Predicting air pollution amounts in cities using a Gaussian Process model
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Updated
Oct 26, 2021 - Python
Predicting air pollution amounts in cities using a Gaussian Process model
We present a probabilistic model for neural spike counts that can capture arbitrary single neuron and joint statistics with their modulation by external covariates.
Active Bayesian Causal Inference (Neurips'22)
Probabilistic modeling using PyStan with demonstrative case study experiments from Christopher Bishop's Model-based Machine Learning.
Planning to Fairly Allocate: Probabilistic Fairness in the Restless Bandit Setting (KDD 2023)
Clean Factor Graphs in Python
Coordinate Ascent Variational Inference for Dirichlet Process Mixtures of Gaussians
Code for the research paper Meta-learning with hierarchical models based on similarity of causal mechanisms
Code repository for the paper: Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild (ICCV 2021)
Official Pytorch implementation of "Probabilistic Cross-Modal Embedding" (CVPR 2021)
List of casual implementations of machine learning models from scratch.
The official repository for AAAI 2024 Oral paper "Structured Probabilistic Coding"
Predicting Mortality after Transcatheter Aortic Valve Replacement using Preprocedural CT [Scientific Reports 2024]
Official Pytorch implementation of "Improved Probabilistic Image-Text Representations" (ICLR 2024)
Clean Random Events for Probabilistic Reasoning in Python
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