744 citations · 7.6k across the 130 of their papers we have counts for
5 papers · 1 filter
Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior
Shashank Subramanian, Peter Harrington, Kurt Keutzer +4
Pre-trained machine learning (ML) models have shown great performance for a wide range of applications, in particular in natural language processing (NLP) and computer vision (CV).…
Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence
Ashesh Chattopadhyay, Jaideep Pathak, Ebrahim Nabizadeh +2
Recent years have seen a surge in interest in building deep learning-based fully data-driven models for weather prediction. Such deep learning models if trained on observations can…
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Atılım Güneş Baydin, Lei Shao, Wahid Bhimji +14
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models. However, applications to science remai…
Graph Neural Networks for IceCube Signal Classification
Nicholas Choma, Federico Monti, Lisa Gerhardt +7
Tasks involving the analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly develop…
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Atılım Güneş Baydin, Lukas Heinrich, Wahid Bhimji +12
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which all…