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A curated collection of resources and research related to the geometry of representations in the brain, deep networks, and beyond
Symmetry and Geometry in Neural Representations
A curated collection of resources and research related to the geometry of representations in the brain, deep networks, and beyond, collaboratively generated on the Symmetry and Geometry in Neural Representations Slack Workspace.
This is a collaborative work-in-progress. Please contribute via PRs!
Contents
-
Educational Resources
- Differential Geometry + Lie Groups
- Algebra
- Topology
- Geometric Machine Learning
- Computational Neuroscience
- Datasets
- Software Libraries
- Conferences and Workshops
-
Research Papers
- Perspectives
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Neuroscience
- Vision
- Motor Control
- Spatial Navigation
- Abstract Representations
- Methods
-
Geometric Machine Learning
- Theory
- Estimation on Manifolds
- Dimensionality Reduction and Disentangling
- Group-Invariant and -Equivariant Representation Learning
Educational Resources
Differential Geometry + Lie Groups
Textbooks
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Lie Groups, Lie Algebras, and Representations (2003)
Brian C. Hall -
Differential Geometry and Lie Groups: A Computational Perspective (2020)
Gallier & Quaintance
Courses, Lectures, and Videos
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Differential Geometry for Computer Science
Justin Solomon -
Discrete Differential Geometry
CMU -
What is a Manifold?
XylyXylyX -
Manifolds
Robert Davie -
Lie Groups and Lie Algebras
XylyXylyX -
Lectures on Geometric Anatomy of Theoretical Physics
Frederic Schuller -
Weekend with Bernie (Riemann)
Søren Hauberg @ DTU
Notebooks and Blogposts
-
Introduction to Differential Geometry and Machine Learning
Geomstats Jupyter notebooks -
Differential Geometry for Machine Learning
Roger Grosse -
Manifolds: A Gentle Introduction
Brian Keng
Algebra
Textbooks
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Tensors in Computations (2021)
Lek-Heng Lim -
Aspects of Harmonic Analysis and Representation Theory (2021)
Gallier & Quaintance -
Representation Theory of Finite Groups (2012)
Bemjamin Steinberg
Courses, Lectures, and Videos
Topology
Courses, Lectures, and Videos
Geometric Machine Learning
Textbooks
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Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges (2021)
Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković -
Group Theoretical Methods in Machine Learning (2008)
Risi Kondor, PhD Thesis -
Equivariant Convolutional Networks (2021)
Taco Cohen, PhD Thesis -
An Introduction to Optimization on Smooth Manifolds (2022)
Nicolas Boumal
Courses, Lectures, and Videos
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An Introduction to Group-Equivariant Deep Learning (2022)
Erik Bekkers @ UvA -
COMP760: Geometry and Generative Models (2022)
Joey Bose and Prakash Panangaden @ MILA
Notebooks and Blogposts
-
Geometric foundations of Deep Learning
Michael Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković -
What does 2022 hold for Geometric & Graph ML?
Michael Bronstein -
Geometric Machine Learning for Shape Analysis with Jupyter Notebooks
Nina Miolane
Computational Neuroscience
Textbooks
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Introduction to the Theory of Neural Computation (1991)
John Hertz, Anders Krogh, Richard G Palmer -
Theoretical Neuroscience (2001)
Peter Dayan -
Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting (2006)
Eugene M. Izhikevich -
Principles of Neural Design (2015)
Peter Sterling & Simon Laughlin
Books - General Audience
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Rythyms of the Brain (2006)
Gyorgy Buzsaki -
Networks of the Brain (2010)
Olaf Sporns -
Models of the Mind: How Physics, Engineering and Mathematics Have Shaped Our Understanding of the Brain (2021)
Grace Lindsay
Courses, Lectures, and Videos
Datasets
Open-Source Neuroscience Datasets
- OpenNeuro
- NeuroVault
- CRCNS
- NeuroData Without Borders
- Allen Brain Atlas
- Kavli Institute for Systems Neuroscience Grid Cell Database
- The Natural Scenes Dataset
Software Libraries
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Geomstats
- Computation, statistics, and machine learning on non-Euclidean manifolds
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Giotto TDA
- Topological Data Analysis
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E3NN
- E(3)-equivariant neural networks
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equivariant-MLP
- Construct equivariant multilayer perceptrons for arbitrary matrix groups
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SHTOOLS
- Python library for computations involving spherical harmoics
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LieConv
- Generalizing convolutional neural networks for equivariance to Lie groups on arbitrary continuous data
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Open Neuroscience
- A database of open-source tools and software for neuroscience
Conferences and Workshops
- NeurIPS Workshop on Symmetry and Geometry in Neural Representations (2022)
- ICML Workshop on Topology, Algebra and Geometry in Machine Learning (2022
- ICLR Workshop on Geometrical and Topological Representation Learning (2022
- Workshop on Symmetry, Invariance and Neural Representations @ The Bernstein Conference (2022)
Research Papers
Math Tags
Perspectives
Neuroscience
Vision
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The Lie algebra of visual perception (1966)
William C.Hoffman
-
Representation of local geometry in the visual system (1987)
Jan Koenderink -
Operational Significance of Receptive Fields Assemblies (1989)
Jan Koenderink -
The Visual Cortex is a Contact Bundle (1989)
William C. Hoffman -
The neurogeometry of pinwheels as a sub-Riemannian contact structure (2003)
Jean Petitot
Motor Control
Spatial Navigation
Abstract Representations
Methods
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Manifold GPLVMs for discovering non-Euclidean latent structure in neural data (2020)
Kristopher Jensen, Ta-Chu Kao, Marco Tripodi, and Guillaume Hennequin
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Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity (2021)
Mehrdad Jazayeri, Srdjan Ostojic
Geometric Machine Learning
Theory
Estimation on Manifolds
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Intrinsic Statistics on Riemannian Manifolds (2011)
Xavier Pennec -
Geometric Statistics for Computational Anatomy (2016)
Nina Miolane, PhD Thesis -
Maximum Likelihood Estimators on Manifolds (2017)
Hatem Hajri, Salem Said, Yannick Berthoumieu
Dimensionality Reduction and Disentangling
Group-Invariant and -Equivariant Representation Learning
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How we know universals (1947)
Walter Pitts & Warren S. McCulloch -
Learning Lie groups for invariant visual perception (1999)
Rajesh Rao, Daniel Ruderman
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Learning the Lie groups of visual invariance (2007)
Xu Miao, Rajesh Rao
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Learning the irreducible representations of commutative Lie groups (2014)
Taco Cohen & Max Welling
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Group equivariant convolutional networks (2016)
Taco Cohen & Max Welling
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Spherical CNNs (2018)
Taco Cohen, Mario Geiger, Jonas Kohler, & Max Welling