272 citations · 834 across the 20 of their papers we have counts for
4 papers · 1 filter
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning
Tianren Zhang, Shangqi Guo, Tian Tan +2
Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training in…
Interpretable Disentanglement of Neural Networks by Extracting Class-Specific Subnetwork
Yulong Wang, Xiaolin Hu, Hang Su
We propose a novel perspective to understand deep neural networks in an interpretable disentanglement form. For each semantic class, we extract a class-specific functional subnetwo…
Knowledge Distillation via Route Constrained Optimization
Xiao Jin, Baoyun Peng, Yichao Wu +5
Distillation-based learning boosts the performance of the miniaturized neural network based on the hypothesis that the representation of a teacher model can be used as structured a…
Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift
Xiang Li, Shuo Chen, Xiaolin Hu +1
This paper first answers the question "why do the two most powerful techniques Dropout and Batch Normalization (BN) often lead to a worse performance when they are combined togethe…