62 citations · 449 across the 49 of their papers we have counts for
10 papers · 1 filter
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…
QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks
Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen
The advent of noisy intermediate-scale quantum (NISQ) computers raises a crucial challenge to design quantum neural networks for fully quantum learning tasks. To bridge the gap, th…
A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer
Hu Hu, Sabato Marco Siniscalchi, Chao-Han Huck Yang +1
We propose a variational Bayesian (VB) approach to learning distributions of latent variables in deep neural network (DNN) models for cross-domain knowledge transfer, to address ac…
Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition
Hao Yen, Pin-Jui Ku, Chao-Han Huck Yang +4
In this study, we propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR), and build an AR-SCR system. The AR procedure aims to mo…
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification
Hao Yen, Chao-Han Huck Yang, Hu Hu +9
We propose a novel neural model compression strategy combining data augmentation, knowledge transfer, pruning, and quantization for device-robust acoustic scene classification (ASC…
Robust Unsupervised Multi-Object Tracking in Noisy Environments
C. -H. Huck Yang, Mohit Chhabra, Y. -C. Liu +3
Physical processes, camera movement, and unpredictable environmental conditions like the presence of dust can induce noise and artifacts in video feeds. We observe that popular uns…
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…
Longer Version for "Deep Context-Encoding Network for Retinal Image Captioning"
Jia-Hong Huang, Ting-Wei Wu, Chao-Han Huck Yang +1
Automatically generating medical reports for retinal images is one of the promising ways to help ophthalmologists reduce their workload and improve work efficiency. In this work, w…
PATE-AAE: Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classification
Chao-Han Huck Yang, Sabato Marco Siniscalchi, Chin-Hui Lee
We propose using an adversarial autoencoder (AAE) to replace generative adversarial network (GAN) in the private aggregation of teacher ensembles (PATE), a solution for ensuring di…
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…