Search Results for author:
Found 27 papers, 8 papers with code
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 generate synthetic data for machine learning model development and testing.
Training neural networks using Metropolis Monte Carlo and an adaptive variant
We examine the zero-temperature Metropolis Monte Carlo algorithm as a tool for training a neural network by minimizing a loss function.
Machine Learning Diffusion Monte Carlo Energies
We present two machine learning methodologies that are capable of predicting diffusion Monte Carlo (DMC) energies with small datasets (~60 DMC calculations in total).
Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge
Deploying generative machine learning techniques to generate novel chemical structures based on molecular fingerprint representation has been well established in molecular design.
Cellular automata can classify data by inducing trajectory phase coexistence
We show that cellular automata can classify data by inducing a form of dynamical phase coexistence.
Learning stochastic dynamics and predicting emergent behavior using transformers
We show that a neural network originally designed for language processing can learn the dynamical rules of a stochastic system by observation of a single dynamical trajectory of the system, and can accurately predict its emergent behavior under conditions not observed during training.
Dynamic programming with incomplete information to overcome navigational uncertainty in a nautical environment
Using a novel toy nautical navigation environment, we show that dynamic programming can be used when only incomplete information about a partially observed Markov decision process (POMDP) is known.
Scientific Discovery and the Cost of Measurement -- Balancing Information and Cost in Reinforcement Learning
The use of reinforcement learning (RL) in scientific applications, such as materials design and automated chemistry, is increasing.
Twin Neural Network Regression is a Semi-Supervised Regression Algorithm
Twin neural network regression (TNNR) is a semi-supervised regression algorithm, it can be trained on unlabelled data points as long as other, labelled anchor data points, are present.
Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep Learning
Hyperspectral stimulated Raman scattering (SRS) microscopy is a label-free technique for biomedical and mineralogical imaging which can suffer from low signal to noise ratios.
Golem: An algorithm for robust experiment and process optimization
Design of experiment and optimization algorithms are often adopted to solve these tasks efficiently.
Weakly-supervised multi-class object localization using only object counts as labels
Using images labelled with only the counts of the objects present, the structure of the extensive deep neural network can be exploited to perform localization of the objects within the visual field.
Interpretable discovery of new semiconductors with machine learning
To provide guidance in experimental materials synthesis, these need to be coupled with an accurate yet effective search algorithm and training data consistent with experimental observations.
Twin Neural Network Regression
The solution of a traditional regression problem is then obtained by averaging over an ensemble of all predicted differences between the targets of an unseen data point and all training data points.
Neuroevolutionary learning of particles and protocols for self-assembly
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 input from physical concepts such as thermal equilibrium or mechanical stability and without prior knowledge of candidate or competing structures.
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 optimization of quantum systems to study dynamical large deviations in classical ones.
Scientific intuition inspired by machine learning generated hypotheses
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 many areas.
Correspondence between neuroevolution and gradient descent
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 descent on the loss function in the presence of Gaussian white noise.
Active Measure Reinforcement Learning for Observation Cost Minimization
Our empirical evaluation demonstrates that Amrl-Q agents are able to learn a policy and state estimator in parallel during online training.
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment
Reinforcement learning (RL) has been demonstrated to have great potential in many applications of scientific discovery and design.
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 machine learning task and applying it to the solution of a closely related problem.
Learning to grow: control of material self-assembly using evolutionary reinforcement learning
We show that neural networks trained by evolutionary reinforcement learning can enact efficient molecular self-assembly protocols.
Evolutionary reinforcement learning of dynamical large deviations
We show how to calculate the likelihood of dynamical large deviations using evolutionary reinforcement learning.
Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning
Gradient-based reinforcement learning is able to learn the Stirling cycle, whereas an evolutionary approach achieves the optimal Carnot cycle.
Phase space sampling and operator confidence with generative adversarial networks
We demonstrate that a generative adversarial network can be trained to produce Ising model configurations in distinct regions of phase space.
Statistical Mechanics
Extensive deep neural networks for transferring small scale learning to large scale systems
We demonstrate the application of EDNNs to three physical systems: the Ising model and two hexagonal/graphene-like datasets.
Computational Physics Materials Science
Deep learning and the Schrödinger equation
We have trained a deep (convolutional) neural network to predict the ground-state energy of an electron in four classes of confining two-dimensional electrostatic potentials.
