1 citations · 1 across the 5 of their papers we have counts for
5 papers
Discovering High Level Patterns from Simulation Traces
Sean Memery, Kartic Subr
Large Language Models (LLMs) are unable to reliably reason about specific physical systems. Attempts to imbue LLMs with knowledge of the necessary physics concepts have shown great…
xInv: Explainable Optimization of Inverse Problems
Sean Memery, Kevin Denamganai, Anna Kapron-King +1
Inverse problems are central to a wide range of fields, including healthcare, climate science, and agriculture. They involve the estimation of inputs, typically via iterative optim…
CueTip: An Interactive and Explainable Physics-aware Pool Assistant
Sean Memery, Kevin Denamganai, Jiaxin Zhang +3
We present an interactive and explainable automated coaching assistant called CueTip for a variant of pool/billiards. CueTip's novelty lies in its combination of three features: a…
SimLM: Can Language Models Infer Parameters of Physical Systems?
Sean Memery, Mirella Lapata, Kartic Subr
Several machine learning methods aim to learn or reason about complex physical systems. A common first-step towards reasoning is to infer system parameters from observations of its…
Generating Parametric BRDFs from Natural Language Descriptions
Sean Memery, Osmar Cedron, Kartic Subr
Artistic authoring of 3D environments is a laborious enterprise that also requires skilled content creators. There have been impressive improvements in using machine learning to ad…