bgavran/Category_Theory_Machine_Learning 排名第 10,024，此处有 8 个同类条目；EricGuo5513/HumanML3D 排名第 10,028。

Category Theory ∩ Machine Learning Category theory has been finding increasing applications in machine learning. This repository aims to list all of the relevant papers, grouped by fields. For an introduction to the ideas behind category theory, check out this link. There might be papers missing, and some papers are in multiple fields. Feel free to contribute to this list or suggest changes to it by creating a pull request or opening an issue. Order Theory in the Context of Machine

Learning: an application An Invitation to Neuroalgebraic Geometry Algebraic Positional Encodings Learning Structure-Aware Representations of Dependent Types Accelerating Machine Learning Systems via Category Theory: Applications to Spherical Attention for Gene Regulatory Networks Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework Categorical Invariants of Learning Dynamics Product Interaction: An Algebraic Formalism for Deep Learning Architectures Presenting Neural Networks via Coherent Functors Operads for compositional reasoning in LLMs Learning Transfers: Kan Extensions for Neural Invariants Equivariance Equivariant neural networks and

piecewise linear representation theory Local Permutation Equivariance For Graph Neural Networks Stochastic Neural Network Symmetrisation in Markov Categories Metric Learning for Clifford Group Equivariant Neural Networks Categorification of Group Equivariant Neural Networks Equivariant Single View Pose Prediction Via Induced and Restricted Representations Characterizing the invariances of learning algorithms using category theory Mathematical Foundation of Interpretable Equivariant Surrogate Models Filter Equivariant Functions: A symmetric account of length-general extrapolation on lists Relational inductive biases on attention mechanisms Identifiable Equivariant Networks are

Layerwise Equivariant Gaussian Sheaf Neural Networks Theoretical Aspects of Lie Groupoid and Lie Algebroid Equivariant Convolutional Neural Networks Graph Neural Networks Graph Neural Networks are Dynamic Programmers Natural Graph Networks Sheaf Representation Learning Sheaf Neural Networks Sheaf Neural Networks with Connection Laplacians Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs Nonlinear Sheaf Diffusion in Graph Neural Networks Graph Convolutional Neural Networks as Parametric CoKleisli morphisms Learnable Commutative Monoids for Graph Neural Networks Sheaf Neural Networks for

Graph-based Recommender Systems Sheaf theory: from deep geometry to deep learning Asynchronous Algorithmic Alignment with Cocycles Topologically Attributed Graphs for Shape Discrimination Grothendieck Graph Neural Networks Framework: An Algebraic Platform for Crafting Topology-Aware GNNs Don't be Afraid of Cell Complexes!