
Learning to learn generative programs with Memoised WakeSleep
We study a class of neurosymbolic generative models in which neural net...
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Semisupervised Sequential Generative Models
We introduce a novel objective for training deep generative timeseries ...
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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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The Thermodynamic Variational Objective
We introduce the thermodynamic variational objective (TVO) for learning ...
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Imitation Learning of Factored Multiagent Reactive Models
We apply recent advances in deep generative modeling to the task of imit...
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Deep Variational Reinforcement Learning for POMDPs
Many realworld sequential decision making problems are partially observ...
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Revisiting Reweighted WakeSleep
Discrete latentvariable models, while applicable in a variety of settin...
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Secure Wireless Powered and Cooperative Jamming D2D Communications
This paper investigates a secure wirelesspowered devicetodevice (D2D)...
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A Successive Optimization Approach to Pilot Design for MultiCell Massive MIMO Systems
In this letter, we introduce a novel pilot design approach that minimize...
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Distributed Power Control in Downlink Cellular Massive MIMO Systems
This paper compares centralized and distributed methods to solve the pow...
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Tighter Variational Bounds are Not Necessarily Better
We provide theoretical and empirical evidence that using tighter evidenc...
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Improvements to Inference Compilation for Probabilistic Programming in LargeScale Scientific Simulators
We consider the problem of Bayesian inference in the family of probabili...
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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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AutoEncoding Sequential Monte Carlo
We introduce AESMC: a method for using deep neural networks for simultan...
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Using Synthetic Data to Train Neural Networks is ModelBased Reasoning
We draw a formal connection between using synthetic training data to opt...
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Inference Compilation and Universal Probabilistic Programming
We introduce a method for using deep neural networks to amortize the cos...
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Datadriven Sequential Monte Carlo in Probabilistic Programming
Most of Markov Chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC)...
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Tuan Anh Le
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