Croissant: A Metadata Format for ML-Ready Datasets

1 code implementation • 28 Mar 2024 • Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Joan Giner-Miguelez, Nitisha Jain, Michael Kuchnik, Quentin Lhoest, Pierre Marcenac, Manil Maskey, Peter Mattson, Luis Oala, Pierre Ruyssen, Rajat Shinde, Elena Simperl, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Steffen Vogler, Carole-Jean Wu

Data is a critical resource for Machine Learning (ML), yet working with data remains a key friction point.

Friction Management

Optimal Stopping via Randomized Neural Networks

2 code implementations • 28 Apr 2021 • Calypso Herrera, Florian Krach, Pierre Ruyssen, Josef Teichmann

This paper presents the benefits of using randomized neural networks instead of standard basis functions or deep neural networks to approximate the solutions of optimal stopping problems.

BIG-bench Machine Learning

Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices

1 code implementation • 28 Apr 2020 • Calypso Herrera, Florian Krach, Anastasis Kratsios, Pierre Ruyssen, Josef Teichmann

The robust PCA of covariance matrices plays an essential role when isolating key explanatory features.

A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark

2 code implementations • arXiv 2020 • Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, Neil Houlsby

And, how close are we to general visual representations?

Image Classification Representation Learning

The Visual Task Adaptation Benchmark

no code implementations • 25 Sep 2019 • Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, Neil Houlsby

Representation learning promises to unlock deep learning for the long tail of vision tasks without expansive labelled datasets.

Representation Learning