Papers Probabilistic Programming
“Probabilistic Programming” 태그가 달린 논문 273편 · 필터 해제
Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models
To enable robots to achieve high level objectives, engineers typically write scripts that apply existing specialized skills, such as navigation, object detection and manipulation to achieve these goals. Writing good scri…
object-detectionObject DetectionProbabilistic ProgrammingTensor Program Optimization with Probabilistic Programs
Automatic optimization for tensor programs becomes increasingly important as we deploy deep learning in various environments, and efficient optimization relies on a rich search space and effective search. Most existing e…
Deep LearningProbabilistic ProgrammingDesigning Perceptual Puzzles by Differentiating Probabilistic Programs
We design new visual illusions by finding "adversarial examples" for principled models of human perception -- specifically, for probabilistic models, which treat vision as Bayesian inference. To perform this search effic…
Bayesian InferenceColor ConstancyProbabilistic ProgrammingProgram Analysis of Probabilistic Programs
Probabilistic programming is a growing area that strives to make statistical analysis more accessible, by separating probabilistic modelling from probabilistic inference. In practice this decoupling is difficult. No sing…
Probabilistic ProgrammingGuaranteed Bounds for Posterior Inference in Universal Probabilistic Programming
We propose a new method to approximate the posterior distribution of probabilistic programs by means of computing guaranteed bounds. The starting point of our work is an interval-based trace semantics for a recursive, hi…
Probabilistic ProgrammingA meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems
We introduce a formal meta-language for probabilistic programming, capable of expressing both programs and the type systems in which they are embedded. We are motivated here by the desire to allow an AGI to learn not onl…
Probabilistic ProgrammingVocal Bursts Type PredictionHigher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives
Deep Probabilistic Programming (DPP) allows powerful models based on recursive computation to be learned using efficient deep-learning optimization techniques. Additionally, DPP offers a unified perspective, where infere…
Generalization BoundsMeta-LearningProbabilistic ProgrammingCompartmental Models for COVID-19 and Control via Policy Interventions
We demonstrate an approach to replicate and forecast the spread of the SARS-CoV-2 (COVID-19) pandemic using the toolkit of probabilistic programming languages (PPLs). Our goal is to study the impact of various modeling a…
Probabilistic ProgrammingA Probabilistic Programming Idiom for Active Knowledge Search
In this paper, we derive and implement a probabilistic programming idiom for the problem of acquiring new knowledge about an environment. The idiom is implemented utilizing a modern probabilistic programming language. We…
Probabilistic ProgrammingWeighted Programming
We study weighted programming, a programming paradigm for specifying mathematical models. More specifically, the weighted programs we investigate are like usual imperative programs with two additional features: (1) nonde…
Probabilistic ProgrammingMixed Nondeterministic-Probabilistic Automata: Blending graphical probabilistic models with nondeterminism
Graphical models in probability and statistics are a core concept in the area of probabilistic reasoning and probabilistic programming-graphical models include Bayesian networks and factor graphs. In this paper we develo…
Probabilistic ProgrammingSurrogate Likelihoods for Variational Annealed Importance Sampling
Variational inference is a powerful paradigm for approximate Bayesian inference with a number of appealing properties, including support for model learning and data subsampling. By contrast MCMC methods like Hamiltonian …
Bayesian InferenceProbabilistic ProgrammingVariational InferenceSimulation Intelligence: Towards a New Generation of Scientific Methods
The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the …
Probabilistic Programmingscientific discoveryTesting Probabilistic Circuits
Probabilistic circuits (PCs) are a powerful modeling framework for representing tractable probability distributions over combinatorial spaces. In machine learning and probabilistic programming, one is often interested in…
Probabilistic ProgrammingQuerying Labelled Data with Scenario Programs for Sim-to-Real Validation
Simulation-based testing of autonomous vehicles (AVs) has become an essential complement to road testing to ensure safety. Consequently, substantial research has focused on searching for failure scenarios in simulation. …
Autonomous VehiclesProbabilistic ProgrammingMapping probability word problems to executable representations
While solving math word problems automatically has received considerable attention in the NLP community, few works have addressed probability word problems specifically. In this paper, we employ and analyse various neura…
Contextualised Word RepresentationsMathProbabilistic ProgrammingTransfer Learning3DP3: 3D Scene Perception via Probabilistic Programming
We present 3DP3, a framework for inverse graphics that uses inference in a structured generative model of objects, scenes, and images. 3DP3 uses (i) voxel models to represent the 3D shape of objects, (ii) hierarchical sc…
ObjectPose EstimationProbabilistic ProgrammingScene UnderstandingA Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation
Behavior prediction remains one of the most challenging tasks in the autonomous vehicle (AV) software stack. Forecasting the future trajectories of nearby agents plays a critical role in ensuring road safety, as it equip…
PredictionProbabilistic Programmingflip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs
Many of today's probabilistic programming languages (PPLs) have brittle inference performance: the performance of the underlying inference algorithm is very sensitive to the precise way in which the probabilistic program…
Probabilistic ProgrammingLazyPPL: laziness and types in non-parametric probabilistic programs
We introduce LazyPPL, a prototype probabilistic programming library for Haskell. The library emphasises the clarifying power of types, and the connection between non-parametric, stochastic processes and lazy (call by nee…
ClusteringGaussian ProcessesPoint ProcessesProbabilistic Programming