155 citations · 1.6k across the 76 of their papers we have counts for
15 papers · 1 filter
Specific Algorithmic Interpretability of Neural Networks: A Case Study on Textures
Yanglin Zhang, Anneke von Seeger, Gilad Lerman +1
We develop a principled framework for constructing neural networks whose specific parameter realizations admit an explicit algorithmic interpretation. Existing algorithm-inspired a…
Stein Discrepancy for Unsupervised Domain Adaptation
Anneke von Seeger, Dongmian Zou, Gilad Lerman
Unsupervised domain adaptation (UDA) aims to improve model performance on an unlabeled target domain using a related, labeled source domain. A common approach aligns source and tar…
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization
Yifei Yang, Wonjun Lee, Dongmian Zou +1
Hyperbolic representations have shown remarkable efficacy in modeling inherent hierarchies and complexities within data structures. Hyperbolic neural networks have been commonly ap…
The effect of Leaky ReLUs on the training and generalization of overparameterized networks
Yinglong Guo, Shaohan Li, Gilad Lerman
We investigate the training and generalization errors of overparameterized neural networks (NNs) with a wide class of leaky rectified linear unit (ReLU) functions. More specificall…
Monotone Generative Modeling via a Gromov-Monge Embedding
Wonjun Lee, Yifei Yang, Dongmian Zou +1
Generative adversarial networks (GANs) are popular for generative tasks; however, they often require careful architecture selection, extensive empirical tuning, and are prone to mo…
An Unpooling Layer for Graph Generation
Yinglong Guo, Dongmian Zou, Gilad Lerman
We propose a novel and trainable graph unpooling layer for effective graph generation. Given a graph with features, the unpooling layer enlarges this graph and learns its desired n…