Search Results for author: Morgane Riviere

Found 5 papers, 3 papers with code

ASR4REAL: An extended benchmark for speech models

no code implementations16 Oct 2021 Morgane Riviere, Jade Copet, Gabriel Synnaeve

Popular ASR benchmarks such as Librispeech and Switchboard are limited in the diversity of settings and speakers they represent.

Benchmark Language Modelling

The Open Catalyst 2020 (OC20) Dataset and Community Challenges

2 code implementations20 Oct 2020 Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Aini Palizhati, Anuroop Sriram, Brandon Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, Zachary Ulissi

Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuels synthesis, long-term energy storage, and renewable fertilizer production.

An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage

no code implementations14 Oct 2020 C. Lawrence Zitnick, Lowik Chanussot, Abhishek Das, Siddharth Goyal, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Thibaut Lavril, Aini Palizhati, Morgane Riviere, Muhammed Shuaibi, Anuroop Sriram, Kevin Tran, Brandon Wood, Junwoong Yoon, Devi Parikh, Zachary Ulissi

As we increase our reliance on renewable energy sources such as wind and solar, which produce intermittent power, storage is needed to transfer power from times of peak generation to peak demand.

Inspirational Adversarial Image Generation

1 code implementation17 Jun 2019 Baptiste Rozière, Morgane Riviere, Olivier Teytaud, Jérémy Rapin, Yann Lecun, Camille Couprie

We design a simple optimization method to find the optimal latent parameters corresponding to the closest generation to any input inspirational image.

Image Generation

GDPP: Learning Diverse Generations Using Determinantal Point Process

4 code implementations30 Nov 2018 Mohamed Elfeki, Camille Couprie, Morgane Riviere, Mohamed Elhoseiny

Generative models have proven to be an outstanding tool for representing high-dimensional probability distributions and generating realistic-looking images.

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