63 citations · 241 across the 21 of their papers we have counts for
6 papers · 1 filter
Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data
Michiel Stock, Dimitri Boeckaerts, Pieter Dewulf +4
Advances in bioinformatics are primarily due to new algorithms for processing diverse biological data sources. While sophisticated alignment algorithms have been pivotal in analyzi…
The Hyperdimensional Transform for Distributional Modelling, Regression and Classification
Pieter Dewulf, Bernard De Baets, Michiel Stock
Hyperdimensional computing (HDC) is an increasingly popular computing paradigm with immense potential for future intelligent applications. Although the main ideas already took form…
The Hyperdimensional Transform: a Holographic Representation of Functions
Pieter Dewulf, Michiel Stock, Bernard De Baets
Integral transforms are invaluable mathematical tools to map functions into spaces where they are easier to characterize. We introduce the hyperdimensional transform as a new kind…
A two-step learning approach for solving full and almost full cold start problems in dyadic prediction
Tapio Pahikkala, Michiel Stock, Antti Airola +3
Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads. We consider the full and almost full cold start problem in dyadic pre…
Identification of functionally related enzymes by learning-to-rank methods
Michiel Stock, Thomas Fober, Eyke Hüllermeier +6
Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually de…
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data
Tapio Pahikkala, Antti Airola, Michiel Stock +2
In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditione…