25 citations · 70 across the 24 of their papers we have counts for
12 papers · 1 filter
ColorSense: A Study on Color Vision in Machine Visual Recognition
Ming-Chang Chiu, Yingfei Wang, Derrick Eui Gyu Kim +2
Color vision is essential for human visual perception, but its impact on machine perception is still underexplored. There has been an intensified demand for understanding its role…
Inference and Denoise: Causal Inference-based Neural Speech Enhancement
Tsun-An Hsieh, Chao-Han Huck Yang, Pin-Yu Chen +2
This study addresses the speech enhancement (SE) task within the causal inference paradigm by modeling the noise presence as an intervention. Based on the potential outcome framewo…
Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming
Yun-Ning Hung, Chao-Han Huck Yang, Pin-Yu Chen +1
Transfer learning (TL) approaches have shown promising results when handling tasks with limited training data. However, considerable memory and computational resources are often re…
Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise
Jhih-Cing Huang, Yu-Lin Tsai, Chao-Han Huck Yang +4
Recently, quantum classifiers have been found to be vulnerable to adversarial attacks, in which quantum classifiers are deceived by imperceptible noises, leading to misclassificati…
Rethinking Normalization Methods in Federated Learning
Zhixu Du, Jingwei Sun, Ang Li +4
Federated learning (FL) is a popular distributed learning framework that can reduce privacy risks by not explicitly sharing private data. In this work, we explicitly uncover extern…
Iterative Qubits Management for Quantum Index Searching in a Hybrid System
Wenrui Mu, Ying Mao, Long Cheng +3
Recent advances in quantum computing systems attract tremendous attention. Commercial companies, such as IBM, Amazon, and IonQ, have started to provide access to noisy intermediate…
Benchmarking Machine Learning Robustness in Covid-19 Genome Sequence Classification
Sarwan Ali, Bikram Sahoo, Alexander Zelikovskiy +2
The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome -- millions of sequences and counting. This amount of da…
Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression
Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen +1
The noisy intermediate-scale quantum (NISQ) devices enable the implementation of the variational quantum circuit (VQC) for quantum neural networks (QNN). Although the VQC-based QNN…
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning
Momin Abbas, Quan Xiao, Lisha Chen +2
Model-agnostic meta learning (MAML) is currently one of the dominating approaches for few-shot meta-learning. Albeit its effectiveness, the optimization of MAML can be challenging…
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…
Treatment Learning Causal Transformer for Noisy Image Classification
Chao-Han Huck Yang, I-Te Danny Hung, Yi-Chieh Liu +1
Current top-notch deep learning (DL) based vision models are primarily based on exploring and exploiting the inherent correlations between training data samples and their associate…
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing
Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen +2
The rapid development of quantum computing has demonstrated many unique characteristics of quantum advantages, such as richer feature representation and more secured protection on…