Isaac Tamblyn | DeepAI

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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...

0 Isaac Tamblyn, et al. ∙

∙ 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...

0 Stephen Whitelam, et al. ∙

∙ 05/09/2022

Machine Learning Diffusion Monte Carlo Energy Densities

We present two machine learning methodologies which are capable of predi...

0 Kevin Ryczko, et al. ∙

∙ 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...

0 Mohammad Sajjad Ghaemi, et al. ∙

∙ 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...

0 Stephen Whitelam, et al. ∙

∙ 02/17/2022

Learning stochastic dynamics and predicting emergent behavior using transformers

We show that a neural network originally designed for language processin...

0 Corneel Casert, et al. ∙

∙ 06/11/2021

Twin Neural Network Regression is a Semi-Supervised Regression Algorithm

Twin neural network regression (TNNR) is a semi-supervised regression al...

0 Sebastian J. Wetzel, et al. ∙

∙ 03/05/2021

Golem: An algorithm for robust experiment and process optimization

Numerous challenges in science and engineering can be framed as optimiza...

43 Matteo Aldeghi, et al. ∙

∙ 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...

14 Kyle Mills, et al. ∙

∙ 01/12/2021

Interpretable discovery of new semiconductors with machine learning

Machine learning models of materials^1-5 accelerate discovery compared t...

0 Hitarth Choubisa, et al. ∙

∙ 12/29/2020

Twin Neural Network Regression

We introduce twin neural network (TNN) regression. This method predicts ...

0 Sebastian J. Wetzel, et al. ∙

∙ 12/22/2020

Neuroevolutionary learning of particles and protocols for self-assembly

Within simulations of molecules deposited on a surface we show that neur...

34 Stephen Whitelam, et al. ∙

∙ 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...

0 Corneel Casert, et al. ∙

∙ 10/27/2020

Scientific intuition inspired by machine learning generated hypotheses

Machine learning with application to questions in the physical sciences ...

0 Pascal Friederich, et al. ∙

∙ 08/15/2020

Correspondence between neuroevolution and gradient descent

We show analytically that training a neural network by stochastic mutati...

0 Stephen Whitelam, et al. ∙

∙ 05/26/2020

Active Measure Reinforcement Learning for Observation Cost Minimization

Standard reinforcement learning (RL) algorithms assume that the observat...

0 Colin Bellinger, et al. ∙

∙ 04/15/2020

Reinforcement Learning in a Physics-Inspired Semi-Markov Environment

Reinforcement learning (RL) has been demonstrated to have great potentia...

0 Colin Bellinger, et al. ∙

∙ 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 ...

0 Kyle Sprague, et al. ∙

∙ 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...

21 Stephen Whitelam, et al. ∙

∙ 09/02/2019

Evolutionary reinforcement learning of dynamical large deviations

We show how to calculate dynamical large deviations using evolutionary r...

0 Stephen Whitelam, et al. ∙

∙ 03/20/2019

Optimizing thermodynamic trajectories using evolutionary reinforcement learning

Using a model heat engine we show that neural network-based reinforcemen...

0 Chris Beeler, et al. ∙