1k citations · 1.6k across the 21 of their papers we have counts for
6 papers · 1 filter
Learning from Crowds with Sparse and Imbalanced Annotations
Ye Shi, Shao-Yuan Li, Sheng-Jun Huang
Traditional supervised learning requires ground truth labels for the training data, whose collection can be difficult in many cases. Recently, crowdsourcing has established itself…
CCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise
Ming-Kun Xie, Sheng-Jun Huang
Class-conditional noise commonly exists in machine learning tasks, where the class label is corrupted with a probability depending on its ground-truth. Many research efforts have b…
Gym-RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning
Shengyi Huang, Santiago Ontañón, Chris Bamford +1
In recent years, researchers have achieved great success in applying Deep Reinforcement Learning (DRL) algorithms to Real-time Strategy (RTS) games, creating strong autonomous agen…
Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries
Kun-Peng Ning, Lue Tao, Songcan Chen +1
In addition to high accuracy, robustness is becoming increasingly important for machine learning models in various applications. Recently, much research has been devoted to improvi…
Co-Imitation Learning without Expert Demonstration
Kun-Peng Ning, Hu Xu, Kun Zhu +1
Imitation learning is a primary approach to improve the efficiency of reinforcement learning by exploiting the expert demonstrations. However, in many real scenarios, obtaining exp…
Oscars: Adaptive Semi-Synchronous Parallel Model for Distributed Deep Learning with Global View
Sheng Huang
Deep learning has become an indispensable part of life, such as face recognition, NLP, etc., but the training of deep model has always been a challenge, and in recent years, the co…