Papers Probabilistic Programming
“Probabilistic Programming” 태그가 달린 논문 273편 · 필터 해제
Transforming Probabilistic Programs for Model Checking
Probabilistic programming is perfectly suited to reliable and transparent data science, as it allows the user to specify their models in a high-level language without worrying about the complexities of how to fit the mod…
modelProbabilistic ProgrammingUncertainty Analysis in SPECT Reconstruction based on Probabilistic Programming
Single Photon Emission Computed Tomography (SPECT) is one of the nuclear medicine imaging modalities used for functional analysis of animal or human organs. Gamma rays emitted from the scanned body are filtered with coll…
Image ReconstructionProbabilistic ProgrammingPClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming
Data cleaning is naturally framed as probabilistic inference in a generative model of ground-truth data and likely errors, but the diversity of real-world error patterns and the hardness of inference make Bayesian approa…
DiversityProbabilistic ProgrammingA Programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors
Even as deep neural networks have become very effective for tasks in vision and perception, it remains difficult to explain and debug their behavior. In this paper, we present a programmatic and semantic approach to expl…
Probabilistic ProgrammingInferring Signaling Pathways with Probabilistic Programming
Cells regulate themselves via dizzyingly complex biochemical processes called signaling pathways. These are usually depicted as a network, where nodes represent proteins and edges indicate their influence on each other. …
Probabilistic ProgrammingFormal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI
We demonstrate a unified approach to rigorous design of safety-critical autonomous systems using the VerifAI toolkit for formal analysis of AI-based systems. VerifAI provides an integrated toolchain for tasks spanning th…
Probabilistic ProgrammingPlanning as Inference in Epidemiological Models
In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models. The kind of inference tasks undertaken include comput…
Probabilistic ProgrammingStochastically Differentiable Probabilistic Programs
Probabilistic programs with mixed support (both continuous and discrete latent random variables) commonly appear in many probabilistic programming systems (PPSs). However, the existence of the discrete random variables p…
Probabilistic ProgrammingStruct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors
The mixed membership stochastic blockmodel (MMSB) is a popular framework for community detection and network generation. It learns a low-rank mixed membership representation for each node across communities by exploiting…
Community DetectionProbabilistic ProgrammingRelational Reasoning$π$VAE: a stochastic process prior for Bayesian deep learning with MCMC
Stochastic processes provide a mathematically elegant way model complex data. In theory, they provide flexible priors over function classes that can encode a wide range of interesting assumptions. In practice, however, e…
Bayesian InferenceComputational EfficiencyGaussian ProcessesProbabilistic Programming+1DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models
We present the preliminary high-level design and features of DynamicPPL.jl, a modular library providing a lightning-fast infrastructure for probabilistic programming. Besides a computational performance that is often clo…
Probabilistic Programmingtfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware
Markov chain Monte Carlo (MCMC) is widely regarded as one of the most important algorithms of the 20th century. Its guarantees of asymptotic convergence, stability, and estimator-variance bounds using only unnormalized p…
Probabilistic ProgrammingAutomatic structured variational inference
Stochastic variational inference offers an attractive option as a default method for differentiable probabilistic programming. However, the performance of the variational approach depends on the choice of an appropriate …
Probabilistic ProgrammingVariational InferenceJoint Distributions for TensorFlow Probability
A central tenet of probabilistic programming is that a model is specified exactly once in a canonical representation which is usable by inference algorithms. We describe JointDistributions, a family of declarative repres…
Probabilistic ProgrammingSampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach
Though neural network models demonstrate impressive performance, we do not understand exactly how these black-box models make individual predictions. This drawback has led to substantial research devoted to understand th…
General ClassificationPredictionProbabilistic ProgrammingStochastic Probabilistic Programs
We introduce the notion of a stochastic probabilistic program and present a reference implementation of a probabilistic programming facility supporting specification of stochastic probabilistic programs and inference in …
Probabilistic ProgrammingComposable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro
NumPyro is a lightweight library that provides an alternate NumPy backend to the Pyro probabilistic programming language with the same modeling interface, language primitives and effect handling abstractions. Effect hand…
Probabilistic ProgrammingA Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors
Even as deep neural networks have become very effective for tasks in vision and perception, it remains difficult to explain and debug their behavior. In this paper, we present a programmatic and semantic approach to expl…
Probabilistic ProgrammingA New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming
This work presents the concept of kernel mean embedding and kernel probabilistic programming in the context of stochastic systems. We propose formulations to represent, compare, and propagate uncertainties for fairly gen…
Probabilistic ProgrammingParameter elimination in particle Gibbs sampling
Bayesian inference in state-space models is challenging due to high-dimensional state trajectories. A viable approach is particle Markov chain Monte Carlo, combining MCMC and sequential Monte Carlo to form "exact approxi…
Bayesian InferenceEpidemiologyProbabilistic ProgrammingState Space Models