2021

A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks 2021-07-02

Deep Gaussian Processes 2021-06-26

Test-Time Training with Self-Supervision for Generalization under Distribution Shifts 2021-06-09

Hopfield Networks is All You Need 2021-05-12

Generalized Sliced Wasserstein Distances 2021-05-08

Differentiable Particle Filtering via Entropy-Regularized Optimal Transport 2021-04-29

Telescoping Density-Ratio Estimation 2021-04-21

The Thermodynamic Variational Objective 2021-04-14

A Shooting Formulation of Deep Learning 2021-04-07

Sequential Neural Posterior and Likelihood Approximation 2021-03-31

NeuralFDR: Learning Discovery Thresholds from Hypothesis Features 2021-03-03

How to Train your Energy-Based Models 2021-02-24

Neural Word Embedding as Implicit Matrix Factorization 2021-02-10

Deep learning of contagion dynamics on complex networks 2021-02-03

2020

Stochastic Normalizing Flows 2020-12-14

Toward a theory of optimization for over-parameterized systems of non-linear equations: the lessons of deep learning 2020-12-07

Estimation under invariant distributions 2020-11-30

Neural Tangent Kernel: Convergence and Generalization in Neural Networks 2020-11-23

Analyzing Inverse Problems with Invertible Neural Networks 2020-11-16

Learning Latent Subspaces in Variational Autoencoders 2020-11-09

Likelihood-free MCMC with Amortized Approximate Ratio Estimators 2020-11-02

Adversarial Variational Bayes 2020-10-28

Learning in Implicit Generative Models 2020-10-19

Variational Inference with Normalizing Flows 2020-07-28

Neural Processes 2020-07-14

Auto-Encoding Variational Bayes 2020-07-07

Mining gold from implicit models to improve likelihood-free inference 2020-06-30

Stein Variational Gradient Descent as Gradient Flow 2020-06-25

Adaptive approximation and estimation of deep neural network to intrinsic dimensionality 2020-06-16

From optimal transport to generative modeling: the VEGAN cookbook 2020-06-09

MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference 2020-06-02

Preventing Posterior Collapse with delta-VAEs 2020-05-26

Deep Learning for Multi-Scale Changepoint Detection in Multivariate Time Series 2020-05-05

Markov Chain Monte Carlo and Variational Inference: Bridging the Gap 2020-04-28

Information Bottleneck 2020-04-23

Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights 2020-04-14

Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation 2020-04-07

Neural Ordinary Differential Equations 2020-03-24

Uniform convergence may be unable to explain generalization in deep learning 2020-03-10

Nonparametric regression using deep neural networks with ReLU activation function 2020-02-27

2019

Deep Convolutional Networks 2019-12-12

Deep Boltzmann Machines 2019-12-02

Welcome to Neural Networks reading group website! 2019-10-11