no code implementations • 1 Jan 2021 • Hanlin Zhu, Chengyang Ying, Song Zuo
Recent theoretical analysis suggests that ultra-wide neural networks always converge to global minima near the initialization under first order methods.
no code implementations • ICML Workshop AML 2021 • Chengyang Ying, Xinning Zhou, Dong Yan, Jun Zhu
Though deep reinforcement learning (DRL) has obtained substantial success, it may encounter catastrophic failures due to the intrinsic uncertainty caused by stochastic policies and environment variability.
no code implementations • ICML Workshop AML 2021 • You Qiaoben, Xinning Zhou, Chengyang Ying, Jun Zhu
Deep reinforcement learning (DRL) policies are vulnerable to the adversarial attack on their observations, which may mislead real-world RL agents to catastrophic failures.
no code implementations • 30 Jun 2021 • You Qiaoben, Chengyang Ying, Xinning Zhou, Hang Su, Jun Zhu, Bo Zhang
In this paper, we provide a framework to better understand the existing methods by reformulating the problem of adversarial attacks on reinforcement learning in the function space.
1 code implementation • 9 Jun 2022 • Chengyang Ying, Xinning Zhou, Hang Su, Dong Yan, Ning Chen, Jun Zhu
Though deep reinforcement learning (DRL) has obtained substantial success, it may encounter catastrophic failures due to the intrinsic uncertainty of both transition and observation.
no code implementations • 9 Jun 2022 • Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jun Zhu, Jian Song
Under the GSmooth framework, we present a scalable algorithm that uses a surrogate image-to-image network to approximate the complex transformation.
1 code implementation • 12 Jun 2022 • Chengyang Ying, You Qiaoben, Xinning Zhou, Hang Su, Wenbo Ding, Jianyong Ai
Among different adversarial noises, universal adversarial perturbations (UAP), i. e., a constant image-agnostic perturbation applied on every input frame of the agent, play a critical role in Embodied Vision Navigation since they are computation-efficient and application-practical during the attack.
no code implementations • 15 Sep 2022 • Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Jian Song, Ze Cheng
In this paper, we present a novel bi-level optimization framework to resolve the challenge by decoupling the optimization of the targets and constraints.
1 code implementation • 15 Sep 2022 • Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Dong Yan, Jun Zhu
In this paper, we reveal that the instability is also related to a new notion of Reuse Bias of IS -- the bias in off-policy evaluation caused by the reuse of the replay buffer for evaluation and optimization.
1 code implementation • 29 Sep 2022 • Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su, Jun Zhu
To address this problem, we adopt a generative approach by decoupling the learned policy into two parts: an expressive generative behavior model and an action evaluation model.
1 code implementation • 6 Oct 2022 • Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Ze Cheng
We present a unified hard-constraint framework for solving geometrically complex PDEs with neural networks, where the most commonly used Dirichlet, Neumann, and Robin boundary conditions (BCs) are considered.
1 code implementation • 15 Nov 2022 • Zhongkai Hao, Songming Liu, Yichi Zhang, Chengyang Ying, Yao Feng, Hang Su, Jun Zhu
Recent work shows that it provides potential benefits for machine learning models by incorporating the physical prior and collected data, which makes the intersection of machine learning and physics become a prevailing paradigm.
2 code implementations • 28 Feb 2023 • Zhongkai Hao, Zhengyi Wang, Hang Su, Chengyang Ying, Yinpeng Dong, Songming Liu, Ze Cheng, Jian Song, Jun Zhu
However, there are several challenges for learning operators in practical applications like the irregular mesh, multiple input functions, and complexity of the PDEs' solution.
no code implementations • 9 Mar 2023 • Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Songming Liu, Dong Yan, Jun Zhu
Extensive experiments in both image-based and state-based tasks show that TAD can significantly improve the performance of handling different tasks simultaneously, especially for those with high TDR, and display a strong generalization ability to unseen tasks.
1 code implementation • 30 May 2023 • Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Ze Cheng, Jun Zhu
The neural operator has emerged as a powerful tool in learning mappings between function spaces in PDEs.
1 code implementation • 6 Mar 2024 • Zhongkai Hao, Chang Su, Songming Liu, Julius Berner, Chengyang Ying, Hang Su, Anima Anandkumar, Jian Song, Jun Zhu
Pre-training has been investigated to improve the efficiency and performance of training neural operators in data-scarce settings.