no code implementations • 22 Mar 2024 • Zeliang Zhang, Mingqian Feng, Jinyang Jiang, Rongyi Zhu, Yijie Peng, Chenliang Xu
Gradient-based saliency maps are widely used to explain deep neural network decisions.
no code implementations • 18 Mar 2024 • Zeliang Zhang, Jinyang Jiang, Zhuo Liu, Susan Liang, Yijie Peng, Chenliang Xu
In this paper, we introduce an approximation technique for the likelihood ratio (LR) method to alleviate computational and memory demands in gradient estimation.
no code implementations • 1 Mar 2024 • Jinyang Jiang, Xiaotian Liu, Tao Ren, Qinghao Wang, Yi Zheng, Yufu Du, Yijie Peng, Cheng Zhang
We introduce a deep reinforcement learning (DRL) approach for solving management problems including inventory management, dynamic pricing, and recommendation.
no code implementations • 11 Feb 2024 • Tao Ren, Ruihan Zhou, Jinyang Jiang, Jiafeng Liang, Qinghao Wang, Yijie Peng
Existing alpha mining only employs the neural network agent, unable to utilize the structural information of the solution space.
no code implementations • 15 May 2023 • Jinyang Jiang, Zeliang Zhang, Chenliang Xu, Zhaofei Yu, Yijie Peng
While backpropagation (BP) is the mainstream approach for gradient computation in neural network training, its heavy reliance on the chain rule of differentiation constrains the designing flexibility of network architecture and training pipelines.
1 code implementation • 12 May 2023 • Jinyang Jiang, Jiaqiao Hu, Yijie Peng
Classical reinforcement learning (RL) aims to optimize the expected cumulative reward.
no code implementations • 17 Feb 2023 • Zeliang Zhang, Jinyang Jiang, Minjie Chen, Zhiyuan Wang, Yijie Peng, Zhaofei Yu
Noise injection-based method has been shown to be able to improve the robustness of artificial neural networks in previous work.
no code implementations • 27 Jan 2022 • Jinyang Jiang, Jiaqiao Hu, Yijie Peng
Classical reinforcement learning (RL) aims to optimize the expected cumulative rewards.