33 citations · 84 across the 12 of their papers we have counts for
12 papers
How Architecture and Training Affect TPC Representations Across Experiments
Tyler Wheeler, Michelle P. Kuchera, Raghuram Ramanujan +12
Deep-learning efforts have increasingly shifted toward foundation model approaches. In experimental physics, this allows models and learned representations to be reused beyond the…
Forward versus Backward: Comparing Reasoning Objectives in Direct Preference Optimization
Murtaza Nikzad, Raghuram Ramanujan
Large language models exhibit impressive reasoning capabilities yet frequently generate plausible but incorrect solutions, a phenomenon commonly termed hallucination. This paper in…
Sparse Methods for Vector Embeddings of TPC Data
Tyler Wheeler, Michelle P. Kuchera, Raghuram Ramanujan +7
Time Projection Chambers (TPCs) are versatile detectors that reconstruct charged-particle tracks in an ionizing medium, enabling sensitive measurements across a wide range of nucle…
Unpaired Translation of Point Clouds for Modeling Detector Response
Mingyang Li, Michelle Kuchera, Raghuram Ramanujan +3
Modeling detector response is a key challenge in time projection chambers. We cast this problem as an unpaired point cloud translation task, between data collected from simulations…
Implicit Quantile Networks For Emulation in Jet Physics
B. Kronheim, A. Al Kadhim, M. P. Kuchera +2
The ability to model and sample from conditional densities is important in many physics applications. Implicit quantile networks (IQN) have been successfully applied to this task i…
Lightweight Online Learning for Sets of Related Problems in Automated Reasoning
Haoze Wu, Christopher Hahn, Florian Lonsing +3
We present Self-Driven Strategy Learning (), a lightweight online learning methodology for automated reasoning tasks that involve solving a set of related problems.…
Lookahead Pathology in Monte-Carlo Tree Search
Khoi P. N. Nguyen, Raghuram Ramanujan
Monte-Carlo Tree Search (MCTS) is a search paradigm that first found prominence with its success in the domain of computer Go. Early theoretical work established the soundness and…
Two-dimensional total absorption spectroscopy with conditional generative adversarial networks
Cade Dembski, Michelle P. Kuchera, Sean Liddick +2
We explore the use of machine learning techniques to remove the response of large volume -ray detectors from experimental spectra. Segmented -ray total absorption spectromete…
Implicit Quantile Neural Networks for Jet Simulation and Correction
Braden Kronheim, Michelle P. Kuchera, Harrison B. Prosper +1
Reliable modeling of conditional densities is important for quantitative scientific fields such as particle physics. In domains outside physics, implicit quantile neural networks (…
G2SAT: Learning to Generate SAT Formulas
Jiaxuan You, Haoze Wu, Clark Barrett +2
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, a…
Machine Learning Methods for Track Classification in the AT-TPC
Michelle P. Kuchera, Raghuram Ramanujan, Jack Z. Taylor +4
We evaluate machine learning methods for event classification in the Active-Target Time Projection Chamber detector at the National Superconducting Cyclotron Laboratory (NSCL) at M…
Understanding Sampling Style Adversarial Search Methods
Raghuram Ramanujan, Ashish Sabharwal, Bart Selman
UCT has recently emerged as an exciting new adversarial reasoning technique based on cleverly balancing exploration and exploitation in a Monte-Carlo sampling setting. It has been…