Neural Network

Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability

We explore the relationship between the adversarial robustness and numerical stability. Furthermore, we propose IE-Skips, which is a modification of the vaniila skip connections for Residual Network Families inspired by the implicit Euler method and we also theoretically and exmperimently prove the advantages of our structure under adversarial attacks.

PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions

We employ PDOs as steerable filters to design a system equivariant to E(n). We use numerical schemes to discretize this system and obtain PDO-eConvs, which achieve a quadratic order equivariance approximation in the discrete domain.

Neural Ordinary Differential Equations with Envolutionary Weights

Neural networks have been very successful in many learning tasks, for their powerful ability to fit the data. Recently, to understand the success of neural networks, much attention has been paid to the relationship between differential equations and …

Differentiable Linearized ADMM

We propose D-LADMM, which is a K-layer LADMM inspired deep neural network and rigorously prove that there exist a set of learnable parameters for D-LADMM to generate globally converged solutions.

Deep Comprehensive Correlation Mining for Image Clustering

Recent developed deep unsupervised methods allow us to jointly learn representation and cluster unlabelled data. These deep clustering methods mainly focus on the correlation among samples, e.g., selecting high precision pairs to gradually tune the …