50 Open source projects that are alternatives of or similar to sparsebn

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

Hyperstar: Negative Sampling Improves Hypernymy Extraction Based on Projection Learning.

Library for graphical models of decision making, based on pgmpy and networkx

Optimal numerical differentiation of noisy time series data in python.

Python machine learning package based on sklearn API for multivariate data processing and statistical analysis of symmetric positive definite matrices via Riemannian geometry

Julia package of regularization functions for machine learning

An implementation of Bayesian Networks Model for pure C++14 (11) later, including probability inference and structure learning method.

Gaussian dynamic Bayesian networks structure learning and inference based on the bnlearn package

Codes and Datasets for paper RecSys'20 "SSE-PT: Sequential Recommendation Via Personalized Transformer" and NurIPS'19 "Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers"

Statistical Models with Regularization in Pure Julia

🧠👨‍💻Deep Learning Specialization • Lecture Notes • Lab Assignments

Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller

Python for《Deep Learning》，该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现

Code for our paper "Regularizing Neural Networks via Adversarial Model Perturbation", CVPR2021

An implementation of "Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks".

Scaleable input gradient regularization

speechpro.com/

机器学习-Coursera-吴恩达- python+Matlab代码实现

Official implementation codes of "Regularizing neural networks for future trajectory prediction via IRL framework"

[ICLR'21] PyTorch code for our paper "Neural Pruning via Growing Regularization"

Machine Learning models from scratch with a better visualisation

Implementation of key concepts of neuralnetwork via numpy

Implementation of models in our EMNLP 2019 paper: A Logic-Driven Framework for Consistency of Neural Models

AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics

Deep Learning Specialization Course by Coursera. Neural Networks, Deep Learning, Hyper Tuning, Regularization, Optimization, Data Processing, Convolutional NN, Sequence Models are including this Course.

Tensorflow implementation of the Manifold Mixup machine learning research paper

A document covering machine learning basics. 🤖📊

Efficient Algorithms for L0 Regularized Learning

Iterative unfolding for Python

Ordered Weighted L1 regularization for classification and regression in Python

A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow

A toolbox for differentially private data generation

QM model-based design tool and code generator based on UML state machines

Linear Gaussian Bayesian Networks - Inference, Parameter Learning and Representation. 🖧

C++ based implementation of StatNLP framework

pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models

Overview and implementation of Belief Propagation and Loopy Belief Propagation algorithms: sum-product, max-product, max-sum

A Tutorial on Modeling and Inference in Undirected Graphical Models for Hyperspectral Image Analysis

Automatic probabilistic programming for scientific machine learning and dynamical models

Java-based server framework of the graphical language server platform

Markov random fields with covariates

Clean & Documented TF2 implementation of "An end-to-end deep learning architecture for graph classification" (M. Zhang et al., 2018).

Example diagram editors built with Eclipse GLSP

VIP cheatsheets for Stanford's CS 221 Artificial Intelligence

Python Library for learning (Structure and Parameter) and inference (Probabilistic and Causal) in Bayesian Networks.

🎲 A Kotlin DSL for probabilistic programming.

Sampling nuclear data and uncertainty

Experimental and calculated small molecule hydration free energies

Experimental small molecule hydration free energy dataset