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References

All the bibliographic references you need for ‘Hands-on Geometric Deep Learning’ newsletter

Uniform Manifold Approximation & Projection

  1. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction - L. McInnes, J Healy, J. Melvile - Tutte Institute for Mathematics and Computing, 2020

  2. Principal Component Analysis (PCA) - Geeks for Geeks, 2026

  3. Understanding t-SNE by Implementation - A. Orucu - towards Data Science, 2021

  4. UMAP documentation

  5. MNIST Dataset ylecun - Hugging Face

  6. IRIS data set - UC Irvine Machine Learning Repository

Insights into Logistic Regression on Riemannian Manifolds

  1. Tensor Calculus - YouTube - Eigenchris, 2023

  2. Introduction to Geometric Deep Learning - Hands-on Geometric Deep Learning, 2025

  3. Riemannian Manifolds: Foundational Concepts - Hands-on Geometric Deep Learning, 2025

  4. Vector and Covector fields in Python

  5. Exploring Geometric Learning with Geomstat Hands-on Geometric Deep Learning, 2025

  6. Manifold Geometry Meets Logistic Regression: The Rise of Hypergyroplanes - Hyperbole, 2024

  7. Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification - Z. Huang, R. Wang, S. Shan, X. Li, X. Chen - ‡University of Chinese Academy of Sciences, Beijing 2015

Hands-on Principal Geodesic Analysis

  1. Introduction to Differential Geometry J. Robbin, D Salamon - ETH Zurich

  2. Differential geometry for machine learning R. Grosse

  3. Riemannian Manifolds: Foundational Concepts - Hands-on Geometric Deep Learning, 2025

  4. Tensor Calculus - Eigenchris

  5. geomstats: a Python Package for Riemannian Geometry in Machine Learning N. Miolane, J. Mathe, C. Donnat, M. Jorda, X. Pennec

  6. Geomstats API

  7. Riemannian Manifolds: Hands-on with Hypershere - Hands-on Geometric Deep Learning, 2025

Dive into Functional Data Analysis

  1. Introduction to Differential Geometry

  2. Riemannian Manifolds: Foundational Concepts - Hands-on Geometric Deep Learning, 2025

  3. Riemannian Manifolds: Hands-on with Hypershere - Hands-on Geometric Deep Learning, 2025

  4. Functional Data Analysis Wikipedia

  5. Principal Component Analysis for Functional Data on Riemannian Manifolds and Spheres

  6. Geomstats - Github

  7. Introduction to Geomstats for Geometric Learning - Hands-on Geometric Deep Learning, 2025

Introduction to Geometric Deep Learning

  1. Limitations of Deep Neural Networks - S. Tsimenidis - 2020

  2. Geometric and spectral limitations in generative adversarial networks - K. Mahyar - Rutgers University Libraries, 20201

  3. Geometric deep learning: going beyond Euclidean data - M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, P. Vandergheynst - 2017

  4. Geometric foundations of Deep Learning - M. Bronstein - Medium, 2021

  5. A Practical Tutorial on Graph Neural Networks I. Ward, J. Joyner, C. Lickfold, Y. Guo, 2021

  6. A Comprehensive Introduction to Graph Neural Networks - A. Awan - Datacamp, 2021

  7. Graph Neural Networks: A gentle introduction A. Persson - YouTube, 2022

  8. Pytorch Geometric - PyG Documentation, 2024

  9. An introduction to Topological Data Analysis: fundamental and practical aspects for data scientist - F. Chazal, B. Michel - INRIA, FR, 2021

  10. Position: Topological Deep Learning is the New Frontier for Relational Learning - T. Papamarkou et all, 2024

  11. GUDHI - Geometry Understanding in Higher Dimensions - Documentation - INRIA, FR, 2024

  12. Differential Geometric Approaches to Machine Learning - A. Pouplin, PhD Thesis - Technical University of Denmark, 2023

  13. Differential geometry for generative modeling - S. Hauberg, 2025

  14. Github geomstats Documentation, 2023

  15. An Introduction to Deep Learning on Meshes - Practical course - R. Hanocka, H-T Liu, 2022

  16. Taming PyTorch Geometric for Graph Neural Networks - Hands-on Geometric Deep Learning - 2025

  17. Introduction to Differential Geometry - ETH Zurich

  18. Differential Geometric Structures W. Poor - Dover Publications, New York 1981

  19. Introduction to Smooth Manifolds J. Lee - Springer Science+Business media New York 2013

  20. Introduction to Lie Groups and Lie algebras - A Kirillov. Jr - SUNNY at Stony Brook

  21. Taming Symmetry: A Dive into Lie Groups with Python - Hands-on Geometric Deep Learning - 2025

  22. Shape Your Models with the Fisher-Rao Metric - Hands-on Geometric Deep Learning - 2025

  23. What is Fisher Information? - YouTube - Ian Collings 2022

Riemannian Manifolds: Foundational Concepts

  1. Introduction to Geometric Deep Learning: Limitations Current Models - Hands-on Geometric Deep learning, 2025

  2. Friendly Introduction to Geometric Deep Learning - Smooth Manifolds - Hands-on Geometric Deep learning, 2025

  3. Differential Geometric Structures W. Poor - Dover Publications, New York 1981

  4. Tensor Analysis on Manifolds R Bishop, S. Goldberg - Dover Publications, New York 1980

  5. Introduction to Smooth Manifolds J. Lee - Springer Science+Business media New York 2013

  6. Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning

  7. Introduction to Differential Geometry - ETH Zurich

  8. Introduction to Lie Groups and Lie algebras - A Kirillov. Jr - SUNNY at Stony Brook

  9. Mastering Special Orthogonal Groups With Practice Hands-on Geometric Deep learning, 2025

  10. Curvature-informed Graph Learning Hands-on Geometric Deep learning, 2026

Riemannian Manifolds: Hands-on with Hypersphere

  1. Riemannian Manifolds: Foundational Concepts

  2. Foundation of Geometry Learning

  3. Differential Geometric Structures - W. Poor - Dover Publications, New York 1981

  4. Introduction to Smooth Manifolds - J. Lee - Springer Science+Business media New York 2013

  5. Introduction to Differential Geometry - ETH Zurich

  6. Introduction to Geometric Learning in Python with Geomstats

  7. Introduction to Geomstats for Geometric Learning

Insights into k-Means on Riemannian Manifolds

  1. Tensor Calculus EigenChris - YouTube

  2. Introduction to Geometric Deep Learning - Hands-on Geometric Deep Learning, 2025

  3. Riemannian Manifolds: Foundational Concepts - Hands-on Geometric Deep Learning, 2025

  4. Riemannian Manifolds: Hands-on with Hypersphere - Hands-on Geometric Deep Learning, 2025

  5. Introduction to Geometric Learning in Python with Geomstats N. Miolane et all

  6. Geomstats API

  7. Clustering Data That Resides on a Low-Dimensional Manifold in a High-Dimensional Measurement Space A. Kak - Purdue University

  8. Geomstats: Hypersphere - Hands-on Geometric Deep Learning, 2025

  9. Clustering on the Unit Hypersphere using von Mises-Fisher Distributions A. Barnerjee, I. Dhillon, J. Ghosh, S. Sra - University of Texas, Austin

  10. Introduction to Lie Groups and Lie Algebras A. Kirillov, Jr - Dept. of Mathematics - SUNY as Stony Brook.

  11. scikit-learn.org

  12. Introduction to Geomstats for Geometric Learning - Hands-on Geometric Deep Learning, 2025

Exploring Geometric Learning with Geomstats

  1. geomstats: a Python Package for Riemannian Geometry in Machine Learning N. Miolane, J. Mathe, C. Donnat, M. Jorda, X. Pennec

  2. Geomstats API

  3. Introduction to Differential Geometry J. Robbin, D Salamon - ETH Zurich

  4. Differential geometry for machine learning - R. Grosse

  5. Riemannian Manifolds: 1 Foundation - Hands-on Geometric Deep Learning, 2025

  6. Riemannian Manifolds: 2. Hands-on with Hypershere - Hands-on Geometric Deep Learning, 2025

  7. Information Geometry: Near Randomness and Near Independence -

    K. Arvin, CT Dodson - Springer-Verlag 2008

  8. Introduction to Lie groups and Lie algebras A. Kirillov, Jr. - Dept. of Mathematics, SUNY at Stony Brook

  9. Basics of Classical Lie Groups: The Exponential Map, Lie Groups, and Lie Algebras

Reusable Neural Blocks in PyTorch

  1. Introduction to Geometric Deep Learning - Hands-on Geometric Deep Learning, 2025

  2. An Introduction to Graph Neural Networks: Models and Applications - M. Allamanis - Microsoft Research, 2021

  3. PyTorch

  4. PyTorch Geometric (PyG)

  5. An Introduction to Convolutional Neural Networks - K. O’Shea, R. Nash, 2015

  6. An introduction to Variational Autoencoders - D. Kingma, M. Welling - Google, 2019

  7. UML Class Diagram: Unified Modeling Language (UML) - Geeks for Geeks - System Design Tutorial, 2026

  8. Graph Convolutional Networks: Introduction to GNNs M. Labonne - towards Data Science, 2023

Block by block: Rethinking Deep Learning Architecture

  1. Design Patterns: Elements of Reusable Object-Oriented Software - E. Gamma, R. Helm, R. Johnson, J. Vlissides - Addison-Wesley Publishing 1995

  2. Reusable Neural Blocks in PyTorch - Hands-on Geometric Deep Learning, 2025

  3. Object Oriented Programming - Wikipedia

  4. Design Patterns: Builder Pattern - Java Design Patterns - Tutorials Point

  5. Introduction to Variational Autoencoders - D. Kingma, M. Welling - Foundations and Trends in Machine Learning, 2019

Einstein Summation in Geometric Deep Learning

  1. Einstein Summation Notation Course

  2. Einstein Summation Notation A. Sengupta

  3. Numpy einsum - numpy.org

  4. Torch einsum - PyTorch.org

  5. Building Multilayer Perceptron Models in PyTorch A. Tam - Machine Learning Mastery

  6. Kalman Filter Tutorial KalmanFilter.Net

  7. Limitation of Linear Kalman Filter - Geometric Learning, 2024

Taming PyTorch Geometric for Graph Neural Networks

  1. Fast Graph Representation Learning with PyTorch Geometric M. Fey, J. Lenssen - Dept. Computer Graphics - TU Dortmund University

  2. PyTorch Geometric Documentation pyg.org

  3. A Practical Tutorial on Graph Neural Networks I. Ward, J. Joyner, C. Lickfold, Y. Guo, M. Bennamoun

  4. YouTube: Build your first GNN A. Nandakumar

  5. YouTube: Graph Representation Learning - Stanford Education class CS224w-2018

  6. YouTube: ntroduction to Graph Neural Networks - P. Veličković

  7. Foundations and Frontiers of Graph Learning Theory Y. Huang et all. - IEEE

  8. Theory of Graph Neural Networks: Representation and Learning. S. Jegelka - CSAIL, MIT

  9. PyG Datasets

  10. GraphSAINT: Graph Sampling Based Inductive Learning Method H.Zeng, H. Zhou, A. Srivastava, R. Kannan, V. Prasanna

  11. Visualization of Graph Neural Networks P Nicolas

  12. NetworkX.org

Taming Symmetry: A Dive into Lie Groups with Python

  1. Introduction to Geometric Deep Learning - P. Nicolas - Substack

  2. Introduction to Differential Geometry - J. Robbin, D. Salamon - ETH Zurich

  3. Geometric Methods and Manifold Learning - M. Belkin - Ohio State University

  4. Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning - J. Gallier and J. Quaintance - Department of Computer and Information Science - University of Pennsylvania

  5. Basics of Classical Lie groups: The Exponential Map, Lie Groups, and Lie Algebras University of Pennsylvania

  6. Introduction to Lie Groups and Lie Algebras - A. Kirillov, Jr - SUNY at Stony Brook

  7. Overview of Geomstats for Geometric Learning P. Nicolas - Substack

  8. Geomstats Library - GitHub

Demystifying Graph Sampling & Walk Methods

  1. Taming PyTorch Geometric for Graph Neural Networks P. Nicolas - 2025

  2. A Practical Tutorial on Graph Neural Networks I. Ward, J. Joyner, C. Lickfold, Y. Guo, M. Bennamoun - 2012

  3. A Comprehensive Introduction to Graph Neural Networks - Datacamp - 2022

  4. Graph Neural Networks: A Gentil Introduction - YouTube. A. Persson

  5. Stanford CS: Machine Learning with Graphs - YouTube - CS-224 Stanford University Online

  6. Github - PyTorch Geometric

  7. PyTorch Geometric Datasets

  8. Flickr Dataset

Plug & Play Training for Graph Convolutional Networks

  1. Taming PyTorch Geometric for Graph Neural Networks - Hands-on Geometric Deep Learning, 2025

  2. Demystifying Graph Sampling & Walk Methods - Hands-on Geometric Deep Learning, 2025

  3. A Practical Tutorial on Graph Neural Networks I. Ward, J. Joyner, C. Lickfold, Y. Guo, M. Bennamoun - 2021

  4. A Comprehensive Introduction to Graph Neural Networks - Datacamp - 2022

  5. Graph Neural Networks: A Gentil Introduction - A. Persson - YouTube, 2023

  6. Stanford CS: Machine Learning with Graphs - YouTube - CS-224 Stanford - YouTube, 2021

  7. Reusable Neural Blocks in PyTorch - Hands-on Geometric Deep Learning, 2025

  8. Demystifying Graph Sampling & Walk Methods: Graph Samplers - Hands-on Geometric Deep Learning, 2025

  9. Demystifying Graph Sampling & Walk Methods: Data Splits - Hands-on Geometric Deep Learning, 2025

  10. Flickr Dataset - PyTorch Geometric API - Datasets

  11. Demystifying Graph Sampling & Walk Methods: Datasets - Hands-on Geometric Deep Learning, 2025

How to Tune a Graph Convolutional Network

  1. Taming PyTorch Geometric for Graph Neural Networks - Hands-on Geometric Deep Learning, 2025

  2. Demystifying Graph Sampling & Walk Methods - Hands-on Geometric Deep Learning, 2025

  3. Graph Loaders - Hands-on Geometric Deep Learning, 2025

  4. Hyperparameter tuning - Geeks for Geeks, 2022

  5. Announcing Optuna 4.2

  6. Optuna: Human-in-the-loop Optimization Tutorial Optuna

  7. Plug & Play Training for Graph Neural Networks - Hands-on Geometric Deep Learning, 2025

  8. Reusable Neural Blocks in PyTorch - Hands-on Geometric Deep Learning, 2025

  9. Block by Block: Rethinking Deep Learning Architecture

  10. Neighbor Node Sampling - Hands-on Geometric Deep Learning, 2025

  11. Graph Sampling Based Inductive Learning

Neighbors Matter: How Homophily Shapes Graph Neural Networks

  1. A Practical Tutorial on Graph Neural Networks I. Ward, J. Joyner, C. Lickfold, Y. Guo, M. Bennamoun - 2012

  2. A Comprehensive Introduction to Graph Neural Networks - Datacamp - 2022

  3. Graph Neural Networks: A Gentil Introduction - YouTube. A. Persson

  4. Stanford CS: Machine Learning with Graphs - YouTube - CS-224 Stanford University Online

  5. Github - PyTorch Geometric

  6. Taming PyTorch Geometric for Graph Neural Networks - Hands-on Geometric Deep Learning, 2025

  7. PyTorch Geometric Datasets

SE(3): The Lie Group That Moves the World

  1. Taming Symmetries: A Dive into Lie groups with Python

  2. SE(3) Transformers: 3D Roto-Translation Equivariant Attention Networks - F. Fuch, D. Worrall, V. Fisher, M. Welling

  3. Explore Geometric Learning with Geomstats P. Nicolas - Substack

  4. Geomstats Library - GitHub

  5. Introduction to Differential Geometry - J. Robbin, D. Salamon - ETH Zurich

  6. Basics of Classical Lie groups: The Exponential Map, Lie Groups, and Lie Algebras University of Pennsylvania

  7. Introduction to Lie Groups and Lie Algebras - A. Kirillov Jr. SUNY as Stony Brook

  8. The Lie group SE(3) - University of Pennsylvania

  9. Github.com/patnicolas/geometriclearning/Lie/LieSE3Group.py

Geometry of Closed-Form Statistical Manifolds

  1. Riemannian Manifolds: Foundational Concepts - Hands-on Geometric Deep Learning, 2025

  2. Riemannian Manifolds: Hands-on with Hypersphere - Hands-on Geometric Deep Learning, 2025

  3. Differential Geometric Structures W. Poor - Dover Publications, New York 1981

  4. Introduction to Smooth Manifolds J. Lee - Springer Science+Business media New York 2013

  5. Introduction to Differential Geometry - ETH Zurich

  6. What is Fisher Information? YouTube - Ian Collings

  7. An Elementary Introduction to Information Geometry F. Nielsen - Sony Computer Science Laboratories.

  8. Geomstats

  9. Exploring Geometry Learning with Geomstats - Hands-on Geometric Deep Learning, 2025

Shape Your Models with the Fisher-Rao Metric

  1. Geometry of Closed Form Statistical Manifolds - Hands-on Geometric Deep Learning, 2025

  2. Fisher Information Metric - Wikipedia

  3. What is Fisher Information? YouTube - Ian Collings

  4. Geomstats

  5. Exploring Geometry Learning with Geomstats - Hands-on Geometric Deep Learning, 2025

Mastering Special Orthogonal Groups With Practice

  1. Riemannian Manifolds: Foundational Concepts. - Hands-on Geometric Deep Learning, 2025

  2. Riemannian Manifolds: Hands-on with Hypersphere - - Hands-on Geometric Deep Learning, 2025

  3. Taming Symmetry: A Dive into Lie groups with Python - - Hands-on Geometric Deep Learning, 2025

  4. geomstats: a Python Package for Riemannian Geometry in Machine Learning N. Miolane, J. Mathe, C. Donnat, M. Jorda, X. Pennec

  5. Geomstats API

  6. SE(3): The Lie Group That Moves the World - Hands-on Geometric Deep Learning, 2025

  7. PyTorch Github

  8. Geomstats Github

  9. Riemannian Manifolds: Foundational Concepts - Core Elements - Hands-on Geometric Deep Learning, 2025

  10. Riemannian Manifolds: Foundational Concepts - Geodesic & Exponential Map - Hands-on Geometric Deep Learning, 2025

  11. Basics of Classical Lie groups: The Exponential Map, Lie groups and Lie algebra - J. Gallier - University of Pennsylvania CS-610 Advanced Geometric Methods in Computer Science, Chap 14, 2023

  12. Rodrigues’ rotation formula - Simsangcheol Medium, 2023

  13. Unit Quaternions and Rotations in SO(3) - Linear Algebra for Computer Vision and Machine Learning, CIS-5150 - University of Pennsylvania

A Journey into the Lie Group SO(4)

  1. Riemannian Manifolds: Foundational Concepts - Hands-on Geometric Deep learning, 2025

  2. Riemannian Manifolds: Hands-on with Hypersphere - Hands-on Geometric Deep learning, 2025

  3. Taming Symmetry: A Dive into Lie groups with Python -Hands-on Geometric Deep learning, 2025

  4. geomstats: a Python Package for Riemannian Geometry in Machine Learning N. Miolane, J. Mathe, C. Donnat, M. Jorda, X. Pennec

  5. Geomstats API

  6. Exploring Geometry Learning with Geomstats - Hands-on Geometric Deep learning, 2025

  7. Mastering Special Orthogonal Groups With Practice - Hands-on Geometric Deep learning, 2025

  8. Rodrigues’s Formula - Hands-on Geometric Deep learning, 2025

  9. Rodrigues’ rotation formula - Simsangcheol Medium, 2023

  10. so(4) is isomorphic to so(3) + so(3) math.stackexchange

  11. Manim Tutorials - manim.org

  12. Manim Community - manim.org

  13. Manim Example Scenes - manim.org

From Nodes to Complexes: A Guide to Topological Deep Learning

  1. Introduction to Geometric Deep Learning: Topological Data Analysis P. Nicolas - Hands-on Geometric Deep learning

  2. Introduction to Geometric Deep Learning P. Nicolas - Hands-on Geometric Deep learning

  3. A Practical Tutorial on Graph Neural Networks

  4. A Comprehensive Introduction to Graph Neural Networks - Datacamp

  5. Demystifying Graph Sampling & Walk Methods

  6. Taming PyTorch Geometric for Graph Neural Networks

  7. Topological Deep Learning: Going Beyond Graph Data M. Hajij et all

  8. Architectures of Topological Deep Learning: A Survey on Topological Neural Networks M. Papillon, S. Sanborn, M. Hajij, N. Miolane

  9. Visualization of Graph Neural Networks P. Nicolas - LinkedIn Newsletter

  10. NetworkX Org

  11. TopoNetX Documentation - PyT-Team

  12. TopoModelX Documentation - PyT-Team

Exploring Simplicial Complexes for Deep Learning: Concepts to Code

  1. From Nodes to Complexes: A Guide to Topological Deep Learning - Hands-on Geometric Deep learning - 2025

  2. A Practical Tutorial on Graph Neural Networks I. Ward, J. Joyner, C. Lickfold, Y. Guo, M. Bennamoun

  3. YouTube: Build your first GNN A. Nandakumar

  4. Introduction to Geometric Deep Learning Hands-on Geometric Deep learning - 2025

  5. Demystifying Graph Sampling & Walk Methods - Hands-on Geometric Deep learning - 2025

  6. Taming PyTorch Geometric for Graph Neural Networks Hands-on Geometric Deep learning - 2025

  7. Topological Deep Learning: Going Beyond Graph Data M. Hajij et all

  8. Architectures of Topological Deep Learning: A Survey on Topological Neural Networks M. Papillon, S. Sanborn, M. Hajij, N. Miolane

  9. Introduction to Geometric Deep Learning: Topological Data Analysis P. Nicolas - Hands-on Geometric Deep learning

  10. From Nodes to Complexes: A Guide to Topological Deep Learning - NetworkX

  11. From Nodes to Complexes: A Guide to Topological Deep Learning - TopoX

  12. NetworkX Org

  13. TopoNetX Documentation - PyT-Team

  14. Visualization of Graph Neural Networks P. Nicolas - LinkedIn Newsletter - 2025

Topological Lifting of Graph Neural Networks

  1. NetworkX Hands-on Geometric Deep Learning - 2025

  2. Taming PyTorch Geometric for Graph Neural Networks Hands-on Geometric Deep Learning - 2025

  3. TopoX Hands-on Geometric Deep Learning - 2025

  4. Exploring Simplicial Complexes for Deep Learning Hands-on Geometric Deep Learning - 2025

  5. Hodge-Laplacian Hands-on Geometric Deep Learning - 2025

  6. PyTorch Geometric Graph Datasets

  7. Github TopoNetX - transform/graph_to_simplicial_complex.py

  8. Github TopoNetX - algorithms/spectrum.py

Revisiting Inductive Graph Neural Networks

  1. Taming PyTorch Geometric for Graph Neural Networks Hands-on Geometric Deep Learning - 2025

  2. Plug & Play Training for Graph Convolutional Networks Hands-on Geometric Deep Learning - 2025

  3. GraphSAGE: Inductive Representation Learning on Large Graphs J. Leskovec, SNAP - Stanford University

  4. Inductive Representation Learning on Large Graphs. W.L. Hamilton, R. Ying, and J. Leskovec 2017.

  5. Reusable Neural Blocks in PyTorch & PyG Hands-on Geometric Deep Learning - 2025

  6. Block by block: Rethinking Deep Learning Architecture Hands-on Geometric Deep Learning - 2025

  7. Taming PyTorch Geometric for Graph Neural Networks: Graph Loaders Hands-on Geometric Deep Learning - 2025

  8. Demystifying Graph Sampling & Walk Methods Hands-on Geometric Deep Learning - 2025

Graph Convolutional or SAGE Networks? Shootout

  1. A Gentle Introduction to Graph Neural Networks B. Sanchez-Lengeling, E. Reif, A. Pearce, A. Wiltschko - Distill - 2021

  2. An introduction to Robust Graph Convolutional Networks M. Najafi, P. S. Yu - University of Illinois at Chicago - 2021

  3. Revisiting Inductive Graph Neural Networks Hands-on Geometric Deep learning - 2025

  4. Inductive Representation Learning on Large Graphs W. Hamilton, R. Ying, J. Leskovec - Dept. of Computer Science - Stanford University - 2017

  5. Graph SAGE vs Graph Convolution Hands-on Geometric Deep learning - 2025

  6. Graph Neural Network Neural Components Hands-on Geometric Deep learning - 2025

  7. GraphSAGE block Hands-on Geometric Deep learning - 2025

  8. Plug & Play Training for Graph Convolutional Networks Hands-on Geometric Deep learning - 2025

  9. How to Tune a Graph Convolutional Network Hands-on Geometric Deep learning - 2025

  10. GraphSAGE Model Hands-on Geometric Deep learning - 2025

  11. Taming Graph Neural Networks with PyTorch Geometric - Graph DatasetsHands-on Geometric Deep learning - 2025

  12. PyTorch Geometric Benchmark

Slimming the Graph Neural Network Footprint

  1. Automatic Mixed Precision examples PyTorch Documentation

  2. When to set pin_memory to true? K. Zhong - PyTorch Documentation

  3. How Activation Checkpointing enables scaling up training deep learning models - Medium Y. Beer, O. Bar - Medium

  4. Demystifying Graph Sampling & Walk Methods - Hands-on Geometric Deep Learning, 2025

  5. Decorators in Python Geeks for Geeks, 2025

  6. Reference API: torch.cuda PyTorch documentation

  7. MPS backend PyTorch documentation

  8. Plug & Play Training of Graph Convolutional Networks - Hands-on Geometric Deep Learning, 2025

Graphs Reimagined: The Power of Cell Complexes

  1. From Nodes to Complexes: A Guide to Topological Deep Learning - Hands-on Geometric Deep Learning

  2. Exploring Simplicial Complexes for Deep Learning: Concepts to Code - Hands-on Geometric Deep Learning, 2025

  3. Don’t be Afraid of Cell Complexes: An Introduction from an Applied PerspectiveJ. Hoppe, V. Grande, M. Schaub - RWTH Aachen University, 2025

  4. Cell Complex Neural Networks Hajij, Istvan, Zamzmi, 2023

  5. Graph Laplacian: From Basic Concepts to Modern Applications - H. Mhadi - Medium, 2025

  6. A Gentle Introduction to the Laplacian S. Cristina - Machine Learning Mastery, 2022

  7. CW Complex - Wikipedia

  8. TopoX: A Suite of Python Packages for Machine Learning on Topological Domains M. Hajij et all, 2025

  9. Github - PyTeam/TopoNetX

Exploring Hypergraphs with TopoX Library

  1. Exploring Simplicial Complexes for Deep Learning: Concepts to Code - Hands-on Geometric Deep Learning, 2025

  2. Graphs Reimagined: The Power of Cell Complexes - Hands-on Geometric Deep Learning, 2025

  3. A Gentle Introduction to Hypergraph Mathematics - HyperNetX, 2022

  4. Introduction to Hypergraphs [Graph Theory] - V. Sine - YouTube, 2022

  5. A Gentle Introduction to the Laplacian S. Cristina - Machine Learning Mastery, 2022

  6. TopoX: A Suite of Python Packages for Machine Learning on Topological Domains M. Hajij et all, 2025

  7. Github - PyTeam/TopoNetX

  8. Graphs Reimagined: The Power of Cell Complexes - TopoNetX Hands-on Geometric Deep Learning, 2025

  9. Graphs Reimagined: The Power of

Understanding Data Through Persistence Diagrams

  1. Topological Methods in Machine Learning B. COSKUNUZER,, CÜNEYT GÜRCAN AKÇORA, 2024

  2. Demystifying the Math of Geometric Deep Learning - Topology Hands-on Geometric Deep Learning, 2025

  3. From Nodes to Complexes: A Guide to Topological Deep Learning Hands-on Geometric Deep Learning, 2025

  4. Exploring Simplicial Complexes for Deep Learning: Concepts to Code Hands-on Geometric Deep Learning, 2025

  5. Persistent homology: a step-by-step introduction for newcomers U. Fugacci, S. Scaramuccia, F. Luricich, L. De Floriani - Smart Tools and Apps in Computer Graph, 2016

  6. Persistent Homology: A Pedagogical Introduction with Biological Applications U. J. Kemme, C. A. Agyingi, 2025

  7. Barcodes: The Persistent Topology of Data R. Ghrist, 2007

  8. Scitkit-Learn TDA 1.1.1 Documentation

  9. ripser.py documentation

  10. Scikit_TDA Github

  11. Ripser Github

  12. scikit-learn make_swiss_roll

  13. Torus - Wikipedia

Turbocharging Neural Networks with Taichi Language

  1. High-performance parallel programming in Python Taichi Lang

  2. Taichi Github

  3. The Taichi Programming Language - YouTube - SIGGRAPH Ethan Hu, 2020

  4. A brief history of Taichi Programming Language. YouTube - Taichi Graphics, 2022

  5. Playing with Your Data - YouTube - Taichi Graphic SIGGRAPH Asia, 2022

  6. Taichi LVM Sparse Runtime

  7. Taichi Differential Programming

Curvature-informed Graph Learning

  1. Over-squashing in Graph Neural Networks: A comprehensive survey - S. Akansha, ScienceDirect - Neural Computing, 2025

  2. Over smoothing issue in graph neural network - A. Ait Aomar - Towards Data Science, 2021

  3. Shape Analysis Series - YouTube - Justin Solomon, 2023

  4. Digital Geometry Processing - Discrete Differential Geometry - H. Lie, 2015

  5. From Nodes to Complexes: A Guide to Topological Deep Learning - Hands-on Geometric Deep Learning, 2025

  6. Graphs Reimagined: The Power of Cell Complexes - Hands-on Geometric Deep Learning, 2025

  7. Discrete Ricci Curvature with Applications Dr. Y. Olliver, YouTube, 2011

  8. The Earth Mover’s Distance - Stanford University, 2023

  9. The Sinkhorn Knopp Algorithm — Without Proof - F Lanke Fu Tarimo - Medium, 2021

  10. Understanding Over-Squashing and Bottlenecks on Graph via Curvature - J Topping, F. Di Giovanni, B. Chamberlain, X. Dong, M. Bronstein - Imperial College London, Twitter, 2022

  11. PyTorch Github

  12. PyTorch Geometric Github PyG Team

  13. Floyd-Warshall Algorithm - Algorithms for Competitive Programming, 2025

  14. Riemannian Manifolds: Hands-on with Hypersphere - Geodesics - Hands-on Geometric Deep Learning, 2025

Visualization Tools for Geometric Deep Learning

  1. Python Libraries for Mesh, Point Cloud, and Data Visualization

  2. Matplotlib Tutorials

  3. Matplotlib - Animation Examples

  4. Manim Tutorials - manim.org

  5. Manim Community - manim.org

  6. Manim Example Scenes - manim.org

  7. PyVista

  8. PlotPy Documentation

  9. SE(3): The Lie Group That Moves the World - Hands-on Geometric Deep Learning, 2025

  10. Graph Convolutional or SAGE Networks? - Hands-on Geometric Deep Learning, 2025

Mathematics of Abstract World Models

  1. World Models: The Next Frontier in Our Path to AGI is Here - I. de Gregorio - Medium, 2023

  2. Nvidia Glossary: World Models Nvidia 2026

  3. AI and World Models. - R. Worden - Active Inference Institute, 2026

  4. World Models D. Ha, J Schmidhuber, 2018

  5. Sora as a World Model? F. D. Puspitasar et all, 2026

  6. Can a Bayesian Oracle Prevent Harm from anAgent? - Y. Bengio et all, 2024

  7. LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristic - R. Balestriero, Y. LeCun, 2025

  8. From Words to Worlds: Spatial Intelligence is AI’s Next Frontier - Dr. Fei-Fei Lie - Substack, 2025

  9. Learning Abstract World Models with a Group-Structured Latent Space - T. Delliaux, N-K Vu, V. Francois-Lavet, E. Van der Pol, 2025

  10. VJEPA: Variational Joint Embedding Predictive Architectures as Probabilistic World Models - Y. Huang, 2026

  11. VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning - A. Bardes, J. Ponce, Y. LeCun, 2021

  12. Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments - H.J. Lillemark, B. Huang, F. Zhan, Y. Du, T. Anderson Keller, 2025

  13. Symplectic Generative Networks (SGNs): A Hamiltonian Framework for Invertible Deep Generative Modeling - A. Aich, A. B. Aich, 2025

  14. On the Spatiotemporal Dynamics of Generalization in Neural Networks - Z. Wei, 2026

  15. Demystifying the Math of Geometric Deep Learning-Graph Theory - - Hands-on Geometric Deep Learning, 2025

  16. Demystifying the Math of Geometric Deep Learning-Topology - Hands-on Geometric Deep Learning, 2025

  17. Demystifying the Math of Geometric Deep Learning-Differential Geometry - Hands-on Geometric Deep Learning, 2025

Graphs Deserve Some Attention

  1. Graph Attention Networks P. Velickovic, G, Cucurull, A. Casanova, A. Romero, P. Lio, Y. Bengio, 2018

  2. Demystifying Graph Sampling & Walk Methods - Hands-on Geometric Deep Learning, 2025

  3. Neighbors Matter: How Homophily Shapes Graph Neural Networks - Hands-on Geometric Deep Learning, 2025

  4. Taming PyTorch Geometric for Graph Neural Networks - Hands-on Geometric Deep Learning, 2025

  5. Graph Convolutional or SAGE Networks? Shootout - Hands-on Geometric Deep Learning, 2025

  6. Revisiting Inductive Graph Neural Networks: Transductive vs. Inductive Graph Networks - Hands-on Geometric Deep Learning, 2025

  7. PyTorch nn package

  8. PyTorch Geometric Github - PyG Team

  9. Block by block: Rethinking Deep Learning Architecture - Hands-on Geometric Deep Learning, 2025

  10. Demystifying Graph Sampling & Walk Methods - Graph Loaders - Hands-on Geometric Deep Learning, 2025

  11. Plug & Play Training for Graph Convolutional Networks - Setting up training - Hands-on Geometric Deep Learning, 2025

Benchmarking Topological Deep Learning

  1. TopoBench: A Framework for Benchmarking Topological Deep Learning. L. Telyatnikov et All. 2025

  2. Exploring Simplicial Complexes for Deep Learning: Concepts to Code - Hands-on Geometric Deep Learning, 2025

  3. Graphs Reimagined: The Power of Cell Complexes - Hands-on Geometric Deep Learning, 2025

  4. Exploring Hypergraphs with TopoX Library - Hands-on Geometric Deep Learning, 2025

  5. Topological Lifting of Graph Neural Networks - Hands-on Geometric Deep Learning, 2025

  6. TopoX: A Suite of Python Packages for Machine Learning on Topological Domains - M. Hajij et all, 2024

  7. Taming PyTorch Geometric for Graph Neural Networks - Hands-on Geometric Deep Learning, 2025

  8. PyTorch Geometric - Dataset Cheatsheet

  9. TUDataset: A collection of benchmark datasets for learning with graphs C.

A Guided Tour of the Joint Embedding Predictive Architecture

  1. World Models: The Next Frontier in Our Path to AGI is Here - I. de Gregorio - Medium, 2023

  2. AI and World Models. - R. Worden - Active Inference Institute, 2026

  3. World Models D. Ha, J Schmidhuber, 2018

  4. Learning Abstract World Models with a Group-Structured Latent Space - T. Delliaux, N-K Vu, V. Francois-Lavet, E. Van der Pol, 2025

  5. From Words to Worlds: Spatial Intelligence is AI’s Next Frontier - Dr. Fei-Fei Lie - Substack, 2025

  6. A path towards autonomous machine intelligence - Open Review - Y. LeCun, 2022

  7. Critiques of World Models - E. Xing, M, Deng, J. Hou, Z. Hu, 2025

  8. V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning - M. Assran et all - 2025

  9. Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture - M. Assran, Q. Duval, I. Misra, P. Bojanowski, P. Vincent, M. Rabbat, Y. LeCun, N. Ballas - 2023

  10. LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics - R. Balestriero, Y. LeCun - 2025

  11. Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud - A. Saito, P. Kudeshia, J. Poovvancheri - 2025

  12. ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model - H. Zhang, Y. Li, S. He, T. Nagarajan, M. Chen, J. Lu, A. Li, Y. Fu - 2026

  13. LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels - L. Maes, Q. Le Lidec, D. Scieur, Y. LeCun, R. Balestriero - 2026

  14. Temporal Straightening for Latent Planning - Y. Wang , O. Bounou , G. Zhou, R. Balestriero , T. G. J. Rudner , Y. LeCun - 2026

  15. JEPA-VLA: Video Predictive Embedding is Needed for VLA Models - S. Miao, N. Feng, J. Wu, Y. Lin, X. He, D. Li, M. Long - 2026

  16. Var-JEPA: A Variational Formulation of the Joint-Embedding Predictive Architecture – Bridging Predictive and Generative Self-Supervised Learning - M. Gogl, C. Yau - 2026

  17. Social-JEPA: Emergent Geometric Isomorphism in Independently Trained World Models - H. Zhang et all - 2026

  18. Le MuMo JEPA: Multi-Modal Self-Supervised Representation Learning with Learnable Fusion Tokens - C. Cornelissen, S. Leroux, P. Simoens - 2026

  19. Laya: A LeJEPA Approach to EEG via Latent Prediction over Reconstruction - S. Panchavati, U. Panchavati, C. Arnold, W. Speier - 2026

  20. BiJEPA: Bi-directional Joint Embedding Predictive Architecture for Symmetric Representation Learning - Y. Huang - 2026

  21. GeoWorld: Geometric World Models - Z. Zhang, D. Li, I. Reid, R. Hartley - 2026

  22. US-JEPA: A Joint Embedding Predictive Architecture for Medical Ultrasound - A. Radhachandran, V. Ivezic, S. Athreya, R. Anilkumar, C. W. Arnold, W. Speier - 2026

  23. VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning - A. Bardes, J. Ponce, Y. LeCun, 2021

  24. Hugging Face - Daily Papers Joint Embedding Predictive Architecture March 2026

  25. Reinforcement Learning: An Introduction - R. Sutton, A. Barto - The MIT Press, 2015

Hands-on Stochastic Gradient Langevin Dynamics

  1. Stochastic Gradient Descent: A Basic Explanation - M. Mishra - Medium, 2023

  2. Gentle Introduction to the Adam Optimization Algorithm for Deep Learning - J. Brownlee - Machine Learning Mastery, 2021

  3. Langevin Dynamics - N. Katz - Towards Data Science, 2021

  4. The promises and pitfalls of Stochastic Gradient Langevin Dynamics - N. Brosse, E. Moulines, A. Durmus, 2018

  5. Bayesian Learning via Stochastic Gradient Langevin Dynamics M. Welling, Y. Whye The, 2011

  6. PyTorch Github

  7. Ackley Function - Simon Fraser University

  8. Rosenbrock Function - Simon Fraser University

  9. The Isotropic Gaussian: The Most Beautiful Equation Nobody Explained to You - Dr S. AI - Medium, 2026

  10. Matplotlib Github

Decoding Neural Manifolds

  1. An Introduction to Biological Neurons - Zenva, 2018

  2. From sensory to perceptual manifolds: The twist of neural geometry - H. Ma, L. Jiang, T. Liu, J. Liu, 2025

  3. A unifying perspective on neural manifolds and circuits for cognition - C, Langdon, M. Genkin, T. A. Engel - Nature Reviews, Neuroscience, 2023

  4. Neural Manifolds for the Control of Movement - J.A. Gallego, M.G. Perich., L.E. Miller, S.A. Solla - YouTube, 2017

  5. Computation and Neural Manifolds - David Barack, UC Merced - YouTube, 2023

  6. Manifold Learning: Theory and Applications: -Y. Ma, Y. Fu, - CRC Press, 2012

  7. Introduction to IsoMAP: Isoscapes Modeling, Analysis, and Prediction - Isoscapes, 2011

The Irreverent Geometry of Consciousness

  1. Decoding Neural Manifolds - Hands-on Geometric Deep Learning, 2026

  2. Consciousness, AI, and the Limits of Scientific Explanation - B. Love - Empirical.ai, 2026

  3. AI Consciousness: The Maker Doubts - H. Schrijfuis, 2026

  4. Will AI be conscious in the future? Here’s what a philosopher and a neuroscientist think - W. Gillet, 2026

  5. Neural Manifolds for the Control of Movement - J.A. Gallego, M.G. Perich., L.E. Miller, S.A. Solla, 2017

  6. Neural manifold analysis of brain circuit dynamics in health and disease - R. Mitchell-Heggs, S. Prado, G. P. Gava1, M, A. Go, S. R. Schultz, 2022

  7. Introduction to IsoMAP: Isoscapes Modeling, Analysis, and Prediction - Isoscapes, 2011

  8. Contact Geometry of the Visual Cortex - M. Marcolli - Geometry of Neuroscience - Caltech, 2026

  9. From sensory to perceptual manifolds: The twist of neural geometry - H. Ma, L. Jiang, T. Liu, J. Liu, 2025

  10. Shape your Models with the Fisher-Rao Metric - Hands-on Geometic Deep Learning, 2025

  11. Taming Symmetry with Lie groups: Lie Groups Basics - Hands-on Geometic Deep Learning, 2025

  12. The emergence of geometric worldviews in qualia space - YouTube - 3rd International Symposium on the Mathematics of Neuroscience - P. Resende, 2022

  13. Qualia and Symmetry - YouTube - R. Kanai - Models of Consciousness Conferences, 2022

  14. Sheaves are the Canonical Data Structure for Information Integration M. Robinson, 2015

  15. On Brain as a Mathematical Manifold: Neural Manifolds, Sheaf Semantics, and Leibnizian Harmony - T. Inoué - Faculty of Informatics, Yamato University, Osaka, Japan, 2026

  16. Sheaf theory: from deep geometry to deep learning - A. Ayzenberg, G. Magai, T. Gebhart, G. Solomadin, 2025

Persistent Homology for the Rest of Us

  1. Demystifying the Math of Geometric Deep Learning - Topology - Hands-on Geometric Deep Learning, 2025

  2. From Nodes to Complexes: A Guide to Topological Deep Learning - Hands-on Geometric Deep Learning, 2025

  3. Exploring Simplicial Complexes for Deep Learning: Concepts to Code - Hands-on Geometric Deep Learning, 2025

  4. Topological Methods in Machine Learning - B. COSKUNUZER,, CÜNEYT GÜRCAN AKÇORA, 2024

  5. Persistent homology: a step-by-step introduction for newcomers - U. Fugacci, S. Scaramuccia, F. Luricich, L. De Floriani - Smart Tools and Apps in Computer Graph, 2016

  6. Persistent Homology: A Pedagogical Introduction with Biological Applications - U. J. Kemme, C. A. Agyingi, 2025

  7. Scitkit-Learn TDA 1.1.1 Documentation

  8. ripser.py documentation

  9. Scikit_TDA Github

  10. Ripser Github

  11. Understanding Data Through Persistence Diagrams - Hands-on Geometric Deep Learning, 2025

  12. Barcodes: The Persistent Topology of Data 0 R. Ghrist, 2007

Fractal Dimension for Configuring Convolutional Networks

  1. Fractals and the Fractal Dimension Vanderbilt University - Psychology department, 2022

  2. An Introduction to Dimension Theory and Fractal Geometry: Fractal Dimensions and Measures - E. Pearse, 2018

  3. Measuring fractal dimension by box-counting - PorePy, 2021

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