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07/13/2023
Sequential Monte Carlo Learning for Time Series Structure Discovery
This paper presents a new approach to automatically discovering accurate...
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10/27/2022
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images
The problem of inferring object shape from a single 2D image is undercon...
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06/17/2022
Lossy Compression with Gaussian Diffusion
We describe a novel lossy compression approach called DiffC which is bas...
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10/25/2021
Nested R̂: Assessing Convergence for Markov chain Monte Carlo when using many short chains
When using Markov chain Monte Carlo (MCMC) algorithms, we can increase t...
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04/29/2021
What Are Bayesian Neural Network Posteriors Really Like?
The posterior over Bayesian neural network (BNN) parameters is extremely...
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11/06/2020
Underspecification Presents Challenges for Credibility in Modern Machine Learning
ML models often exhibit unexpectedly poor behavior when they are deploye...
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02/04/2020
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware
Markov chain Monte Carlo (MCMC) is widely regarded as one of the most im...
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01/14/2020
Hamiltonian Monte Carlo Swindles
Hamiltonian Monte Carlo (HMC) is a powerful Markov chain Monte Carlo (MC...
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10/23/2019
Automatically Batching Control-Intensive Programs for Modern Accelerators
We present a general approach to batching arbitrary computations for acc...
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06/07/2019
Automatic Reparameterisation of Probabilistic Programs
Probabilistic programming has emerged as a powerful paradigm in statisti...
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11/29/2018
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
Deriving conditional and marginal distributions using conjugacy relation...
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10/16/2018
The LORACs prior for VAEs: Letting the Trees Speak for the Data
In variational autoencoders, the prior on the latent codes z is often tr...
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09/12/2018
An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation
Music relies heavily on self-reference to build structure and meaning. W...
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09/12/2018
Music Transformer
Music relies heavily on repetition to build structure and meaning. Self-...
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02/16/2018
Variational Autoencoders for Collaborative Filtering
We extend variational autoencoders (VAEs) to collaborative filtering for...
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11/25/2017
Generalizing Hamiltonian Monte Carlo with Neural Networks
We present a general-purpose method to train Markov chain Monte Carlo ke...
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04/17/2017
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models
Professional-grade software applications are powerful but complicated-ex...
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04/13/2017
Stochastic Gradient Descent as Approximate Bayesian Inference
Stochastic Gradient Descent with a constant learning rate (constant SGD)...
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01/13/2017
Deep Probabilistic Programming
We propose Edward, a Turing-complete probabilistic programming language....
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02/08/2016
A Variational Analysis of Stochastic Gradient Algorithms
Stochastic Gradient Descent (SGD) is an important algorithm in machine l...
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09/23/2015
The Stan Math Library: Reverse-Mode Automatic Differentiation in C++
As computational challenges in optimization and statistical inference gr...
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05/28/2015
A trust-region method for stochastic variational inference with applications to streaming data
Stochastic variational inference allows for fast posterior inference in ...
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11/25/2014
Image Classification and Retrieval from User-Supplied Tags
This paper proposes direct learning of image classification from user-su...
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11/07/2014
Beta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference
Beta process is the standard nonparametric Bayesian prior for latent fac...
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04/16/2014
Structured Stochastic Variational Inference
Stochastic variational inference makes it possible to approximate poster...
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12/20/2013