
Hierarchical FewShot Generative Models
A fewshot generative model should be able to generate data from a distr...
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Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks
Datadriven methods based on machine learning have the potential to acce...
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Representation Learning for OutOfDistribution Generalization in Reinforcement Learning
Learning data representations that are useful for various downstream tas...
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Generalization and Robustness Implications in ObjectCentric Learning
The idea behind objectcentric representation learning is that natural s...
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On the Transfer of Disentangled Representations in Realistic Settings
Learning meaningful representations that disentangle the underlying stru...
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Optimal Variance Control of the Score Function Gradient Estimator for Importance Weighted Bounds
This paper introduces novel results for the score function gradient esti...
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SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
Normalizing flows and variational autoencoders are powerful generative m...
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Closing the Dequantization Gap: PixelCNN as a SingleLayer Flow
Flow models have recently made great progress at modeling quantized sens...
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LAVAE: Disentangling Location and Appearance
We propose a probabilistic generative model for unsupervised learning of...
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BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
With the introduction of the variational autoencoder (VAE), probabilisti...
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Attend, Copy, Parse  Endtoend information extraction from documents
Document information extraction tasks performed by humans create data co...
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Recurrent Relational Networks for Complex Relational Reasoning
Humans possess an ability to abstractly reason about objects and their i...
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A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
This paper takes a step towards temporal reasoning in a dynamically chan...
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Hash Embeddings for Efficient Word Representations
We present hash embeddings, an efficient method for representing words i...
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CloudScan  A configurationfree invoice analysis system using recurrent neural networks
We present CloudScan; an invoice analysis system that requires zero conf...
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EndtoEnd Information Extraction without TokenLevel Supervision
Most stateoftheart information extraction approaches rely on tokenle...
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SemiSupervised Generation with Clusteraware Generative Models
Deep generative models trained with large amounts of unlabelled data hav...
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Neural Machine Translation with Characters and Hierarchical Encoding
Most existing Neural Machine Translation models use groups of characters...
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Sequential Neural Models with Stochastic Layers
How can we efficiently propagate uncertainty in a latent state represent...
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An Adaptive ResampleMove Algorithm for Estimating Normalizing Constants
The estimation of normalizing constants is a fundamental step in probabi...
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Auxiliary Deep Generative Models
Deep generative models parameterized by neural networks have recently ac...
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Ladder Variational Autoencoders
Variational Autoencoders are powerful models for unsupervised learning. ...
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Autoencoding beyond pixels using a learned similarity metric
We present an autoencoder that leverages learned representations to bett...
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Bayesian inference for spatiotemporal spikeandslab priors
In this work, we address the problem of solving a series of underdetermi...
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Spatiotemporal Spike and Slab Priors for Multiple Measurement Vector Problems
We are interested in solving the multiple measurement vector (MMV) probl...
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Convolutional LSTM Networks for Subcellular Localization of Proteins
Machine learning is widely used to analyze biological sequence data. Non...
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Deep Belief Nets for Topic Modeling
Applying traditional collaborative filtering to digital publishing is ch...
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Protein Secondary Structure Prediction with Long Short Term Memory Networks
Prediction of protein secondary structure from the amino acid sequence i...
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Bayesian leaveoneout crossvalidation approximations for Gaussian latent variable models
The future predictive performance of a Bayesian model can be estimated u...
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Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models
Expectation Propagation (EP) provides a framework for approximate infere...
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Predictive Active Set Selection Methods for Gaussian Processes
We propose an active set selection framework for Gaussian process classi...
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