44 citations · 94 across the 7 of their papers we have counts for
7 papers
COPRA: Conditional Parameter Adaptation with Reinforcement Learning for Video Anomaly Detection
Darryl Cherian Jacob, Xinyu Liu, Kai Wang +1
Vision-language models (VLMs) have shown strong performance in video anomaly detection (VAD) while providing interpretable predictions. However, existing VLM-based VAD methods suff…
OracleTSC: Oracle-Informed Reward Hurdle and Uncertainty Regularization for Traffic Signal Control
Darryl Jacob, Xinyu Liu, Muchao Ye +2
Transparent decision-making is essential for traffic signal control (TSC) systems to earn public trust. However, traditional reinforcement learning-based TSC methods function as bl…
OISD: On-Policy Internal Self-Distillation of Language Models
Xinyu Liu, Darryl Cherian Jacob, Yang Zhou +2
Recent reinforcement learning (RL) post-training approaches primarily optimize the final output policy using sparse outcome-level rewards, while largely overlooking predictive sign…
Understanding Real-World Traffic Safety through RoadSafe365 Benchmark
Xinyu Liu, Darryl C. Jacob, Yuxin Liu +4
Although recent traffic benchmarks have advanced multimodal data analysis, they generally lack systematic evaluation aligned with official safety standards. To fill this gap, we in…
Automated Design of Pulse Sequences for Magnetic Resonance Fingerprinting using Physics-Inspired Optimization
Stephen P. Jordan, Siyuan Hu, Ignacio Rozada +9
Magnetic Resonance Fingerprinting (MRF) is a method to extract quantitative tissue properties such as T1 and T2 relaxation rates from arbitrary pulse sequences using conventional m…
Feeding the multitude: A polynomial-time algorithm to improve sampling
Andrew J. Ochoa, Darryl C. Jacob, Salvatore Mandrà +1
A wide variety of optimization techniques, both exact and heuristic, tend to be biased samplers. This means that when attempting to find multiple uncorrelated solutions of a degene…
From Near to Eternity: Spin-glass planting, tiling puzzles, and constraint satisfaction problems
Firas Hamze, Darryl C. Jacob, Andrew J. Ochoa +3
We present a methodology for generating Ising Hamiltonians of tunable complexity and with a priori known ground states based on a decomposition of the model graph into edge-disjoin…