Search Results for author:
Found 11 papers, 7 papers with code
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism
We present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks.
Self-Supervised Representation Learning for Astronomical Images
We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks.
Estimating Galactic Distances From Images Using Self-supervised Representation Learning
We use a contrastive self-supervised learning framework to estimate distances to galaxies from their photometric images.
Fast, high-fidelity Lyman $α$ forests with convolutional neural networks
Full-physics cosmological simulations are powerful tools for studying the formation and evolution of structure in the universe but require extreme computational resources.
Mining for Strong Gravitational Lenses with Self-supervised Learning
We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' Data Release 9.
Self-supervised similarity search for large scientific datasets
We present the use of self-supervised learning to explore and exploit large unlabeled datasets.
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor.
FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators
Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe.
Generative Modeling of High-resolution Global Precipitation Forecasts
Forecasting global precipitation patterns and, in particular, extreme precipitation events is of critical importance to preparing for and adapting to climate change.
Towards Stability of Autoregressive Neural Operators
Neural operators have proven to be a promising approach for modeling spatiotemporal systems in the physical sciences.
A Practical Probabilistic Benchmark for AI Weather Models
We also reveal how multiple time-step loss functions, which many data-driven weather models have employed, are counter-productive: they improve deterministic metrics at the cost of increased dissipation, deteriorating probabilistic skill.
