Summary of Revisiting Essential and Nonessential Settings Of Evidential Deep Learning, by Mengyuan Chen et al.
Revisiting Essential and Nonessential Settings of Evidential Deep Learningby Mengyuan Chen, Junyu Gao, Changsheng XuFirst…
Revisiting Essential and Nonessential Settings of Evidential Deep Learningby Mengyuan Chen, Junyu Gao, Changsheng XuFirst…
Preconditioning for Accelerated Gradient Descent Optimization and Regularizationby Qiang YeFirst submitted to arxiv on: 30…
Stochastic Inverse Problem: stability, regularization and Wasserstein gradient flowby Qin Li, Maria Oprea, Li Wang,…
Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularizationby Haoran Li, Zhennan Jiang,…
Tailored Federated Learning: Leveraging Direction Regulation & Knowledge Distillationby Huidong Tang, Chen Li, Huachong Yu,…
An Unbiased Risk Estimator for Partial Label Learning with Augmented Classesby Jiayu Hu, Senlin Shu,…
Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptationby Yucheng Wang, Peiliang Gong, Min Wu,…
Double Actor-Critic with TD Error-Driven Regularization in Reinforcement Learningby Haohui Chen, Zhiyong Chen, Aoxiang Liu,…
Forgetting, Ignorance or Myopia: Revisiting Key Challenges in Online Continual Learningby Xinrui Wang, Chuanxing Geng,…
Spectral Wavelet Dropout: Regularization in the Wavelet Domainby Rinor Cakaj, Jens Mehnert, Bin YangFirst submitted…