84 citations · 181 across the 12 of their papers we have counts for
5 papers · 1 filter
Learning from Ambiguous Data with Hard Labels
Zeke Xie, Zheng He, Nan Lu +5
Real-world data often contains intrinsic ambiguity that the common single-hard-label annotation paradigm ignores. Standard training using ambiguous data with these hard labels may…
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…
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…