Search Results for author: Jiameng Fan

Found 6 papers, 5 papers with code

POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems

1 code implementation31 Mar 2023 YiXuan Wang, Weichao Zhou, Jiameng Fan, Zhilu Wang, Jiajun Li, Xin Chen, Chao Huang, Wenchao Li, Qi Zhu

We also present a novel approach to propagate TMs more efficiently and precisely across ReLU activation functions.

POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems

2 code implementations25 Jun 2021 Chao Huang, Jiameng Fan, Zhilu Wang, YiXuan Wang, Weichao Zhou, Jiajun Li, Xin Chen, Wenchao Li, Qi Zhu

We present POLAR, a polynomial arithmetic-based framework for efficient bounded-time reachability analysis of neural-network controlled systems (NNCSs).

DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck

1 code implementation26 Feb 2021 Jiameng Fan, Wenchao Li

This approach enables us to train high-performance policies that are robust to visual distractions and can generalize well to unseen environments.

reinforcement-learning Reinforcement Learning (RL) +1

Adversarial Training and Provable Robustness: A Tale of Two Objectives

1 code implementation13 Aug 2020 Jiameng Fan, Wenchao Li

We propose a principled framework that combines adversarial training and provable robustness verification for training certifiably robust neural networks.

Vocal Bursts Valence Prediction

ReachNN: Reachability Analysis of Neural-Network Controlled Systems

1 code implementation25 Jun 2019 Chao Huang, Jiameng Fan, Wenchao Li, Xin Chen, Qi Zhu

In this work, we propose a new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions, i. e., as long as they ensure that the neural networks are Lipschitz continuous.

Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation

no code implementations6 Mar 2019 Jiameng Fan, Wenchao Li

An important facet of reinforcement learning (RL) has to do with how the agent goes about exploring the environment.

reinforcement-learning Reinforcement Learning (RL) +1

Cannot find the paper you are looking for? You can Submit a new open access paper.