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Found 17 papers, 10 papers with code
Accurate Computation of Quantum Excited States with Neural Networks
We present a variational Monte Carlo algorithm for estimating the lowest excited states of a quantum system which is a natural generalization of the estimation of ground states.
Neural Wave Functions for Superfluids
We demonstrate key limitations of the FermiNet Ansatz in studying the unitary Fermi gas and propose a simple modification based on the idea of an antisymmetric geminal power singlet (AGPs) wave function.
A Self-Attention Ansatz for Ab-initio Quantum Chemistry
In recent years, deep neural networks like the FermiNet and PauliNet have been used to significantly improve the accuracy of these first-principle calculations, but they lack an attention-like mechanism for gating interactions between electrons.
Ab-initio quantum chemistry with neural-network wavefunctions
Machine learning and specifically deep-learning methods have outperformed human capabilities in many pattern recognition and data processing problems, in game playing, and now also play an increasingly important role in scientific discovery.
Integrable Nonparametric Flows
This reverses the conventional task of normalizing flows -- rather than being given samples from a unknown target distribution and learning a flow that approximates the distribution, we are given a perturbation to an initial distribution and aim to reconstruct a flow that would generate samples from the known perturbed distribution.
Better, Faster Fermionic Neural Networks
The Fermionic Neural Network (FermiNet) is a recently-developed neural network architecture that can be used as a wavefunction Ansatz for many-electron systems, and has already demonstrated high accuracy on small systems.
Disentangling by Subspace Diffusion
We present a novel nonparametric algorithm for symmetry-based disentangling of data manifolds, the Geometric Manifold Component Estimator (GEOMANCER).
Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks
Here we introduce a novel deep learning architecture, the Fermionic Neural Network, as a powerful wavefunction Ansatz for many-electron systems.
Towards a Definition of Disentangled Representations
Here we propose that a principled solution to characterising disentangled representations can be found by focusing on the transformation properties of the world.
Spectral Inference Networks: Unifying Deep and Spectral Learning
We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization.
Unrolled Generative Adversarial Networks
We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator.
Connecting Generative Adversarial Networks and Actor-Critic Methods
Both generative adversarial networks (GAN) in unsupervised learning and actor-critic methods in reinforcement learning (RL) have gained a reputation for being difficult to optimize.
Learning to learn by gradient descent by gradient descent
The move from hand-designed features to learned features in machine learning has been wildly successful.
Convolution by Evolution: Differentiable Pattern Producing Networks
In this work we introduce a differentiable version of the Compositional Pattern Producing Network, called the DPPN.
A structured matrix factorization framework for large scale calcium imaging data analysis
We present a structured matrix factorization approach to analyzing calcium imaging recordings of large neuronal ensembles.
Neurons and Cognition Quantitative Methods Applications
Robust learning of low-dimensional dynamics from large neural ensembles
We show on model data that the parameters of latent linear dynamical systems can be recovered, and that even if the dynamics are not stationary we can still recover the true latent subspace.
Probabilistic Deterministic Infinite Automata
We suggest that our method for averaging over PDFAs is a novel approach to predictive distribution smoothing.
