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Found 5 papers, 4 papers with code
Lightweight Online Learning for Sets of Related Problems in Automated Reasoning
We present Self-Driven Strategy Learning ($\textit{sdsl}$), a lightweight online learning methodology for automated reasoning tasks that involve solving a set of related problems.
Lookahead Pathology in Monte-Carlo Tree Search
Monte-Carlo Tree Search (MCTS) is an adversarial search paradigm that first found prominence with its success in the domain of computer Go.
Implicit Quantile Neural Networks for Jet Simulation and Correction
Reliable modeling of conditional densities is important for quantitative scientific fields such as particle physics.
G2SAT: Learning to Generate SAT Formulas
The Boolean Satisfiability (SAT) problem is the canonical NP-complete problem and is fundamental to computer science, with a wide array of applications in planning, verification, and theorem proving.
Machine Learning Methods for Track Classification in the AT-TPC
We evaluate machine learning methods for event classification in the Active-Target Time Projection Chamber detector at the National Superconducting Cyclotron Laboratory (NSCL) at Michigan State University.
