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Probabilistic Programming

Probabilistic programming languages are designed to describe probabilistic models and then perform inference in those models. PPLs are closely related to graphical models and Bayesian networks, but are more expressive and flexible.

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Papers

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Showing 201–250 of 273 papers

TitleStatusHype
Probabilistic Planning by Probabilistic Programming—0
Bayesian Neural NetworksCode0
Using probabilistic programs as proposals—0
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators—0
High Five: Improving Gesture Recognition by Embracing Uncertainty—0
ZhuSuan: A Library for Bayesian Deep LearningCode0
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic ProgramsCode0
Anytime Exact Belief Propagation—0
RankPL: A Qualitative Probabilistic Programming Language—0
Learning Probabilistic Programs Using Backpropagation—0
Importance Sampled Stochastic Optimization for Variational Inference—0
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric BayesCode0
Deep Probabilistic Programming—0
A Convenient Category for Higher-Order Probability Theory—0
Adversarial Message Passing For Graphical Models—0
Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming—0
Summary - TerpreT: A Probabilistic Programming Language for Program Induction—0
A Probabilistic Programming Approach To Probabilistic Data Analysis—0
Better call Saul: Flexible Programming for Learning and Inference in NLPCode0
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric BayesCode0
Inference Compilation and Universal Probabilistic ProgrammingCode0
Consistent Kernel Mean Estimation for Functions of Random Variables—0
Deep Amortized Inference for Probabilistic ProgramsCode0
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models—0
Robust Energy Storage Scheduling for Imbalance Reduction of Strategically Formed Energy Balancing Groups—0
Probabilistic Data Analysis with Probabilistic ProgrammingCode0
Practical optimal experiment design with probabilistic programs—0
TerpreT: A Probabilistic Programming Language for Program Induction—0
Automatic Generation of Probabilistic Programming from Time Series Data—0
Swift: Compiled Inference for Probabilistic Programming Languages—0
Spreadsheet Probabilistic Programming—0
Structured Factored Inference: A Framework for Automated Reasoning in Probabilistic Programming Languages—0
Measuring the reliability of MCMC inference with bidirectional Monte Carlo—0
The Physics of Text: Ontological Realism in Information Extraction—0
Applications of Probabilistic Programming (Master's thesis, 2015)—0
A Step from Probabilistic Programming to Cognitive Architectures—0
Dataflow Matrix Machines as a Generalization of Recurrent Neural NetworksCode0
Composing inference algorithms as program transformations—0
A theory of contemplation—0
Bachelor's thesis on generative probabilistic programming (in Russian language, June 2014)—0
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints—0
Probabilistic Programming with Gaussian Process Memoization—0
BayesDB: A probabilistic programming system for querying the probable implications of data—0
Linear Models of Computation and Program Learning—0
Data-driven Sequential Monte Carlo in Probabilistic Programming—0
Lazy Factored Inference for Functional Probabilistic Programming—0
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching—0
RELLY: Inferring Hypernym Relationships Between Relational Phrases—0
A New Approach to Probabilistic Programming Inference—0
Automatic Variational Inference in Stan—0
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