Filtering Variational Objectives

NeurIPS 2017 • tensorflow/models •

When used as a surrogate objective for maximum likelihood estimation in latent variable models, the evidence lower bound (ELBO) produces state-of-the-art results.

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models

NeurIPS 2017 • tensorflow/models •

Learning in models with discrete latent variables is challenging due to high variance gradient estimators.

LightGBM: A Highly Efficient Gradient Boosting Decision Tree

NeurIPS 2017 • Microsoft/LightGBM

We prove that, since the data instances with larger gradients play a more important role in the computation of information gain, GOSS can obtain quite accurate estimation of the information gain with a much smaller data size.

Attention Is All You Need

NeurIPS 2017 • tensorflow/tensor2tensor •

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.

CONSTITUENCY PARSING MACHINE TRANSLATION

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

NeurIPS 2017 • jantic/DeOldify •

Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible.

IMAGE GENERATION

A Unified Approach to Interpreting Model Predictions

NeurIPS 2017 • slundberg/shap •

Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications.

FEATURE IMPORTANCE INTERPRETABLE MACHINE LEARNING

Convolutional Gaussian Processes

NeurIPS 2017 • pyro-ppl/pyro •

We present a practical way of introducing convolutional structure into Gaussian processes, making them more suited to high-dimensional inputs like images.

Doubly Stochastic Variational Inference for Deep Gaussian Processes

NeurIPS 2017 • pyro-ppl/pyro •

Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice.

Improved Training of Wasserstein GANs

NeurIPS 2017 • tensorpack/tensorpack •

Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability.

CONDITIONAL IMAGE GENERATION

Dynamic Routing Between Capsules

NeurIPS 2017 • naturomics/CapsNet-Tensorflow •

We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters.

IMAGE CLASSIFICATION