1.4k citations · 1.6k across the 6 of their papers we have counts for
12 papers
A Generalized Lottery Ticket Hypothesis
Ibrahim Alabdulmohsin, Larisa Markeeva, Daniel Keysers +1
We introduce a generalization to the lottery ticket hypothesis in which the notion of "sparsity" is relaxed by choosing an arbitrary basis in the space of parameters. We present ev…
MLP-Mixer: An all-MLP Architecture for Vision
Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov +9
Convolutional Neural Networks (CNNs) are the go-to model for computer vision. Recently, attention-based networks, such as the Vision Transformer, have also become popular. In this…
What Do Neural Networks Learn When Trained With Random Labels?
Hartmut Maennel, Ibrahim Alabdulmohsin, Ilya Tolstikhin +4
We study deep neural networks (DNNs) trained on natural image data with entirely random labels. Despite its popularity in the literature, where it is often used to study memorizati…
Predicting Neural Network Accuracy from Weights
Thomas Unterthiner, Daniel Keysers, Sylvain Gelly +2
We show experimentally that the accuracy of a trained neural network can be predicted surprisingly well by looking only at its weights, without evaluating it on input data. We moti…
Practical and Consistent Estimation of f-Divergences
Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga +2
The estimation of an f-divergence between two probability distributions based on samples is a fundamental problem in statistics and machine learning. Most works study this problem…
GeNet: Deep Representations for Metagenomics
Mateo Rojas-Carulla, Ilya Tolstikhin, Guillermo Luque +3
We introduce GeNet, a method for shotgun metagenomic classification from raw DNA sequences that exploits the known hierarchical structure between labels for training. We provide a…