Titlebook: Artificial Intelligence; First CAAI Internati Lu Fang,Yiran Chen,Weisheng Dong Conference proceedings 2021 Springer Nature Switzerland AG 2

insincerity

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

調整校對

compose

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

FICE

Crayon

Obedient

exclamation

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

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

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.