There are 6 repositories under probabilistic-models topic.
Bayesian inference with probabilistic programming.
Code for modelling estimated deaths and cases for COVID19.
Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.)
This repository contains my full work and notes on Coursera's NLP Specialization (Natural Language Processing) taught by the instructor Younes Bensouda Mourri and Łukasz Kaiser offered by deeplearning.ai
Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods.
Machine Learning library for the web and Node.
[ICASSP 2024] This is the official code for "VoiceFlow: Efficient Text-to-Speech with Rectified Flow Matching"
Collection of probabilistic models and inference algorithms
Unofficial Pytorch code for "Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models"
GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation [IJCV2023]
Simulate realistic trajectory data seen through sporadic reporting
Visual Inertial Odometry (VIO) / Simultaneous Localization & Mapping (SLAM) using iSAM2 framework from the GTSAM library.
Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing
Materials of the Nordic Probabilistic AI School 2019.
A repository for generative models
Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python
Training an n-gram based Language Model using KenLM toolkit for Deep Speech 2
Materials of the Nordic Probabilistic AI School 2021.
A library for discrete-time Markov chains analysis.
Code for ICML 2019 paper "Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering" [long-oral]
PyTorch implementation of the paper "NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity." (NeurIPS 2020)
A Python Library for Deep Probabilistic Modeling
A machine learning library for spacecraft collision avoidance
Stochastic tree ensembles (BART / XBART) for supervised learning and causal inference
A quick introduction to all most important concepts of Probability Theory, only freshman level of mathematics needed as prerequisite.
C++ template library for probabilistic programming
A curated collection of papers on probabilistic circuits, computational graphs encoding tractable probability distributions.
Treeffuser is an easy-to-use package for probabilistic prediction and probabilistic regression on tabular data with tree-based diffusion models.
This is all my notebooks, lab solutions, and assignments for the DeepLearning.AI Natural Language Processing Specialization on Coursera.
Repo for the Tutorials of Day1-Day3 of the Nordic Probabilistic AI School 2021 (https://probabilistic.ai/)