Papers Probabilistic Programming
“Probabilistic Programming” 태그가 달린 논문 273편 · 필터 해제
Efficient Incremental Belief Updates Using Weighted Virtual Observations
We present an algorithmic solution to the problem of incremental belief updating in the context of Monte Carlo inference in Bayesian statistical models represented by probabilistic programs. Given a model and a sample-ap…
Probabilistic ProgrammingSymbolicAI: A framework for logic-based approaches combining generative models and solvers
We introduce SymbolicAI, a versatile and modular framework employing a logic-based approach to concept learning and flow management in generative processes. SymbolicAI enables the seamless integration of generative model…
Few-Shot LearningIn-Context LearningProbabilistic ProgrammingStatistical Learning of Conjunction Data Messages Through a Bayesian Non-Homogeneous Poisson Process
Current approaches for collision avoidance and space traffic management face many challenges, mainly due to the continuous increase in the number of objects in orbit and the lack of scalable and automated solutions. To a…
Collision AvoidanceProbabilistic ProgrammingDiffusion models for probabilistic programming
We propose Diffusion Model Variational Inference (DMVI), a novel method for automated approximate inference in probabilistic programming languages (PPLs). DMVI utilizes diffusion models as variational approximations to t…
Probabilistic ProgrammingVariational InferenceWorst-Case Analysis is Maximum-A-Posteriori Estimation
The worst-case resource usage of a program can provide useful information for many software-engineering tasks, such as performance optimization and algorithmic-complexity-vulnerability discovery. This paper presents a ge…
Bayesian InferenceProbabilistic ProgrammingInferring Capabilities from Task Performance with Bayesian Triangulation
As machine learning models become more general, we need to characterise them in richer, more meaningful ways. We describe a method to infer the cognitive profile of a system from diverse experimental data. To do so, we i…
Probabilistic ProgrammingPearl's and Jeffrey's Update as Modes of Learning in Probabilistic Programming
The concept of updating a probability distribution in the light of new evidence lies at the heart of statistics and machine learning. Pearl's and Jeffrey's rule are two natural update mechanisms which lead to different o…
Probabilistic ProgrammingVariational InferenceFrom Probabilistic Programming to Complexity-based Programming
The paper presents the main characteristics and a preliminary implementation of a novel computational framework named CompLog. Inspired by probabilistic programming systems like ProbLog, CompLog builds upon the inferenti…
DescriptiveNegationProbabilistic ProgrammingScaling Integer Arithmetic in Probabilistic Programs
Distributions on integers are ubiquitous in probabilistic modeling but remain challenging for many of today's probabilistic programming languages (PPLs). The core challenge comes from discrete structure: many of today's …
Probabilistic ProgrammingTowards an architectural framework for intelligent virtual agents using probabilistic programming
We present a new framework called KorraAI for conceiving and building embodied conversational agents (ECAs). Our framework models ECAs' behavior considering contextual information, for example, about environment and inte…
Probabilistic ProgrammingFrom Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought
How does language inform our downstream thinking? In particular, how do humans make meaning from language--and how can we leverage a theory of linguistic meaning to build machines that think in more human-like ways? In t…
Bayesian InferenceProbabilistic ProgrammingRelational ReasoningA Heavy-Tailed Algebra for Probabilistic Programming
Despite the successes of probabilistic models based on passing noise through neural networks, recent work has identified that such methods often fail to capture tail behavior accurately, unless the tails of the base dist…
Probabilistic ProgrammingVariational InferenceScalable Neural-Probabilistic Answer Set Programming
The goal of combining the robustness of neural networks and the expressiveness of symbolic methods has rekindled the interest in Neuro-Symbolic AI. Deep Probabilistic Programming Languages (DPPLs) have been developed for…
Probabilistic ProgrammingQuestion AnsweringVisual Question AnsweringVisual Question Answering (VQA)Push: Concurrent Probabilistic Programming for Bayesian Deep Learning
We introduce a library called Push that takes a probabilistic programming approach to Bayesian deep learning (BDL). This library enables concurrent execution of BDL inference algorithms on multi-GPU hardware for neural n…
Bayesian InferenceDeep LearningGPUProbabilistic ProgrammingAutomating Model Comparison in Factor Graphs
Bayesian state and parameter estimation have been automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, which still requires error-prone and time-co…
modelparameter estimationProbabilistic ProgrammingBayesian Calibration of MEMS Accelerometers
This study aims to investigate the utilization of Bayesian techniques for the calibration of micro-electro-mechanical systems (MEMS) accelerometers. These devices have garnered substantial interest in various practical a…
Probabilistic ProgrammingSequential Monte Carlo Steering of Large Language Models using Probabilistic Programs
Even after fine-tuning and reinforcement learning, large language models (LLMs) can be difficult, if not impossible, to control reliably with prompts alone. We propose a new inference-time approach to enforcing syntactic…
Language ModelingLanguage ModellingProbabilistic ProgrammingText GenerationExact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach
We present an exact Bayesian inference method for discrete statistical models, which can find exact solutions to a large class of discrete inference problems, even with infinite support and continuous priors. To express …
Bayesian InferenceProbabilistic ProgrammingString Diagrams with Factorized Densities
A growing body of research on probabilistic programs and causal models has highlighted the need to reason compositionally about model classes that extend directed graphical models. Both probabilistic programs and causal …
Causal InferenceProbabilistic ProgrammingDimensionality Reduction as Probabilistic Inference
Dimensionality reduction (DR) algorithms compress high-dimensional data into a lower dimensional representation while preserving important features of the data. DR is a critical step in many analysis pipelines as it enab…
Dimensionality ReductionGaussian ProcessesProbabilistic Programming