3 citations · 6 across the 8 of their papers we have counts for
9 papers
Quantum Adaptive Self-Attention for Financial Rebalancing: An Empirical Study on Automated Market Makers in Decentralized Finance
Chi-Sheng Chen, Aidan Hung-Wen Tsai
We formulate automated market maker (AMM) \emph{rebalancing} as a binary detection problem and study a hybrid quantum--classical self-attention block, \textbf{Quantum Adaptive Self…
Quantum and Classical Machine Learning in Decentralized Finance: Comparative Evidence from Multi-Asset Backtesting of Automated Market Makers
Chi-Sheng Chen, Aidan Hung-Wen Tsai
This study presents a comprehensive empirical comparison between quantum machine learning (QML) and classical machine learning (CML) approaches in Automated Market Makers (AMM) and…
Benchmarking Classical and Quantum Models for DeFi Yield Prediction on Curve Finance
Chi-Sheng Chen, Aidan Hung-Wen Tsai
The rise of decentralized finance (DeFi) has created a growing demand for accurate yield and performance forecasting to guide liquidity allocation strategies. In this study, we ben…
Exploring the Potential of QEEGNet for Cross-Task and Cross-Dataset Electroencephalography Encoding with Quantum Machine Learning
Chi-Sheng Chen, Samuel Yen-Chi Chen, Huan-Hsin Tseng
Electroencephalography (EEG) is widely used in neuroscience and clinical research for analyzing brain activity. While deep learning models such as EEGNet have shown success in deco…
Psycho Gundam: Electroencephalography based real-time robotic control system with deep learning
Chi-Sheng Chen, Wei-Sheng Wang
The Psycho Frame, a sophisticated system primarily used in Universal Century (U.C.) series mobile suits for NEWTYPE pilots, has evolved as an integral component in harnessing the l…
NECOMIMI: Neural-Cognitive Multimodal EEG-informed Image Generation with Diffusion Models
Chi-Sheng Chen
NECOMIMI (NEural-COgnitive MultImodal EEG-Informed Image Generation with Diffusion Models) introduces a novel framework for generating images directly from EEG signals using advanc…
Quantum Multimodal Contrastive Learning Framework
Chi-Sheng Chen, Aidan Hung-Wen Tsai, Sheng-Chieh Huang
In this paper, we propose a novel framework for multimodal contrastive learning utilizing a quantum encoder to integrate EEG (electroencephalogram) and image data. This groundbreak…
QEEGNet: Quantum Machine Learning for Enhanced Electroencephalography Encoding
Chi-Sheng Chen, Samuel Yen-Chi Chen, Aidan Hung-Wen Tsai +1
Electroencephalography (EEG) is a critical tool in neuroscience and clinical practice for monitoring and analyzing brain activity. Traditional neural network models, such as EEGNet…
Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive Learning
Chi-Sheng Chen, Chun-Shu Wei
Decoding images from non-invasive electroencephalographic (EEG) signals has been a grand challenge in understanding how the human brain process visual information in real-world sce…