Event2Vec library offers additive embeddings for discrete event sequences

A new library called Event2Vec has been developed for learning embeddings of discrete event sequences. This library utilizes a simple additive structure where a history is the running sum of its event vectors, allowing linear models to decompose exactly into per-event terms. Training is self-supervised through next-event prediction and a reconstruction term, requiring no external labels. Event2Vec supports various embedding spaces, including hyperbolic, spherical, and non-commutative options, and offers a scikit-learn-style API with a PyTorch backend. AI

IMPACT Provides a novel, interpretable method for sequence modeling that could improve root-cause analysis in various domains.

RANK_REASON The cluster describes a new research paper and accompanying open-source library for learning event embeddings. [lever_c_demoted from research: ic=1 ai=1.0]

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• additive event embeddings
• Advanced Light Source
• Event2Vec
• Berkeley Lab
• Linear Score Tests for Variance Components in Linear Mixed Models and Applications to Genetic Association Studies
• MIT
• NeurReps@NeuralIPS Workshop
• per-event terms
• PyTorch

• paper
• product

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