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Datasets

Greatest papers with code

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.

LATENT VARIABLE MODELS

Neural Variational Inference and Learning in Belief Networks

31 Jan 2014tensorflow/models

Highly expressive directed latent variable models, such as sigmoid belief networks, are difficult to train on large datasets because exact inference in them is intractable and none of the approximate inference methods that have been applied to them scale well.

LATENT VARIABLE MODELS VARIATIONAL INFERENCE

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.

LATENT VARIABLE MODELS

Simple and Effective Noisy Channel Modeling for Neural Machine Translation

IJCNLP 2019 pytorch/fairseq

Previous work on neural noisy channel modeling relied on latent variable models that incrementally process the source and target sentence.

LATENT VARIABLE MODELS MACHINE TRANSLATION

On the Discrepancy between Density Estimation and Sequence Generation

17 Feb 2020tensorflow/tensor2tensor

In this paper, by comparing several density estimators on five machine translation tasks, we find that the correlation between rankings of models based on log-likelihood and BLEU varies significantly depending on the range of the model families being compared.

DENSITY ESTIMATION LATENT VARIABLE MODELS MACHINE TRANSLATION STRUCTURED PREDICTION

Neural Ordinary Differential Equations

NeurIPS 2018 rtqichen/torchdiffeq

Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network.

LATENT VARIABLE MODELS MULTIVARIATE TIME SERIES FORECASTING MULTIVARIATE TIME SERIES IMPUTATION

Gaussian Processes for Big Data

26 Sep 2013cornellius-gp/gpytorch

We introduce stochastic variational inference for Gaussian process models.

GAUSSIAN PROCESSES LATENT VARIABLE MODELS VARIATIONAL INFERENCE

Neural Processes

4 Jul 2018deepmind/neural-processes

A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision.

LATENT VARIABLE MODELS

Neural Variational Inference for Text Processing

19 Nov 2015carpedm20/variational-text-tensorflow

We validate this framework on two very different text modelling applications, generative document modelling and supervised question answering.

ANSWER SELECTION LATENT VARIABLE MODELS TOPIC MODELS VARIATIONAL INFERENCE

Adversarially Regularized Autoencoders

ICML 2018 jakezhaojb/ARAE

This adversarially regularized autoencoder (ARAE) allows us to generate natural textual outputs as well as perform manipulations in the latent space to induce change in the output space.

LATENT VARIABLE MODELS REPRESENTATION LEARNING STYLE TRANSFER