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Probabilistic Programming
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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.
<span style="color:grey; opacity: 0.6">( Image credit: Michael Betancourt )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo
DeepRV: pre-trained spatial priors for accelerated disease mapping
Proceedings 40th International Conference on Logic Programming
Representation of Molecules via Algebraic Data Types : Advancing Beyond SMILES & SELFIES
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
A Distribution Semantics for Probabilistic Term Rewriting
Identifying latent disease factors differently expressed in patient subgroups using group factor analysis
Inference Plans for Hybrid Particle Filtering
Markov Senior -- Learning Markov Junior Grammars to Generate User-specified Content
Probabilistic Parameter Estimators and Calibration Metrics for Pose Estimation from Image Features
Querying Labeled Time Series Data with Scenario Programs
Probabilistic Programming with Programmable Variational Inference
Data Petri Nets meet Probabilistic Programming (Extended version)
The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison
Simplifying debiased inference via automatic differentiation and probabilistic programming
ScenicNL: Generating Probabilistic Scenario Programs from Natural Language
COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty
Automated Efficient Estimation using Monte Carlo Efficient Influence Functions
BlackJAX: Composable Bayesian inference in JAX
Efficient Incremental Belief Updates Using Weighted Virtual Observations
SymbolicAI: A framework for logic-based approaches combining generative models and solvers
Statistical Learning of Conjunction Data Messages Through a Bayesian Non-Homogeneous Poisson Process
Diffusion models for probabilistic programming
Worst-Case Analysis is Maximum-A-Posteriori Estimation
Inferring Capabilities from Task Performance with Bayesian Triangulation
Pearl's and Jeffrey's Update as Modes of Learning in Probabilistic Programming
From Probabilistic Programming to Complexity-based Programming
Scaling Integer Arithmetic in Probabilistic Programs
Towards an architectural framework for intelligent virtual agents using probabilistic programming
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought
A Heavy-Tailed Algebra for Probabilistic Programming
Scalable Neural-Probabilistic Answer Set Programming
Push: Concurrent Probabilistic Programming for Bayesian Deep Learning
Automating Model Comparison in Factor Graphs
Bayesian Calibration of MEMS Accelerometers
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