232 citations · 462 across the 7 of their papers we have counts for
8 papers
Multi-Epoch Machine Learning 1: Unravelling Nature vs Nurture for Galaxy Formation
Robert McGibbon, Sadegh Khochfar
We present a novel machine learning method for predicting the baryonic properties of dark matter only subhalos from N-body simulations. Our model is built using the extremely rando…
Efficient hyperparameter optimization by way of PAC-Bayes bound minimization
John J. Cherian, Andrew G. Taube, Robert T. McGibbon +6
Identifying optimal values for a high-dimensional set of hyperparameters is a problem that has received growing attention given its importance to large-scale machine learning appli…
Theano: A Python framework for fast computation of mathematical expressions
The Theano Development Team, Rami Al-Rfou, Guillaume Alain +110
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has bee…
Identification of simple reaction coordinates from complex dynamics
Robert T. McGibbon, Brooke E. Husic, Vijay S. Pande
Reaction coordinates are widely used throughout chemical physics to model and understand complex chemical transformations. We introduce a definition of the natural reaction coordin…
Efficient maximum likelihood parameterization of continuous-time Markov processes
Robert T. McGibbon, Vijay S. Pande
Continuous-time Markov processes over finite state-spaces are widely used to model dynamical processes in many fields of natural and social science. Here, we introduce an maximum l…
Perspective: Markov Models for Long-Timescale Biomolecular Dynamics
Christian R. Schwantes, Robert T. McGibbon, Vijay S. Pande
Molecular dynamics simulations have the potential to provide atomic-level detail and insight to important questions in chemical physics that cannot be observed in typical experimen…
Variational cross-validation of slow dynamical modes in molecular kinetics
Robert T. McGibbon, Vijay S. Pande
Markov state models (MSMs) are a widely used method for approximating the eigenspectrum of the molecular dynamics propagator, yielding insight into the long-timescale statistical k…
Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models
Robert T. McGibbon, Bharath Ramsundar, Mohammad M. Sultan +2
We present a machine learning framework for modeling protein dynamics. Our approach uses L1-regularized, reversible hidden Markov models to understand large protein datasets genera…