1 citations · 1 across the 2 of their papers we have counts for
5 papers
A Stochastic Bundle Method for Interpolating Networks
Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel +1
We propose a novel method for training deep neural networks that are capable of interpolation, that is, driving the empirical loss to zero. At each iteration, our method constructs…
Comment on Stochastic Polyak Step-Size: Performance of ALI-G
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
This is a short note on the performance of the ALI-G algorithm (Berrada et al., 2020) as reported in (Loizou et al., 2021). ALI-G (Berrada et al., 2020) and SPS (Loizou et al., 202…
Training Neural Networks for and by Interpolation
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
In modern supervised learning, many deep neural networks are able to interpolate the data: the empirical loss can be driven to near zero on all samples simultaneously. In this work…
Deep Frank-Wolfe For Neural Network Optimization
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
Learning a deep neural network requires solving a challenging optimization problem: it is a high-dimensional, non-convex and non-smooth minimization problem with a large number of…
Smooth Loss Functions for Deep Top-k Classification
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
The top-k error is a common measure of performance in machine learning and computer vision. In practice, top-k classification is typically performed with deep neural networks train…