no code implementations • 13 Dec 2023 • Yanqiu Wu, Eromanga Adermann, Chandra Thapa, Seyit Camtepe, Hajime Suzuki, Muhammad Usman
Our extensive simulation results present that attacks generated on QVCs transfer well to CNN models, indicating that these adversarial examples can fool neural networks that they are not explicitly designed to attack.
no code implementations • 22 Aug 2023 • Larry Huynh, Jin Hong, Ajmal Mian, Hajime Suzuki, Yanqiu Wu, Seyit Camtepe
Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within classical computational frameworks.
no code implementations • 6 Nov 2022 • Yanqiu Wu, Qingyang Li, Zhiwei Qin
Motivated by this observation, we make an attempt to optimize the distribution of demand to handle this problem by learning the long-term spatio-temporal values as a guideline for pricing strategy.
no code implementations • 17 Nov 2021 • Yanqiu Wu, Xinyue Chen, Che Wang, Yiming Zhang, Keith W. Ross
In particular, Truncated Quantile Critics (TQC) achieves state-of-the-art asymptotic training performance on the MuJoCo benchmark with a distributional representation of critics; and Randomized Ensemble Double Q-Learning (REDQ) achieves high sample efficiency that is competitive with state-of-the-art model-based methods using a high update-to-data ratio and target randomization.
1 code implementation • NeurIPS 2020 • Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, Keith Ross
There has recently been a surge in research in batch Deep Reinforcement Learning (DRL), which aims for learning a high-performing policy from a given dataset without additional interactions with the environment.
3 code implementations • ICML 2020 • Che Wang, Yanqiu Wu, Quan Vuong, Keith Ross
We aim to develop off-policy DRL algorithms that not only exceed state-of-the-art performance but are also simple and minimalistic.
no code implementations • 25 Sep 2019 • Che Wang, Yanqiu Wu, Quan Vuong, Keith Ross
The field of Deep Reinforcement Learning (DRL) has recently seen a surge in the popularity of maximum entropy reinforcement learning algorithms.