Alex Hernández-García

Trans. Mach. Learn. Res., 2024

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

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2024

Improved Off-policy Reinforcement Learning in Biological Sequence Design.

[BibT_eX]

[DOI]

Jinkyoo Park

CoRR, 2024

2023

Hard-Constrained Deep Learning for Climate Downscaling.

[BibT_eX]

[DOI]

Paula Harder

J. Mach. Learn. Res., 2023

Towards equilibrium molecular conformation generation with GFlowNets.

[BibT_eX]

[DOI]

Alexandra Volokhova

Michal Koziarski

CoRR, 2023

On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions.

[BibT_eX]

[DOI]

CoRR, 2023

Crystal-GFN: sampling crystals with desirable properties and constraints.

[BibT_eX]

[DOI]

CoRR, 2023

Multi-variable Hard Physical Constraints for Climate Model Downscaling.

[BibT_eX]

[DOI]

Jose González-Abad

Paula Harder

José Manuel Gutiérrez Llorente

CoRR, 2023

Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling.

[BibT_eX]

[DOI]

Qidong Yang

CoRR, 2023

Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning.

[BibT_eX]

[DOI]

Alexandra Sasha Luccioni

CoRR, 2023

GFlowNets for AI-Driven Scientific Discovery.

[BibT_eX]

[DOI]

CoRR, 2023

A theory of continuous generative flow networks.

[BibT_eX]

[DOI]

Léna Néhale Ezzine

Nikolay Malkin

Proceedings of the International Conference on Machine Learning, 2023

Multi-Objective GFlowNets.

[BibT_eX]

[DOI]

Moksh Jain

Sharath Chandra Raparthy

Proceedings of the International Conference on Machine Learning, 2023

FAENet: Frame Averaging Equivariant GNN for Materials Modeling.

[BibT_eX]

[DOI]

Alexandre Duval

Victor Schmidt

Santiago Miret

Fragkiskos D. Malliaros

Proceedings of the International Conference on Machine Learning, 2023

2022

Generating physically-consistent high-resolution climate data with hard-constrained neural networks.

[BibT_eX]

[DOI]

Campbell D. Watson

Daniela Szwarcman

David S. Rolnick

CoRR, 2022

Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning.

[BibT_eX]

[DOI]

Siba Moussa

Michael Kilgour

Clara Jans

Miroslava Cuperlovic-Culf

Lena Simine

CoRR, 2022

Biological Sequence Design with GFlowNets.

[BibT_eX]

[DOI]

Moksh Jain

Emmanuel Bengio

Jarrid Rector-Brooks

Bonaventure F. P. Dossou

Proceedings of the International Conference on Machine Learning, 2022

ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Preface.

[BibT_eX]

[DOI]

Samuel Albanie

João F. Henriques

Luca Bertinetto

Hazel Doughty

Gül Varol

Proceedings of the NeurIPS 2021 Workshop on Pre-Registration in Machine Learning, 2021

2020

Data augmentation and image understanding.

[BibT_eX]

[DOI]

PhD thesis, 2020

Data augmentation and image understanding.

[BibT_eX]

[DOI]

CoRR, 2020

Rethinking supervised learning: insights from biological learning and from calling it by its name.

[BibT_eX]

[DOI]

CoRR, 2020

2019

Learning robust visual representations using data augmentation invariance.

[BibT_eX]

[DOI]

Tim C. Kietzmann

CoRR, 2019

Learning Representational Invariance Instead of Categorization.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshops, 2019

2018

Data augmentation instead of explicit regularization.

[BibT_eX]

[DOI]

CoRR, 2018

Do deep nets really need weight decay and dropout?

[BibT_eX]

[DOI]

CoRR, 2018

Further Advantages of Data Augmentation on Convolutional Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2018, 2018

2017

Perceived emotion from images through deep neural networks.

[BibT_eX]

[DOI]