Probabilistic Model

Probabilistic models are mathematical frameworks used to represent and reason under uncertainty, aiming to quantify the likelihood of different outcomes. Current research focuses on improving the efficiency and accuracy of these models across diverse applications, including generative AI (e.g., diffusion models, sum-product networks), uncertainty quantification in large language models, and robust inference in Bayesian networks. This work is significant because it enhances the reliability and interpretability of AI systems, leading to improved decision-making in various fields such as healthcare, finance, and scientific discovery.

Papers

December 3, 2022

Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS
Gary Tom, Riley J. Hickman, Aniket Zinzuwadia, Afshan Mohajeri, Benjamin Sanchez-Lengeling, Alan Aspuru-Guzik
Probabilistic Model Calibration Performance Stronger Generalizability Molecular Representation Known Molecule Molecular Design Molecular Property

November 28, 2022

Probabilistic Modelling of Signal Mixtures with Differentiable Dictionaries
Lukáš Samuel Marták, Rainer Kelz, Gerhard Widmer
Mixture Component Probabilistic Model Non Negative Matrix Factorization Differentiable Search Mixture Signal

November 22, 2022

Can denoising diffusion probabilistic models generate realistic astrophysical fields?
Nayantara Mudur, Douglas P. Finkbeiner
Generative Adversarial Network Diffusion Explainer Probabilistic Model Score Based Generative Density Field

October 21, 2022

October 19, 2022

Efficient, probabilistic analysis of combinatorial neural codes
Thomas F Burns, Irwansyah
High Efficiency Probabilistic Model Neural Activity Biological Neural Network Neural Code

October 18, 2022

Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy
Bayesian Optimization Probabilistic Model Black Box Function Discrete Environment Mixed Space Reparameterization Method Probabilistic Surrogate

October 16, 2022

October 12, 2022

Quantifying Uncertainty with Probabilistic Machine Learning Modeling in Wireless Sensing
Amit Kachroo, Sai Prashanth Chinnapalli
Uncertainty Quantification Probabilistic Model Epistemic Uncertainty Wi Fi Sensing Wireless Sensing Probabilistic Machine Learning

October 6, 2022

Probabilistic Model Incorporating Auxiliary Covariates to Control FDR
Lin Qiu, Nils Murrugarra-Llerena, Vítor Silva, Lin Lin, Vernon M. Chinchilli
Probabilistic Model Relevant Covariates Multiple Hypothesis Testing False Discovery Rate Auxiliary Covariates

October 4, 2022

September 30, 2022

September 23, 2022

A Probabilistic Model of Activity Recognition with Loose Clothing
Tianchen Shen, Irene Di Giulio, Matthew Howard
Activity Recognition Probabilistic Model Motion Capture Attached Sensor

September 22, 2022

Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru R. Zhang
Diffusion Model Theoretical Understanding Probabilistic Model Langevin Dynamic Open Sampling Score Based Generative Performance Score Modeling Assumption

September 20, 2022

Probabilistic Dalek -- Emulator framework with probabilistic prediction for supernova tomography
Wolfgang Kerzendorf, Nutan Chen, Jack O'Brien, Johannes Buchner, Patrick van der Smagt
Probabilistic Model Radiative Transfer Type Ia Supernova

September 15, 2022

GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
Xiaoqi Wang, Han-Wei Shen
Graph Neural Network Probabilistic Model GNN Model GNN Cl Model Agnostic Explanation Generative Explanation Explanation Graph Model Reconciling Explanation

September 13, 2022

Borch: A Deep Universal Probabilistic Programming Language
Lewis Belcher, Johan Gudmundsson, Michael Green
Deep Neural Network Multi Layer Probabilistic Model Probabilistic Programming Connectionist Model

Probabilistic Model

Papers

Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS

Probabilistic Modelling of Signal Mixtures with Differentiable Dictionaries

Can denoising diffusion probabilistic models generate realistic astrophysical fields?

Uncertain Evidence in Probabilistic Models and Stochastic Simulators

Targeted active learning for probabilistic models

Efficient, probabilistic analysis of combinatorial neural codes

Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization

Comparing Synthetic Tabular Data Generation Between a Probabilistic Model and a Deep Learning Model for Education Use Cases

Learning Probabilities of Causation from Finite Population Data

Quantifying Uncertainty with Probabilistic Machine Learning Modeling in Wireless Sensing

Probabilistic Model Incorporating Auxiliary Covariates to Control FDR

HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences

Diffusion Models for Graphs Benefit From Discrete State Spaces

Physically Meaningful Uncertainty Quantification in Probabilistic Wind Turbine Power Curve Models as a Damage Sensitive Feature

TabDDPM: Modelling Tabular Data with Diffusion Models

A Probabilistic Model of Activity Recognition with Loose Clothing

Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Probabilistic Dalek -- Emulator framework with probabilistic prediction for supernova tomography

GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks

Borch: A Deep Universal Probabilistic Programming Language