62 citations · 84 across the 6 of their papers we have counts for
4 papers · 1 filter
Shallow Univariate ReLu Networks as Splines: Initialization, Loss Surface, Hessian, & Gradient Flow Dynamics
Justin Sahs, Ryan Pyle, Aneel Damaraju +4
Understanding the learning dynamics and inductive bias of neural networks (NNs) is hindered by the opacity of the relationship between NN parameters and the function represented. W…
An Improved Semi-Supervised VAE for Learning Disentangled Representations
Weili Nie, Zichao Wang, Ankit B. Patel +1
Learning interpretable and disentangled representations is a crucial yet challenging task in representation learning. In this work, we focus on semi-supervised disentanglement lear…
Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks
Joshua J. Michalenko, Ameesh Shah, Abhinav Verma +3
We investigate the internal representations that a recurrent neural network (RNN) uses while learning to recognize a regular formal language. Specifically, we train a RNN on positi…
Training Neural Networks Without Gradients: A Scalable ADMM Approach
Gavin Taylor, Ryan Burmeister, Zheng Xu +3
With the growing importance of large network models and enormous training datasets, GPUs have become increasingly necessary to train neural networks. This is largely because conven…