11 citations · 25 across the 6 of their papers we have counts for
11 papers
Directed Graph Auto-Encoders
Georgios Kollias, Vasileios Kalantzis, Tsuyoshi Idé +2
We introduce a new class of auto-encoders for directed graphs, motivated by a direct extension of the Weisfeiler-Leman algorithm to pairs of node labels. The proposed model learns…
A Revision of Neural Tangent Kernel-based Approaches for Neural Networks
Kyung-Su Kim, Aurélie C. Lozano, Eunho Yang
Recent theoretical works based on the neural tangent kernel (NTK) have shed light on the optimization and generalization of over-parameterized networks, and partially bridge the ga…
A General Family of Stochastic Proximal Gradient Methods for Deep Learning
Jihun Yun, Aurelie C. Lozano, Eunho Yang
We study the training of regularized neural networks where the regularizer can be non-smooth and non-convex. We propose a unified framework for stochastic proximal gradient descent…
Stochastic Gradient Methods with Block Diagonal Matrix Adaptation
Jihun Yun, Aurelie C. Lozano, Eunho Yang
Adaptive gradient approaches that automatically adjust the learning rate on a per-feature basis have been very popular for training deep networks. This rich class of algorithms inc…
On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm
Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen +3
CLEVER (Cross-Lipschitz Extreme Value for nEtwork Robustness) is an Extreme Value Theory (EVT) based robustness score for large-scale deep neural networks (DNNs). In this paper, we…
M-estimation with the Trimmed l1 Penalty
Jihun Yun, Peng Zheng, Eunho Yang +2
We study high-dimensional estimators with the trimmed penalty, which leaves the largest parameter entries penalty-free. While optimization techniques for this nonconve…