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rwa - Machine Learning on Sequential Data Using a Recurrent Weighted Average

  •    Python

This repository holds the code to a new kind of RNN model for processing sequential data. The model computes a recurrent weighted average (RWA) over every previous processing step. With this approach, the model can form direct connections anywhere along a sequence. This stands in contrast to traditional RNN architectures that only use the previous processing step. A detailed description of the RWA model has been published in a manuscript at https://arxiv.org/pdf/1703.01253.pdf. Because the RWA can be computed as a running average, it does not need to be completely recomputed with each processing step. The numerator and denominator can be saved from the previous step. Consequently, the model scales like that of other RNN models such as the LSTM model.

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

  •    Python

pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models. The package is specifically tailored to analyze sequential data which capture multiple observations of short, independent paths observed in an underlying graph topology. Examples for such data include user click streams in information networks, biological pathways, or traces of information propagating in social media. Unifying the analysis of pathways and temporal networks, pathpy also supports the extraction of time-respecting paths from time-stamped network data. It extends (and will eventually supersede) the package pyTempnets.

peax - Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders

  •    Jupyter

Epigenomic data expresses a rich body of diverse patterns that help to identify regulatory elements like promoter, enhancers, etc. But finding these patterns reliably genome wide is challenging. Peax is a tool for interactive visual pattern search and exploration of epigenomic patterns based on unsupervised representation learning with convolutional autoencoders. The visual search is driven by manually labeled genomic regions for actively learning a classifier to reflect your notion of interestingness. Citation: Lekschas et al., Peax: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning, Computer Graphics Forum, 2020, doi: 10.1111/cgf.13971.

rPSMF - Code for Probabilistic Sequential Matrix Factorization

  •    Python

If you encounter a problem when using this repository or simply want to ask a question, please don't hesitate to open an issue on GitHub or send an email to odakyildiz at turing dot ac dot uk and/or gertjanvandenburg at gmail dot com. Our Probabilistic Sequential Matrix Factorization (PSMF) method allows you to model high-dimensional timeseries data that exhibits non-stationary dynamics. We also propose a robust variant of the model, called rPSMF, that handles model misspecification and outliers.









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