Search Results for author: Samuel Yen-Chi Chen

Found 20 papers, 7 papers with code

When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing

no code implementations17 Feb 2022 Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen, Yu Tsao, Pin-Yu Chen

Our experiments on intent classification show that our proposed BERT-QTC model attains competitive experimental results in the Snips and ATIS spoken language datasets.

Classification Federated Learning +3

Financial Vision Based Reinforcement Learning Trading Strategy

no code implementations3 Feb 2022 Yun-Cheng Tsai, Fu-Min Szu, Jun-Hao Chen, Samuel Yen-Chi Chen

Hence, we need to ask about the AI "black box", including why did AI decide to do this or not?

reinforcement-learning

Financial Vision Based Differential Privacy Applications

no code implementations28 Dec 2021 Jun-Hao Chen, Yi-Jen Wang, Yun-Cheng Tsai, Samuel Yen-Chi Chen

We apply two representative deep learning privacy-privacy frameworks proposed by Google to financial trading data.

Quantum Architecture Search via Continual Reinforcement Learning

no code implementations10 Dec 2021 Esther Ye, Samuel Yen-Chi Chen

To aid this endeavor, this paper proposes a machine learning-based method to construct quantum circuit architectures.

Continual Learning Q-Learning +1

The Dawn of Quantum Natural Language Processing

2 code implementations13 Oct 2021 Riccardo Di Sipio, Jia-Hong Huang, Samuel Yen-Chi Chen, Stefano Mangini, Marcel Worring

In this paper, we discuss the initial attempts at boosting understanding human language based on deep-learning models with quantum computing.

Sentiment Analysis

Variational Quantum Reinforcement Learning via Evolutionary Optimization

no code implementations1 Sep 2021 Samuel Yen-Chi Chen, Chih-Min Huang, Chia-Wei Hsing, Hsi-Sheng Goan, Ying-Jer Kao

Recent advance in classical reinforcement learning (RL) and quantum computation (QC) points to a promising direction of performing RL on a quantum computer.

reinforcement-learning

Quantum Architecture Search via Deep Reinforcement Learning

1 code implementation15 Apr 2021 En-Jui Kuo, Yao-Lung L. Fang, Samuel Yen-Chi Chen

Recent advances in quantum computing have drawn considerable attention to building realistic application for and using quantum computers.

reinforcement-learning

Federated Quantum Machine Learning

no code implementations22 Mar 2021 Samuel Yen-Chi Chen, Shinjae Yoo

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located.

Federated Learning

Quantum machine learning with differential privacy

no code implementations10 Mar 2021 William M Watkins, Samuel Yen-Chi Chen, Shinjae Yoo

In this study, we develop a hybrid quantum-classical model that is trained to preserve privacy using differentially private optimization algorithm.

General Classification

An end-to-end trainable hybrid classical-quantum classifier

no code implementations4 Feb 2021 Samuel Yen-Chi Chen, Chih-Min Huang, Chia-Wei Hsing, Ying-Jer Kao

We introduce a hybrid model combining a quantum-inspired tensor network and a variational quantum circuit to perform supervised learning tasks.

Tensor Networks

Hybrid Quantum-Classical Graph Convolutional Network

no code implementations15 Jan 2021 Samuel Yen-Chi Chen, Tzu-Chieh Wei, Chao Zhang, Haiwang Yu, Shinjae Yoo

This research provides a hybrid quantum-classical graph convolutional network (QGCNN) for learning HEP data.

Quantum Convolutional Neural Networks for High Energy Physics Data Analysis

no code implementations22 Dec 2020 Samuel Yen-Chi Chen, Tzu-Chieh Wei, Chao Zhang, Haiwang Yu, Shinjae Yoo

This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events.

Hybrid quantum-classical classifier based on tensor network and variational quantum circuit

no code implementations30 Nov 2020 Samuel Yen-Chi Chen, Chih-Min Huang, Chia-Wei Hsing, Ying-Jer Kao

One key step in performing quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices is the dimension reduction of the input data prior to their encoding.

Dimensionality Reduction Tensor Networks

Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition

2 code implementations26 Oct 2020 Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen, Pin-Yu Chen, Sabato Marco Siniscalchi, Xiaoli Ma, Chin-Hui Lee

Testing on the Google Speech Commands Dataset, the proposed QCNN encoder attains a competitive accuracy of 95. 12% in a decentralized model, which is better than the previous architectures using centralized RNN models with convolutional features.

 Ranked #1 on Keyword Spotting on Google Speech Commands (10-keyword Speech Commands dataset metric)

Automatic Speech Recognition Federated Learning +1

Quantum Long Short-Term Memory

no code implementations3 Sep 2020 Samuel Yen-Chi Chen, Shinjae Yoo, Yao-Lung L. Fang

Long short-term memory (LSTM) is a kind of recurrent neural networks (RNN) for sequence and temporal dependency data modeling and its effectiveness has been extensively established.

Data Augmentation for Deep Candlestick Learner

2 code implementations14 May 2020 Chia-Ying Tsao, Jun-Hao Chen, Samuel Yen-Chi Chen, Yun-Cheng Tsai

To successfully build a deep learning model, it will need a large amount of labeled data.

Data Augmentation

Explainable Deep Convolutional Candlestick Learner

2 code implementations8 Jan 2020 Jun-Hao Chen, Samuel Yen-Chi Chen, Yun-Cheng Tsai, Chih-Shiang Shur

Candlesticks are graphical representations of price movements for a given period.

Time Series

Variational Quantum Circuits for Deep Reinforcement Learning

1 code implementation30 Jun 2019 Samuel Yen-Chi Chen, Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma, Hsi-Sheng Goan

To the best of our knowledge, this work is the first proof-of-principle demonstration of variational quantum circuits to approximate the deep $Q$-value function for decision-making and policy-selection reinforcement learning with experience replay and target network.

Decision Making reinforcement-learning

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