The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

With infinitely many high-quality data points, infinite computational power, an infinitely large
foundation model with a perfect training algorithm and guaranteed zero generalization error …

We propose a categorical semantics of gradient-based machine learning algorithms in terms
of lenses, parametric maps, and reverse derivative categories. This foundation provides a …

This document is an elementary introduction to string diagrams. It takes a computer science
perspective: rather than using category theory as a starting point, we build on intuitions from …

Artificial intelligence (AI) is currently based largely on black-box machine learning models
which lack interpretability. The field of eXplainable AI (XAI) strives to address this major …

Disentangling the factors of variation in data is a fundamental concept in machine learning
and has been studied in various ways by different researchers, leading to a multitude of …

The framework of causal models provides a principled approach to causal reasoning,
applied today across many scientific domains. Here we present this framework in the …

This thesis introduces quantum natural language processing (QNLP) models based on a
simple yet powerful analogy between computational linguistics and quantum mechanics …

Every known artificial deep neural network (DNN) corresponds to an object in a canonical
Grothendieck's topos; its learning dynamic corresponds to a flow of morphisms in this topos …

There are different meanings of the term “compositionality” within science: what one
researcher would call compositional, is not at all compositional for another researcher. The …