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08/31/2023
Natural Quantum Monte Carlo Computation of Excited States
We present a variational Monte Carlo algorithm for estimating the lowest...
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05/11/2023
Neural Wave Functions for Superfluids
Understanding superfluidity remains a major goal of condensed matter phy...
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11/24/2022
A Self-Attention Ansatz for Ab-initio Quantum Chemistry
We present a novel neural network architecture using self-attention, the...
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08/26/2022
Ab-initio quantum chemistry with neural-network wavefunctions
Machine learning and specifically deep-learning methods have outperforme...
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12/03/2020
Integrable Nonparametric Flows
We introduce a method for reconstructing an infinitesimal normalizing fl...
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11/13/2020
Better, Faster Fermionic Neural Networks
The Fermionic Neural Network (FermiNet) is a recently-developed neural n...
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06/23/2020
Disentangling by Subspace Diffusion
We present a novel nonparametric algorithm for symmetry-based disentangl...
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09/05/2019
Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks
Given access to accurate solutions of the many-electron Schrödinger equa...
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12/05/2018
Towards a Definition of Disentangled Representations
How can intelligent agents solve a diverse set of tasks in a data-effici...
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06/06/2018
Spectral Inference Networks: Unifying Spectral Methods With Deep Learning
We present Spectral Inference Networks, a framework for learning eigenfu...
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11/07/2016
Unrolled Generative Adversarial Networks
We introduce a method to stabilize Generative Adversarial Networks (GANs...
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10/06/2016
Connecting Generative Adversarial Networks and Actor-Critic Methods
Both generative adversarial networks (GAN) in unsupervised learning and ...
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06/14/2016
Learning to learn by gradient descent by gradient descent
The move from hand-designed features to learned features in machine lear...
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06/08/2016