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03/18/2021
Lossless compression with state space models using bits back coding
We generalize the 'bits back with ANS' method to time-series models with...
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07/08/2020
Diverse Ensembles Improve Calibration
Modern deep neural networks can produce badly calibrated predictions, es...
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06/16/2020
Density Deconvolution with Normalizing Flows
Density deconvolution is the task of estimating a probability density fu...
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06/15/2020
Ordering Dimensions with Nested Dropout Normalizing Flows
The latent space of normalizing flows must be of the same dimensionality...
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02/10/2020
On Contrastive Learning for Likelihood-free Inference
Likelihood-free methods perform parameter inference in stochastic simula...
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11/26/2019
Scalable Extreme Deconvolution
The Extreme Deconvolution method fits a probability density to a dataset...
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07/29/2019
CloudLSTM: A Recurrent Neural Model for Spatiotemporal Point-cloud Stream Forecasting
This paper introduces CloudLSTM, a new branch of recurrent neural networ...
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06/10/2019
Neural Spline Flows
A normalizing flow models a complex probability density as an invertible...
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06/05/2019
Cubic-Spline Flows
A normalizing flow models a complex probability density as an invertible...
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04/17/2019
Dynamic Evaluation of Transformer Language Models
This research note combines two methods that have recently improved the ...
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02/07/2019
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning
Multi-task learning allows the sharing of useful information between mul...
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11/29/2018
Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting
Modern deep neural network models suffer from adversarial examples, i.e....
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11/21/2018
Sequential Neural Methods for Likelihood-free Inference
Likelihood-free inference refers to inference when a likelihood function...
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10/12/2018
Mode Normalization
Normalization methods are a central building block in the deep learning ...
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05/18/2018
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
We present Sequential Neural Likelihood (SNL), a new method for Bayesian...
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11/13/2017
Model Criticism in Latent Space
Model criticism is usually carried out by assessing if replicated data g...
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09/21/2017
Dynamic Evaluation of Neural Sequence Models
We present methodology for using dynamic evaluation to improve neural se...
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09/11/2017
A determinant-free method to simulate the parameters of large Gaussian fields
We propose a determinant-free approach for simulation-based Bayesian inf...
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05/19/2017
Masked Autoregressive Flow for Density Estimation
Autoregressive models are among the best performing neural density estim...
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10/15/2016
Markov Chain Truncation for Doubly-Intractable Inference
Computing partition functions, the normalizing constants of probability ...
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09/26/2016
Multiplicative LSTM for sequence modelling
We introduce multiplicative LSTM (mLSTM), a recurrent neural network arc...
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05/20/2016
Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation
Many statistical models can be simulated forwards but have intractable l...
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05/07/2016
Neural Autoregressive Distribution Estimation
We present Neural Autoregressive Distribution Estimation (NADE) models, ...
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02/24/2016
Differentiation of the Cholesky decomposition
We review strategies for differentiating matrix-based computations, and ...
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02/12/2015
MADE: Masked Autoencoder for Distribution Estimation
There has been a lot of recent interest in designing neural network mode...
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08/09/2014
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes
Probabilistic matrix factorization (PMF) is a powerful method for modeli...
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10/07/2013
A Deep and Tractable Density Estimator
The Neural Autoregressive Distribution Estimator (NADE) and its real-val...
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06/02/2013
RNADE: The real-valued neural autoregressive density-estimator
We introduce RNADE, a new model for joint density estimation of real-val...
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10/28/2012
Parallel MCMC with Generalized Elliptical Slice Sampling
Probabilistic models are conceptually powerful tools for finding structu...
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07/11/2012
Bayesian Learning in Undirected Graphical Models: Approximate MCMC algorithms
Bayesian learning in undirected graphical models|computing posterior dis...
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05/29/2012
A Framework for Evaluating Approximation Methods for Gaussian Process Regression
Gaussian process (GP) predictors are an important component of many Baye...
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12/31/2009