245 citations · 783 across the 82 of their papers we have counts for
7 papers · 1 filter
Learnable Splines for Unstructured Neural Operators
Vignesh Gopakumar, Zhuoyuan Wang, Ander Gray +12
Neural operators approximate solution operators of partial differential equations at a fraction of the cost of numerical solvers. Efficient grid-based architectures such as the Fou…
PTNO: Training Neural Operators with Noisy Monte Carlo Estimates for Particle Transport Problems
Yubo Cao, Xi Deng, Mengqi Xia +3
Particle transport under multiple scattering is central to radiative transfer and plasma physics, yet high-fidelity Monte Carlo (MC) simulations must trace prohibitively many parti…
GeoWind2Plan: Mission-Time 3D Urban Wind Prediction for Energy-Efficient UAV Planning
Shaoxiang Qin, Yucheng Zhao, Fuyuan Lyu +6
In urban low-altitude flight, buildings reshape ambient wind into spatially varying 3D flow, making unmanned aerial vehicle (UAV) energy depend on local wind exposure as well as pa…
The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs
Pengrui Han, Rafal Kocielnik, Peiyang Song +4
Personality traits have long been studied as predictors of human behavior. Recent advances in Large Language Models (LLMs) suggest similar patterns may emerge in artificial systems…
LeanProgress: Guiding Search for Neural Theorem Proving via Proof Progress Prediction
Robert Joseph George, Suozhi Huang, Peiyang Song +1
Mathematical reasoning remains a significant challenge for Large Language Models (LLMs) due to hallucinations. When combined with formal proof assistants like Lean, these hallucina…
Lean Copilot: Large Language Models as Copilots for Theorem Proving in Lean
Peiyang Song, Kaiyu Yang, Anima Anandkumar
Neural theorem proving combines large language models (LLMs) with proof assistants such as Lean, where the correctness of formal proofs can be rigorously verified, leaving no room…
Perspectives on the State and Future of Deep Learning - 2023
Micah Goldblum, Anima Anandkumar, Richard Baraniuk +7
The goal of this series is to chronicle opinions and issues in the field of machine learning as they stand today and as they change over time. The plan is to host this survey perio…