Machine Learning on Sequential Data Using a Recurrent Weighted Average

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

Tensorflow Implementation of GAN modeling for sequential data

Sequential sets to sequential sets learning

PyTorch re-implementation of [Structured Inference Networks for Nonlinear State Space Models, AAAI 17]

Simplified Python implementation of the Density Line Chart by Moritz & Fisher.

Benchmarking synthetic data generators for sequential data in terms of accuracy and privacy.

This repository contains PyTorch implementations of Neural Process, Attentive Neural Process, and Recurrent Attentive Neural Process.

Code for Probabilistic Sequential Matrix Factorization

Incremental Label Propagation (ILP) - Incremental Semi-Supervised Learning from Streams for Object Classification

Data loader and model for variable length data in PyTorch

Effectively visualizing cluster flows and sizes for sequential cluster analyses using matplotlib.

This project aims to use LSTM for forecasting the total output of a RAS system based on the sequential input data.