84 citations · 181 across the 12 of their papers we have counts for
6 papers · 1 filter
On the Overlooked Structure of Stochastic Gradients
Zeke Xie, Qian-Yuan Tang, Mingming Sun +1
Stochastic gradients closely relate to both optimization and generalization of deep neural networks (DNNs). Some works attempted to explain the success of stochastic optimization f…
CGAR: Critic Guided Action Redistribution in Reinforcement Leaning
Tairan Huang, Xu Li, Hao Li +2
Training a game-playing reinforcement learning agent requires multiple interactions with the environment. Ignorant random exploration may cause a waste of time and resources. It's…
Dataset Pruning: Reducing Training Data by Examining Generalization Influence
Shuo Yang, Zeke Xie, Hanyu Peng +3
The great success of deep learning heavily relies on increasingly larger training data, which comes at a price of huge computational and infrastructural costs. This poses crucial q…
SpaceE: Knowledge Graph Embedding by Relational Linear Transformation in the Entity Space
Jinxing Yu, Yunfeng Cai, Mingming Sun +1
Translation distance based knowledge graph embedding (KGE) methods, such as TransE and RotatE, model the relation in knowledge graphs as translation or rotation in the vector space…
Under-confidence Backdoors Are Resilient and Stealthy Backdoors
Minlong Peng, Zidi Xiong, Quang H. Nguyen +3
By injecting a small number of poisoned samples into the training set, backdoor attacks aim to make the victim model produce designed outputs on any input injected with pre-designe…
On the Power-Law Hessian Spectrums in Deep Learning
Zeke Xie, Qian-Yuan Tang, Yunfeng Cai +2
It is well-known that the Hessian of deep loss landscape matters to optimization, generalization, and even robustness of deep learning. Recent works empirically discovered that the…