2 citations · 2 across the 4 of their papers we have counts for
4 papers
Smoothness Errors in Dynamics Models and How to Avoid Them
Edward Berman, Luisa Li, Jung Yeon Park +1
Modern neural networks have shown promise for solving partial differential equations over surfaces, often by discretizing the surface as a mesh and learning with a mesh-aware graph…
On Uncertainty Calibration for Equivariant Functions
Edward Berman, Jacob Ginesin, Marco Pacini +1
Data-sparse settings such as robotic manipulation, molecular physics, and galaxy morphology classification are some of the hardest domains for deep learning. For these problems, eq…
The State of Julia for Scientific Machine Learning
Edward Berman, Jacob Ginesin
Julia has been heralded as a potential successor to Python for scientific machine learning and numerical computing, boasting ergonomic and performance improvements. Since Julia's i…
Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions
Federico Cassano, Luisa Li, Akul Sethi +8
A significant amount of research is focused on developing and evaluating large language models for a variety of code synthesis tasks. These include synthesizing code from natural l…