Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability

no code implementations • ICML 2020 • Mingjie Li, Lingshen He, Zhouchen Lin

By viewing ResNet as an explicit Euler discretization of an ordinary differential equation (ODE), for the first time, we find that the adversarial robustness of ResNet is connected to the numerical stability of the corresponding dynamic system.

Adversarial Attack Adversarial Robustness

Efficient Equivariant Network

1 code implementation • NeurIPS 2021 • Lingshen He, Yuxuan Chen, Zhengyang Shen, Yiming Dong, Yisen Wang, Zhouchen Lin

Group equivariant CNNs (G-CNNs) that incorporate more equivariance can significantly improve the performance of conventional CNNs.

Gauge Equivariant Transformer

no code implementations • NeurIPS 2021 • Lingshen He, Yiming Dong, Yisen Wang, DaCheng Tao, Zhouchen Lin

Attention mechanism has shown great performance and efficiency in a lot of deep learning models, in which relative position encoding plays a crucial role.

Position

PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions

3 code implementations • ICML 2020 • Zhengyang Shen, Lingshen He, Zhouchen Lin, Jinwen Ma

In implementation, we discretize the system using the numerical schemes of PDOs, deriving approximately equivariant convolutions (PDO-eConvs).

Image Classification Rotated MNIST