Deep Learning
On the generalization of learning algorithms that do not converge
Generalization analyses of deep learning typically assume that the training converges to a fixed point.
A Quantitative Geometric Approach to Neural-Network Smoothness
We implement the algorithms induced from this quantitative geometric approach, which are based on semidefinite programming (SDP).
Distilling Representations from GAN Generator via Squeeze and Span Y u Yang
In particular, we present a novel "squeeze-and-span" technique to distill knowledge from a generator
Jesse Mu
One common answer is that an agent should be rewarded for attaining "novel" states in the environment, but naive measures of novelty have
Randomized Channel Shuffling: Minimal-Overhead Backdoor Attack Detection without Clean Datasets
Deep neural networks (DNNs) typically require massive data to train on, which is a hurdle for numerous practical domains.