31 Open source projects that are alternatives of or similar to deeprob-kit

PyTorch implementation of the paper "NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity." (NeurIPS 2020)

Separating Normalizing Flows code from Pyro and improving API

Blang's software development kit

Microscopic Markov Chain Approach to model the spreading of COVID-19

Implementation of DeepDB: Learn from Data, not from Queries!

Materials of the Nordic Probabilistic AI School 2019.

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

A quick introduction to all most important concepts of Probability Theory, only freshman level of mathematics needed as prerequisite.

A curated collection of papers on probabilistic circuits, computational graphs encoding tractable probability distributions.

Materials of the Nordic Probabilistic AI School 2021.

Repo for the Tutorials of Day1-Day3 of the Nordic Probabilistic AI School 2021 (https://probabilistic.ai/)

Multi-Touch Attribution

Machine Learning models from scratch with a better visualisation

A collection of Methods and Models for various architectures of Artificial Neural Networks

Code for ICML 2019 paper "Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering" [long-oral]

nessai: Nested Sampling with Artificial Intelligence

Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)

Implementation of Unconstrained Monotonic Neural Network and the related experiments. These architectures are particularly useful for modelling monotonic transformations in normalizing flows.

We got a stew going!

Code for the paper Semi-Conditional Normalizing Flows for Semi-Supervised Learning

Implementations of normalizing flows using python and tensorflow

Jupyter Notebook corresponding to 'Going with the Flow: An Introduction to Normalizing Flows'

Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"

Implementation of Normalizing flows on MNIST https://arxiv.org/abs/1505.05770

Implementation of "Intensity-Free Learning of Temporal Point Processes" (Spotlight @ ICLR 2020)

PyTorch implementation of NVIDIA WaveGlow with constant memory cost.

A Julia framework for invertible neural networks

PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)

Continuous-time gradient flow for generative modeling and variational inference

Sum-Product Network learning routines in python

🎲 A Kotlin DSL for probabilistic programming.