
Learning discrete state abstractions with deep variational inference
Abstraction is crucial for effective sequential decision making in domai...
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On Exploration, Exploitation and Learning in Adaptive Importance Sampling
We study adaptive importance sampling (AIS) as an online learning proble...
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Evaluating Combinatorial Generalization in Variational Autoencoders
We evaluate the ability of variational autoencoders to generalize to uns...
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Amortized Population Gibbs Samplers with Neural Sufficient Statistics
We develop amortized population Gibbs (APG) samplers, a new class of aut...
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Neural Topographic Factor Analysis for fMRI Data
Neuroimaging experiments produce a large volume (gigabytes) of highdime...
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Composing Modeling and Inference Operations with Probabilistic Program Combinators
Probabilistic programs with dynamic computation graphs can define measur...
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Inference Trees: Adaptive Inference with Exploration
We introduce inference trees (ITs), a new class of inference methods tha...
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An Introduction to Probabilistic Programming
This document is designed to be a firstyear graduatelevel introduction...
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Learning Disentangled Representations with SemiSupervised Deep Generative Models
Variational autoencoders (VAEs) learn representations of data by jointly...
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Probabilistic structure discovery in time series data
Existing methods for structure discovery in time series data construct i...
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Path Finding under Uncertainty through Probabilistic Inference
We introduce a new approach to solving pathfinding problems under uncer...
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Inducing Interpretable Representations with Variational Autoencoders
We develop a framework for incorporating structured graphical models in ...
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Interacting Particle Markov Chain Monte Carlo
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a P...
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A New Approach to Probabilistic Programming Inference
We introduce and demonstrate a new approach to inference in expressive p...
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Particle Gibbs with Ancestor Sampling for Probabilistic Programs
Particle Markov chain Monte Carlo techniques rank among current stateof...
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OutputSensitive Adaptive MetropolisHastings for Probabilistic Programs
We introduce an adaptive outputsensitive MetropolisHastings algorithm ...
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Tempering by Subsampling
In this paper we demonstrate that tempering Markov chain Monte Carlo sam...
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Stylistic Clusters and the Syrian/South Syrian Tradition of FirstMillennium BCE Levantine Ivory Carving: A Machine Learning Approach
Thousands of firstmillennium BCE ivory carvings have been excavated fro...
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Hierarchicallycoupled hidden Markov models for learning kinetic rates from singlemolecule data
We address the problem of analyzing sets of noisy timevarying signals t...
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Bayesian Optimization for Probabilistic Programs
We present the first general purpose framework for marginal maximum a po...
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Hierarchical Disentangled Representations
Deep latentvariable models learn representations of highdimensional da...
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Learning Disentangled Representations of Texts with Application to Biomedical Abstracts
We propose a method for learning disentangled sets of vector representat...
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Structured Representations for Reviews:AspectBased Variational Hidden Factor Models
We present Variational AspectBased Latent Dirichlet Allocation (VALDA),...
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Modeling Theory of Mind for Autonomous Agents with Probabilistic Programs
As autonomous agents become more ubiquitous, they will eventually have t...
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Can VAEs Generate Novel Examples?
An implicit goal in works on deep generative models is that such models ...
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Structured Neural Topic Models for Reviews
We present Variational Aspectbased Latent Topic Allocation (VALTA), a f...
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Deep Markov SpatioTemporal Factorization
We introduce deep Markov spatiotemporal factorization (DMSTF), a deep g...
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