Search Results for author:
Found 5 papers, 2 papers with code
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.
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.
