32 citations · 36 across the 3 of their papers we have counts for
3 papers
cs.CV2022
Multi-Teacher Knowledge Distillation for Incremental Implicitly-Refined Classification
Longhui Yu, Zhenyu Weng, Yuqing Wang +1
Incremental learning methods can learn new classes continually by distilling knowledge from the last model (as a teacher model) to the current model (as a student model) in the seq…
cs.LG2021★ 4 cited
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect
Yuqing Wang, Minshuo Chen, Tuo Zhao +1
Recent empirical advances show that training deep models with large learning rate often improves generalization performance. However, theoretical justifications on the benefits of…
cs.LG2020★ 32 cited
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? -- A Neural Tangent Kernel Perspective
Kaixuan Huang, Yuqing Wang, Molei Tao +1
Deep residual networks (ResNets) have demonstrated better generalization performance than deep feedforward networks (FFNets). However, the theory behind such a phenomenon is still…