Search Results for author:
Found 12 papers, 6 papers with code
On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions
The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorporate the geometric configuration of all atoms.
Crystal-GFN: sampling crystals with desirable properties and constraints
Accelerating material discovery holds the potential to greatly help mitigate the climate crisis.
Multi-Fidelity Active Learning with GFlowNets
For example, in scientific discovery, we are often faced with the problem of exploring very large, high-dimensional spaces, where querying a high fidelity, black-box objective function is very expensive.
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling
In this work, we propose a downscaling method based on the Fourier neural operator.
Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning
Machine learning (ML) requires using energy to carry out computations during the model training process.
GFlowNets for AI-Driven Scientific Discovery
However, in order to truly leverage large-scale data sets and high-throughput experimental setups, machine learning methods will need to be further improved and better integrated in the scientific discovery pipeline.
Multi-Objective GFlowNets
We study the problem of generating diverse candidates in the context of Multi-Objective Optimization.
Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning
Inverse design of short single-stranded RNA and DNA sequences (aptamers) is the task of finding sequences that satisfy a set of desired criteria.
Hard-Constrained Deep Learning for Climate Downscaling
In order to conserve physical quantities, here we introduce methods that guarantee statistical constraints are satisfied by a deep learning downscaling model, while also improving their performance according to traditional metrics.
ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
Climate change is a major threat to humanity, and the actions required to prevent its catastrophic consequences include changes in both policy-making and individual behaviour.
Data augmentation and image understanding
Throughout this dissertation, I use these insights to analyse data augmentation as a particularly useful inductive bias, a more effective regularisation method for artificial neural networks, and as the framework to analyse and improve the invariance of vision models to perceptually plausible transformations.
Rethinking supervised learning: insights from biological learning and from calling it by its name
The renaissance of artificial neural networks was catalysed by the success of classification models, tagged by the community with the broader term supervised learning.
