no code implementations • 20 Feb 2024 • Hao-Yuan Chen, Yen-Jui Chang, Shih-wei Liao, Ching-Ray Chang
Quantum embedding with transformers is a novel and promising architecture for quantum machine learning to deliver exceptional capability on near-term devices or simulators.
no code implementations • 2 Dec 2023 • Hao-Yuan Chen, Yen-Jui Chang, Shih-wei Liao, Ching-Ray Chang
The research explores the potential of quantum deep learning models to address challenging machine learning problems that classical deep learning models find difficult to tackle.
1 code implementation • 26 Sep 2023 • Ching-Yu Chiang, I-Hua Chang, Shih-wei Liao
This study aims to explore efficient tuning methods for the screenshot captioning task.
1 code implementation • 20 Apr 2023 • Hao-Yuan Chen, Yen-Jui Chang, Shih-wei Liao, Ching-Ray Chang
This study uses a trainable variational quantum circuit (VQC) on a gate-based quantum computing model to investigate the potential for quantum benefit in a model-free reinforcement learning problem.
no code implementations • 29 Sep 2021 • Jian Hu, Siyang Jiang, Seth Austin Harding, Haibin Wu, Shih-wei Liao
QMIX, a popular MARL algorithm based on the monotonicity constraint, has been used as a baseline for the benchmark environments, such as Starcraft Multi-Agent Challenge (SMAC), Predator-Prey (PP).
2 code implementations • 6 Feb 2021 • Jian Hu, Siyang Jiang, Seth Austin Harding, Haibin Wu, Shih-wei Liao
Multi-Agent Reinforcement Learning (MARL) has seen revolutionary breakthroughs with its successful application to multi-agent cooperative tasks such as computer games and robot swarms.
no code implementations • 9 Sep 2020 • Jian Hu, Seth Austin Harding, Haibin Wu, Siyue Hu, Shih-wei Liao
Existing methods such as Value Decomposition Network (VDN) and QMIX estimate the value of long-term returns as a scalar that does not contain the information of randomness.
no code implementations • 21 Mar 2020 • Dalong Yang, Chuan Chen, Youhao Zheng, Zibin Zheng, Shih-wei Liao
Instead of directly processing the coupled nodes as GCNs, Node2Grids supports a more efficacious method in practice, mapping the coupled graph data into the independent grid-like data which can be fed into the efficient Convolutional Neural Network (CNN).
3 code implementations • ICCV 2019 • Po-Wei Wu, Yu-Jing Lin, Che-Han Chang, Edward Y. Chang, Shih-wei Liao
Our method is capable of modifying images by changing particular attributes of interest in a continuous manner while preserving the other attributes.
no code implementations • 19 Mar 2019 • Yu-Jing Lin, Po-Wei Wu, Cheng-Han Hsu, I-Ping Tu, Shih-wei Liao
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