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08/02/2023
Multi-variable Hard Physical Constraints for Climate Model Downscaling
Global Climate Models (GCMs) are the primary tool to simulate climate ev...
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06/20/2023
Multi-Fidelity Active Learning with GFlowNets
In the last decades, the capacity to generate large amounts of data in s...
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05/23/2023
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling
Climate simulations are essential in guiding our understanding of climat...
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02/16/2023
Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning
Machine learning (ML) requires using energy to carry out computations du...
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02/01/2023
GFlowNets for AI-Driven Scientific Discovery
Tackling the most pressing problems for humanity, such as the climate cr...
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01/30/2023
A theory of continuous generative flow networks
Generative flow networks (GFlowNets) are amortized variational inference...
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11/22/2022
PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design
Mitigating the climate crisis requires a rapid transition towards lower ...
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10/23/2022
Multi-Objective GFlowNets
In many applications of machine learning, like drug discovery and materi...
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08/10/2022
Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning
Inverse design of short single-stranded RNA and DNA sequences (aptamers)...
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08/08/2022
Generating physically-consistent high-resolution climate data with hard-constrained neural networks
The availability of reliable, high-resolution climate and weather data i...
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10/06/2021
ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
Climate change is a major threat to humanity, and the actions required t...
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12/28/2020
Data augmentation and image understanding
Interdisciplinary research is often at the core of scientific progress. ...
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12/04/2020
Rethinking supervised learning: insights from biological learning and from calling it by its name
The renaissance of artificial neural networks was catalysed by the succe...
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06/26/2019
Further advantages of data augmentation on convolutional neural networks
Data augmentation is a popular technique largely used to enhance the tra...
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06/11/2019
Learning robust visual representations using data augmentation invariance
Deep convolutional neural networks trained for image object categorizati...
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06/11/2018
Data augmentation instead of explicit regularization
Modern deep artificial neural networks have achieved impressive results ...
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02/20/2018