Search Results for author:
Found 9 papers, 2 papers with code
Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data
Advances in bioinformatics are primarily due to new algorithms for processing diverse biological data sources.
The Hyperdimensional Transform for Distributional Modelling, Regression and Classification
Hyperdimensional computing (HDC) is an increasingly popular computing paradigm with immense potential for future intelligent applications.
The Hyperdimensional Transform: a Holographic Representation of Functions
We introduce the hyperdimensional transform as a new kind of integral transform.
A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression
Problems of that kind are often referred to as pairwise learning, dyadic prediction or network inference problems.
Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models
Many complex multi-target prediction problems that concern large target spaces are characterised by a need for efficient prediction strategies that avoid the computation of predictions for all targets explicitly.
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method
In this work we analyze kernel-based methods for pairwise learning, with a particular focus on a recently-suggested two-step method.
A two-step learning approach for solving full and almost full cold start problems in dyadic prediction
Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads.
Identification of functionally related enzymes by learning-to-rank methods
For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored.
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data
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, conditioned on a particular target object.
