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A general-purpose toolbox for efficient Kronecker-based learning

Julkaisun tekijät: Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets

Konferenssin vakiintunut nimi: Juliacon 2020

Julkaisuvuosi: 2020

Kirjan nimi *: JuliaCon Proceedings

Volyymi: 2

Julkaisunumero: 13

Sivujen määrä: 2


Rinnakkaistallenteen osoite:


Pairwise learning is a machine learning paradigm where the goal
is to predict properties of pairs of objects. Applications include
recommender systems, molecular network inference, and ecological interaction prediction. Kronecker-based learning systems provide a simple yet elegant method to learn from such pairs. Using
tricks from linear algebra, these models can be trained, tuned, and
validated on large datasets. Our Julia package Kronecker.jl
aggregates these shortcuts and efficient algorithms using a lazily evaluated Kronecker product ‘⊗’, such that it is easy to experiment
with learning algorithms using the Kronecker product.

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Last updated on 2022-07-04 at 18:42