53 citations · 166 across the 26 of their papers we have counts for
6 papers · 1 filter
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing
Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen +1
This work focuses on designing low complexity hybrid tensor networks by considering trade-offs between the model complexity and practical performance. Firstly, we exploit a low-ran…
Pessimistic Model Selection for Offline Deep Reinforcement Learning
Chao-Han Huck Yang, Zhengling Qi, Yifan Cui +1
Deep Reinforcement Learning (DRL) has demonstrated great potentials in solving sequential decision making problems in many applications. Despite its promising performance, practica…
Voice2Series: Reprogramming Acoustic Models for Time Series Classification
Chao-Han Huck Yang, Yun-Yun Tsai, Pin-Yu Chen
Learning to classify time series with limited data is a practical yet challenging problem. Current methods are primarily based on hand-designed feature extraction rules or domain-s…
Training a Resilient Q-Network against Observational Interference
Chao-Han Huck Yang, I-Te Danny Hung, Yi Ouyang +1
Deep reinforcement learning (DRL) has demonstrated impressive performance in various gaming simulators and real-world applications. In practice, however, a DRL agent may receive fa…
Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning
Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen +4
Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown…
Variational Quantum Circuits for Deep Reinforcement Learning
Samuel Yen-Chi Chen, Chao-Han Huck Yang, Jun Qi +3
The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains. With…