56 citations · 117 across the 16 of their papers we have counts for
6 papers · 1 filter
Density estimation with LLMs: a geometric investigation of in-context learning trajectories
Toni J. B. Liu, Nicolas Boullé, Raphaël Sarfati +1
Large language models (LLMs) demonstrate remarkable emergent abilities to perform in-context learning across various tasks, including time series forecasting. This work investigate…
Lines of Thought in Large Language Models
Raphaël Sarfati, Toni J. B. Liu, Nicolas Boullé +1
Large Language Models achieve next-token prediction by transporting a vectorized piece of text (prompt) across an accompanying embedding space under the action of successive transf…
LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law
Toni J. B. Liu, Nicolas Boullé, Raphaël Sarfati +1
Pretrained large language models (LLMs) are surprisingly effective at performing zero-shot tasks, including time-series forecasting. However, understanding the mechanisms behind su…
PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited Data
Robert Stephany, Christopher Earls
In this paper, we introduce PDE-LEARN, a novel deep learning algorithm that can identify governing partial differential equations (PDEs) directly from noisy, limited measurements o…
PDE-READ: Human-readable Partial Differential Equation Discovery using Deep Learning
Robert Stephany, Christopher Earls
PDE discovery shows promise for uncovering predictive models of complex physical systems but has difficulty when measurements are sparse and noisy. We introduce a new approach for…
Data-driven discovery of Green's functions with human-understandable deep learning
Nicolas Boullé, Christopher J. Earls, Alex Townsend
There is an opportunity for deep learning to revolutionize science and technology by revealing its findings in a human interpretable manner. To do this, we develop a novel data-dri…