350 citations · 670 across the 15 of their papers we have counts for
4 papers · 1 filter
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning
Mohammadamin Tavakoli, Yin Ting T. Chiu, Alexander Shmakov +3
Deep learning-based reaction predictors have undergone significant architectural evolution. However, their reliance on reactions from the US Patent Office results in a lack of inte…
Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actors
Kolby Nottingham, Yasaman Razeghi, Kyungmin Kim +4
Large language models (LLMs) are being applied as actors for sequential decision making tasks in domains such as robotics and games, utilizing their general world knowledge and pla…
End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics
Alexander Shmakov, Kevin Greif, Michael Fenton +3
High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into open questions in particle physics. However, detector effects must be corrected before meas…
Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers
Alexander Shmakov, Alejandro Yankelevich, Jianming Bian +1
The complex events observed at the NOvA long-baseline neutrino oscillation experiment contain vital information for understanding the most elusive particles in the standard model.…