic FC (dFC) separately, little efforts have been mad
调整校对
发表于 2025-3-23 14:47:57
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compose
发表于 2025-3-23 19:36:30
White-Box Attacks on the CNN-Based Myoelectric Control Systemcomputer interaction. Nevertheless, it was found that CNN models are very easily tricked by adversarial instances, which are normal instances with tiny intentional perturbations. In this study, an attack framework based on universal adversarial perturbations (UAP) was proposed to attack the CNN-base
Intersect
发表于 2025-3-24 02:04:01
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FICE
发表于 2025-3-24 04:21:43
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Crayon
发表于 2025-3-24 08:06:28
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Obedient
发表于 2025-3-24 14:04:25
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exclamation
发表于 2025-3-24 18:02:21
Neighborhood Search Acceleration Based on Deep Reinforcement Learning for SSCFLPitated Facility Location Problem (SSCFLP). Specifically, we construct a deep reinforcement learning model which learns a disturbing strategy to iteratively select the customers to be adjusted, and design a neighborhood operator to generate a new solution by reassigning the selected customers. The pr
cardiovascular
发表于 2025-3-24 20:27:24
Darl Kuhn,Sam R. Alapati,Bill Padfield, we propose Gradient Re-weighting Module (GRM) to re-distribute each instance’s gradient contribution to the representation learning network. Extensive experiments on the long-tailed benchmark CIFAR10-LT, CIFAR100-LT and ImageNet-LT demonstrate the effectiveness of our proposed method.
要素
发表于 2025-3-25 02:49:11
Increasing Oversampling Diversity for Long-Tailed Visual Recognition, we propose Gradient Re-weighting Module (GRM) to re-distribute each instance’s gradient contribution to the representation learning network. Extensive experiments on the long-tailed benchmark CIFAR10-LT, CIFAR100-LT and ImageNet-LT demonstrate the effectiveness of our proposed method.