Search Results for author: Victor Schmidt

Found 11 papers, 7 papers with code

torchgfn: A PyTorch GFlowNet library

2 code implementations24 May 2023 Salem Lahlou, Joseph D. Viviano, Victor Schmidt, Yoshua Bengio

The growing popularity of generative flow networks (GFlowNets or GFNs) from a range of researchers with diverse backgrounds and areas of expertise necessitates a library which facilitates the testing of new features such as training losses that can be easily compared to standard benchmark implementations, or on a set of common environments.

FAENet: Frame Averaging Equivariant GNN for Materials Modeling

1 code implementation28 Apr 2023 Alexandre Duval, Victor Schmidt, Alex Hernandez Garcia, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, David Rolnick

Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to specific symmetries.

PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design

2 code implementations22 Nov 2022 Alexandre Duval, Victor Schmidt, Santiago Miret, Yoshua Bengio, Alex Hernández-García, David Rolnick

Catalyst materials play a crucial role in the electrochemical reactions involved in numerous industrial processes key to this transition, such as renewable energy storage and electrofuel synthesis.

Predicting Infectiousness for Proactive Contact Tracing

1 code implementation ICLR 2021 Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Muller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilanuik, David Buckeridge, Gáetan Marceau Caron, Pierre-Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams

Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT).

Image-to-image Mapping with Many Domains by Sparse Attribute Transfer

no code implementations23 Jun 2020 Matthew Amodio, Rim Assouel, Victor Schmidt, Tristan Sylvain, Smita Krishnaswamy, Yoshua Bengio

Unsupervised image-to-image translation consists of learning a pair of mappings between two domains without known pairwise correspondences between points.

Translation Unsupervised Image-To-Image Translation

Towards Lifelong Self-Supervision For Unpaired Image-to-Image Translation

1 code implementation31 Mar 2020 Victor Schmidt, Makesh Narsimhan Sreedhar, Mostafa ElAraby, Irina Rish

Unpaired Image-to-Image Translation (I2IT) tasks often suffer from lack of data, a problem which self-supervised learning (SSL) has recently been very popular and successful at tackling.

Colorization Continual Learning +3

Using Simulated Data to Generate Images of Climate Change

no code implementations26 Jan 2020 Gautier Cosne, Adrien Juraver, Mélisande Teng, Victor Schmidt, Vahe Vardanyan, Alexandra Luccioni, Yoshua Bengio

In our paper, we explore the potential of using images from a simulated 3D environment to improve a domain adaptation task carried out by the MUNIT architecture, aiming to use the resulting images to raise awareness of the potential future impacts of climate change.

Domain Adaptation

Quantifying the Carbon Emissions of Machine Learning

1 code implementation21 Oct 2019 Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, Thomas Dandres

From an environmental standpoint, there are a few crucial aspects of training a neural network that have a major impact on the quantity of carbon that it emits.

BIG-bench Machine Learning

Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks

no code implementations2 May 2019 Victor Schmidt, Alexandra Luccioni, S. Karthik Mukkavilli, Narmada Balasooriya, Kris Sankaran, Jennifer Chayes, Yoshua Bengio

We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs).

Cannot find the paper you are looking for? You can Submit a new open access paper.