175 citations · 1.1k across the 47 of their papers we have counts for
10 papers · 1 filter
Twin Neural Network Regression
Sebastian J. Wetzel, Kevin Ryczko, Roger G. Melko +1
We introduce twin neural network (TNN) regression. This method predicts differences between the target values of two different data points rather than the targets themselves. The s…
Neuroevolutionary learning of particles and protocols for self-assembly
Stephen Whitelam, Isaac Tamblyn
Within simulations of molecules deposited on a surface we show that neuroevolutionary learning can design particles and time-dependent protocols to promote self-assembly, without i…
Scientific intuition inspired by machine learning generated hypotheses
Pascal Friederich, Mario Krenn, Isaac Tamblyn +1
Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in…
Correspondence between neuroevolution and gradient descent
Stephen Whitelam, Viktor Selin, Sang-Won Park +1
We show analytically that training a neural network by conditioned stochastic mutation or neuroevolution of its weights is equivalent, in the limit of small mutations, to gradient…
Inverse Design of a Graphene-Based Quantum Transducer via Neuroevolution
Kevin Ryczko, Pierre Darancet, Isaac Tamblyn
We introduce an inverse design framework based on artificial neural networks, genetic algorithms, and tight-binding calculations, capable to optimize the very large configuration s…
Active Measure Reinforcement Learning for Observation Cost Minimization
Colin Bellinger, Rory Coles, Mark Crowley +1
Standard reinforcement learning (RL) algorithms assume that the observation of the next state comes instantaneously and at no cost. In a wide variety of sequential decision making…
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment
Colin Bellinger, Rory Coles, Mark Crowley +1
Reinforcement learning (RL) has been demonstrated to have great potential in many applications of scientific discovery and design. Recent work includes, for example, the design of…
Watch and learn -- a generalized approach for transferrable learning in deep neural networks via physical principles
Kyle Sprague, Juan Carrasquilla, Steve Whitelam +1
Transfer learning refers to the use of knowledge gained while solving a machine learning task and applying it to the solution of a closely related problem. Such an approach has ena…
Controlled Online Optimization Learning (COOL): Finding the ground state of spin Hamiltonians with reinforcement learning
Kyle Mills, Pooya Ronagh, Isaac Tamblyn
Reinforcement learning (RL) has become a proven method for optimizing a procedure for which success has been defined, but the specific actions needed to achieve it have not. We app…
Optical lattice experiments at unobserved conditions and scales through generative adversarial deep learning
Corneel Casert, Kyle Mills, Tom Vieijra +2
Machine learning provides a novel avenue for the study of experimental realizations of many-body systems, and has recently been proven successful in analyzing properties of experim…