30 citations · 54 across the 5 of their papers we have counts for
7 papers · 1 filter
InstaNAS: Instance-aware Neural Architecture Search
An-Chieh Cheng, Chieh Hubert Lin, Da-Cheng Juan +2
Conventional Neural Architecture Search (NAS) aims at finding a single architecture that achieves the best performance, which usually optimizes task related learning objectives suc…
Policy Certificates: Towards Accountable Reinforcement Learning
Christoph Dann, Lihong Li, Wei Wei +1
The performance of a reinforcement learning algorithm can vary drastically during learning because of exploration. Existing algorithms provide little information about the quality…
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,…
Escaping from Collapsing Modes in a Constrained Space
Chia-Che Chang, Chieh Hubert Lin, Che-Rung Lee +3
Generative adversarial networks (GANs) often suffer from unpredictable mode-collapsing during training. We study the issue of mode collapse of Boundary Equilibrium Generative Adver…
DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures
Jin-Dong Dong, An-Chieh Cheng, Da-Cheng Juan +2
Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performances in applications such as image classification and language modeling. However, t…
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…