21 citations · 34 across the 4 of their papers we have counts for
8 papers
Generalized Optimization: A First Step Towards Category Theoretic Learning Theory
Dan Shiebler
The Cartesian reverse derivative is a categorical generalization of reverse-mode automatic differentiation. We use this operator to generalize several optimization algorithms, incl…
Category Theory in Machine Learning
Dan Shiebler, Bruno Gavranović, Paul Wilson
Over the past two decades machine learning has permeated almost every realm of technology. At the same time, many researchers have begun using category theory as a unifying languag…
Lessons Learned Addressing Dataset Bias in Model-Based Candidate Generation at Twitter
Alim Virani, Jay Baxter, Dan Shiebler +7
Traditionally, heuristic methods are used to generate candidates for large scale recommender systems. Model-based candidate generation promises multiple potential advantages, prima…
Flattening Multiparameter Hierarchical Clustering Functors
Dan Shiebler
We bring together topological data analysis, applied category theory, and machine learning to study multiparameter hierarchical clustering. We begin by introducing a procedure for…
Tuning Word2vec for Large Scale Recommendation Systems
Benjamin P. Chamberlain, Emanuele Rossi, Dan Shiebler +2
Word2vec is a powerful machine learning tool that emerged from Natural Lan-guage Processing (NLP) and is now applied in multiple domains, including recom-mender systems, forecastin…
Incremental Monoidal Grammars
Dan Shiebler, Alexis Toumi, Mehrnoosh Sadrzadeh
In this work we define formal grammars in terms of free monoidal categories, along with a functor from the category of formal grammars to the category of automata. Generalising fro…