15 citations · 24 across the 4 of their papers we have counts for
5 papers · 1 filter
Complement Objective Training
Hao-Yun Chen, Pei-Hsin Wang, Chun-Hao Liu +5
Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although…
Improving Adversarial Robustness via Guided Complement Entropy
Hao-Yun Chen, Jhao-Hong Liang, Shih-Chieh Chang +4
Adversarial robustness has emerged as an important topic in deep learning as carefully crafted attack samples can significantly disturb the performance of a model. Many recent meth…
Searching Toward Pareto-Optimal Device-Aware Neural Architectures
An-Chieh Cheng, Jin-Dong Dong, Chi-Hung Hsu +7
Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performance in many tasks such as image classification and language understanding. However,…
MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning
Chi-Hung Hsu, Shu-Huan Chang, Jhao-Hong Liang +7
Recent studies on neural architecture search have shown that automatically designed neural networks perform as good as expert-crafted architectures. While most existing works aim a…
Task Transfer by Preference-Based Cost Learning
Mingxuan Jing, Xiaojian Ma, Wenbing Huang +2
The goal of task transfer in reinforcement learning is migrating the action policy of an agent to the target task from the source task. Given their successes on robotic action plan…