
Twin Neural Network Regression is a SemiSupervised Regression Algorithm
Twin neural network regression (TNNR) is a semisupervised regression al...
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Golem: An algorithm for robust experiment and process optimization
Numerous challenges in science and engineering can be framed as optimiza...
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Weaklysupervised multiclass 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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Interpretable discovery of new semiconductors with machine learning
Machine learning models of materials^15 accelerate discovery compared t...
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Twin Neural Network Regression
We introduce twin neural network (TNN) regression. This method predicts ...
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Neuroevolutionary learning of particles and protocols for selfassembly
Within simulations of molecules deposited on a surface we show that neur...
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Dynamical large deviations of twodimensional kinetically constrained models using a neuralnetwork state ansatz
We use a neural network ansatz originally designed for the variational o...
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Scientific intuition inspired by machine learning generated hypotheses
Machine learning with application to questions in the physical sciences ...
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Correspondence between neuroevolution and gradient descent
We show analytically that training a neural network by stochastic mutati...
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Active Measure Reinforcement Learning for Observation Cost Minimization
Standard reinforcement learning (RL) algorithms assume that the observat...
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Reinforcement Learning in a PhysicsInspired SemiMarkov Environment
Reinforcement learning (RL) has been demonstrated to have great potentia...
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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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Learning to grow: control of materials selfassembly using evolutionary reinforcement learning
We show that neural networks trained by evolutionary reinforcement learn...
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Evolutionary reinforcement learning of dynamical large deviations
We show how to calculate dynamical large deviations using evolutionary r...
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Optimizing thermodynamic trajectories using evolutionary reinforcement learning
Using a model heat engine we show that neural networkbased reinforcemen...
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Isaac Tamblyn
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