Papers Probabilistic Programming
“Probabilistic Programming” 태그가 달린 논문 273편 · 필터 해제
Using probabilistic programs as proposals
Monte Carlo inference has asymptotic guarantees, but can be slow when using generic proposals. Handcrafted proposals that rely on user knowledge about the posterior distribution can be efficient, but are difficult to der…
Probabilistic ProgrammingImprovements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data. In scientific fields ranging from population biology to cosmology, low-level…
Bayesian InferenceProbabilistic ProgrammingTensorFlow Distributions
The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation. Building on two basic abstractions, it offers flexible…
Deep LearningProbabilistic ProgrammingHigh Five: Improving Gesture Recognition by Embracing Uncertainty
Sensors on mobile devices---accelerometers, gyroscopes, pressure meters, and GPS---invite new applications in gesture recognition, gaming, and fitness tracking. However, programming them remains challenging because human…
ClassificationGeneral ClassificationGesture RecognitionProbabilistic Programming+2ZhuSuan: A Library for Bayesian Deep Learning
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. ZhuSuan is built upon Tensorfl…
Bayesian InferenceDeep LearningProbabilistic ProgrammingregressionDelayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
We introduce a dynamic mechanism for the solution of analytically-tractable substructure in probabilistic programs, using conjugate priors and affine transformations to reduce variance in Monte Carlo estimators. For infe…
parameter estimationProbabilistic ProgrammingAnytime Exact Belief Propagation
Statistical Relational Models and, more recently, Probabilistic Programming, have been making strides towards an integration of logic and probabilistic reasoning. A natural expectation for this project is that a probabil…
Probabilistic ProgrammingRankPL: A Qualitative Probabilistic Programming Language
In this paper we introduce RankPL, a modeling language that can be thought of as a qualitative variant of a probabilistic programming language with a semantics based on Spohn's ranking theory. Broadly speaking, RankPL ca…
Causal InferenceProbabilistic ProgrammingLearning Probabilistic Programs Using Backpropagation
Probabilistic modeling enables combining domain knowledge with learning from data, thereby supporting learning from fewer training instances than purely data-driven methods. However, learning probabilistic models is diff…
Probabilistic ProgrammingImportance Sampled Stochastic Optimization for Variational Inference
Variational inference approximates the posterior distribution of a probabilistic model with a parameterized density by maximizing a lower bound for the model evidence. Modern solutions fit a flexible approximation with s…
Probabilistic ProgrammingStochastic OptimizationVariational InferenceProbabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes
Databases are widespread, yet extracting relevant data can be difficult. Without substantial domain knowledge, multivariate search queries often return sparse or uninformative results. This paper introduces an approach f…
Information RetrievalProbabilistic ProgrammingRetrievalDeep Probabilistic Programming
We propose Edward, a Turing-complete probabilistic programming language. Edward defines two compositional representations---random variables and inference. By treating inference as a first class citizen, on a par with mo…
Probabilistic ProgrammingVariational InferenceA Convenient Category for Higher-Order Probability Theory
Higher-order probabilistic programming languages allow programmers to write sophisticated models in machine learning and statistics in a succinct and structured way, but step outside the standard measure-theoretic formal…
FormProbabilistic ProgrammingAdversarial Message Passing For Graphical Models
Bayesian inference on structured models typically relies on the ability to infer posterior distributions of underlying hidden variables. However, inference in implicit models or complex posterior distributions is hard. A…
Bayesian InferenceProbabilistic ProgrammingMeasuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming
A key limitation of sampling algorithms for approximate inference is that it is difficult to quantify their approximation error. Widely used sampling schemes, such as sequential importance sampling with resampling and Me…
Probabilistic ProgrammingSummary - TerpreT: A Probabilistic Programming Language for Program Induction
We study machine learning formulations of inductive program synthesis; that is, given input-output examples, synthesize source code that maps inputs to corresponding outputs. Our key contribution is TerpreT, a domain-spe…
Probabilistic ProgrammingProgram inductionProgram SynthesisBetter call Saul: Flexible Programming for Learning and Inference in NLP
We present a novel way for designing complex joint inference and learning models using Saul (Kordjamshidi et al., 2015), a recently-introduced declarative learning-based programming language (DeLBP). We enrich Saul with …
Part-Of-Speech TaggingProbabilistic ProgrammingSemantic Role LabelingA Probabilistic Programming Approach To Probabilistic Data Analysis
Probabilistic techniques are central to data analysis, but different approaches can be challenging to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational ab…
BIG-bench Machine LearningClusteringProbabilistic ProgrammingDetecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes
Datasets with hundreds of variables and many missing values are commonplace. In this setting, it is both statistically and computationally challenging to detect true predictive relationships between variables and also to…
Missing ValuesProbabilistic ProgrammingSpecificityInference Compilation and Universal Probabilistic Programming
We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the streng…
Probabilistic Programming