Many of papers are in multiple fields and some of these fields overlap. Let me know what's missing! (preferably by creating a pull request)
- Categorical Foundations of Gradient-Based Learning
- Backprop as Functor
- Lenses and Learners
- Reverse Derivative Ascent
- Dioptics
- Learning Functors using Gradient Descent (longer version here)
- Compositionality for Recursive Neural Networks
- Deep neural networks as nested dynamical systems
- Neural network layers as parametric spans
- Differentiable Causal Computations via Delayed Trace
- Simple Essence of Automatic Differentiation
- Reverse Derivative Categories
- Towards formalizing and extending differential programming using tangent categories
- Markov categories
- Infinite products and zero-one laws in categorical probability
- A Convenient Category for Higher-Order Probability Theory
- Bimonoidal Structure of Probability Monads
- Representable Markov Categories and Comparison of Statistical Experiments in Categorical Probability
- A category theory framework for Bayesian Learning
- Causal Theories: A Categorical Perspective on Bayesian Networks
- Bayesian machine learning via category theory
- A Categorical Foundation for Bayesian probability
- Bayesian Open Games
- Causal Inference by String Diagram Surgery
- Disintegration and Bayesian Inversion via String Diagrams
- Categorical Stochastic Processes and Likelihood
- Bayesian Updates Compose Optically
- Automatic Backward Filtering Forward Guiding for Markov processes and graphical models
- A Channel-Based Perspective on Conjugate Priors
- On Characterizing the Capacity of Neural Networks using Algebraic Topology
- Persistent-Homology-based Machine Learning and its Applications - A Survey
- Topological Expresivenss of Neural Networks
- Approximating the convex hull via metric space magnitude
- Practical applications of metric space magnitude and weighting vectors
- Weighting vectors for machine learning: numerical harmonic analysis applied to boundary detection
- The magnitude vector of images
- Automata Learning: A Categorical Perspective
- A Categorical Framework for Learning Generalised Tree Automata
- Graph Neural Networks are Dynamic Programmers
- Generalized Convolution and Efficient Language Recognition
- Learning as Change Propagation with Delta Lenses
- From Open Learners to Open Games
- Learners Languages
- A Constructive, Type-Theoretic Approach to Regression via Global Optimisation
- Natural Graph Networks
- Local Permutation Equivariance For Graph Neural Networks
- Characterizing the invariances of learning algorithms using category theory
- Functorial Manifold Learning
- Sheaf Neural Networks
- Sheaf Neural Networks with Connection Laplacians
- Diegetic representation of feedback in open games
- Assessing the Unitary RNN as an End-to-End Compositional Model of Syntax