375 citations · 1.9k across the 50 of their papers we have counts for
15 papers · 1 filter
Tuning the Molecular Weight Distribution from Atom Transfer Radical Polymerization Using Deep Reinforcement Learning
Haichen Li, Christopher R. Collins, Thomas G. Ribelli +4
We devise a novel technique to control the shape of polymer molecular weight distributions (MWDs) in atom transfer radical polymerization (ATRP). This technique makes use of recent…
Learning General Latent-Variable Graphical Models with Predictive Belief Propagation
Borui Wang, Geoffrey Gordon
Learning general latent-variable probabilistic graphical models is a key theoretical challenge in machine learning and artificial intelligence. All previous methods, including the…
Learning Hidden Quantum Markov Models
Siddarth Srinivasan, Geoff Gordon, Byron Boots
Hidden Quantum Markov Models (HQMMs) can be thought of as quantum probabilistic graphical models that can model sequential data. We extend previous work on HQMMs with three contrib…
Unsupervised Learning for Nonlinear PieceWise Smooth Hybrid Systems
Gilwoo Lee, Zita Marinho, Aaron M. Johnson +3
This paper introduces a novel system identification and tracking method for PieceWise Smooth (PWS) nonlinear stochastic hybrid systems. We are able to correctly identify and track…
Predictive State Recurrent Neural Networks
Carlton Downey, Ahmed Hefny, Boyue Li +2
We present a new model, Predictive State Recurrent Neural Networks (PSRNNs), for filtering and prediction in dynamical systems. PSRNNs draw on insights from both Recurrent Neural N…
Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint
Han Zhao, Geoff Gordon
Symmetric nonnegative matrix factorization has found abundant applications in various domains by providing a symmetric low-rank decomposition of nonnegative matrices. In this paper…