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01/04/2023
fintech-kMC: Agent based simulations of financial platforms for design and testing of machine learning systems
We discuss our simulation tool, fintech-kMC, which is designed to genera...
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05/16/2022
Training neural networks using Metropolis Monte Carlo and an adaptive variant
We examine the zero-temperature Metropolis Monte Carlo algorithm as a to...
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05/09/2022
Machine Learning Diffusion Monte Carlo Energy Densities
We present two machine learning methodologies which are capable of predi...
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04/05/2022
Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge
Deploying generative machine learning techniques to generate novel chemi...
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03/10/2022
Cellular automata can classify data by inducing trajectory phase coexistence
We show that cellular automata can classify data by inducing a form of d...
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02/17/2022
Learning stochastic dynamics and predicting emergent behavior using transformers
We show that a neural network originally designed for language processin...
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06/11/2021
Twin Neural Network Regression is a Semi-Supervised Regression Algorithm
Twin neural network regression (TNNR) is a semi-supervised regression al...
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03/05/2021
Golem: An algorithm for robust experiment and process optimization
Numerous challenges in science and engineering can be framed as optimiza...
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02/23/2021
Weakly-supervised multi-class object localization using only object counts as labels
We demonstrate the use of an extensive deep neural network to localize i...
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01/12/2021
Interpretable discovery of new semiconductors with machine learning
Machine learning models of materials^1-5 accelerate discovery compared t...
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12/29/2020
Twin Neural Network Regression
We introduce twin neural network (TNN) regression. This method predicts ...
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12/22/2020
Neuroevolutionary learning of particles and protocols for self-assembly
Within simulations of molecules deposited on a surface we show that neur...
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11/17/2020
Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz
We use a neural network ansatz originally designed for the variational o...
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10/27/2020
Scientific intuition inspired by machine learning generated hypotheses
Machine learning with application to questions in the physical sciences ...
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08/15/2020
Correspondence between neuroevolution and gradient descent
We show analytically that training a neural network by stochastic mutati...
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05/26/2020
Active Measure Reinforcement Learning for Observation Cost Minimization
Standard reinforcement learning (RL) algorithms assume that the observat...
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04/15/2020
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment
Reinforcement learning (RL) has been demonstrated to have great potentia...
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03/03/2020
Watch and learn – a generalized approach for transferrable learning in deep neural networks via physical principles
Transfer learning refers to the use of knowledge gained while solving a ...
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12/18/2019
Learning to grow: control of materials self-assembly using evolutionary reinforcement learning
We show that neural networks trained by evolutionary reinforcement learn...
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09/02/2019
Evolutionary reinforcement learning of dynamical large deviations
We show how to calculate dynamical large deviations using evolutionary r...
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03/20/2019