DECoVaC: Design of Experiments with Controlled Variability Components

no code implementations • 21 Sep 2019 • Thomas Boquet, Laure Delisle, Denis Kochetkov, Nathan Schucher, Parmida Atighehchian, Boris Oreshkin, Julien Cornebise

Reproducible research in Machine Learning has seen a salutary abundance of progress lately: workflows, transparency, and statistical analysis of validation and test performance.

BIG-bench Machine Learning Experimental Design

Fashion-Gen: The Generative Fashion Dataset and Challenge

3 code implementations • 21 Jun 2018 • Negar Rostamzadeh, Seyedarian Hosseini, Thomas Boquet, Wojciech Stokowiec, Ying Zhang, Christian Jauvin, Chris Pal

We introduce a new dataset of 293, 008 high definition (1360 x 1360 pixels) fashion images paired with item descriptions provided by professional stylists.

Image Generation

Uncertainty in Multitask Transfer Learning

no code implementations • ICLR 2019 • Alexandre Lacoste, Boris Oreshkin, Wonchang Chung, Thomas Boquet, Negar Rostamzadeh, David Krueger

The result is a rich and meaningful prior capable of few-shot learning on new tasks.

Few-Shot Learning Transfer Learning

Deep Prior

no code implementations • 13 Dec 2017 • Alexandre Lacoste, Thomas Boquet, Negar Rostamzadeh, Boris Oreshkin, Wonchang Chung, David Krueger

The recent literature on deep learning offers new tools to learn a rich probability distribution over high dimensional data such as images or sounds.