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Concepts8

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📐Linear Algebra15📈Calculus & Differentiation10🎯Optimization14🎲Probability Theory12📊Statistics for ML9📡Information Theory10🔺Convex Optimization7🔢Numerical Methods6🕸Graph Theory for Deep Learning6🔵Topology for ML5🌐Differential Geometry6∞Measure Theory & Functional Analysis6🎰Random Matrix Theory5🌊Fourier Analysis & Signal Processing9🎰Sampling & Monte Carlo Methods10🧠Deep Learning Theory12🛡️Regularization Theory11👁️Attention & Transformer Theory10🎨Generative Model Theory11🔮Representation Learning10🎮Reinforcement Learning Mathematics9🔄Variational Methods8📉Loss Functions & Objectives10⏱️Sequence & Temporal Models8💎Geometric Deep Learning8

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🔷All∑Math⚙️Algo🗂️DS📚Theory

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📚TheoryAdvanced

Weisfeiler-Leman Hierarchy

The Weisfeiler–Leman (WL) hierarchy is a family of color-refinement procedures that iteratively color vertices (or k-tuples of vertices) to capture graph structure for isomorphism testing.

#weisfeiler-leman#color refinement#graph isomorphism+12
📚TheoryIntermediate

Message Passing on Meshes & Point Clouds

Message passing treats meshes and point clouds as graphs where nodes exchange information with neighbors to learn useful features.

#geometric deep learning
Advanced
Group:
Geometric Deep Learning
#message passing
#pointnet
+12
∑MathAdvanced

Spherical Harmonics & SO(3) Representations

Spherical harmonics are smooth wave patterns on the sphere that form an orthonormal basis, much like sine and cosine form a basis on the circle.

#spherical harmonics#so(3)#wigner d-matrix+12
📚TheoryAdvanced

E(n) Equivariant Neural Networks

E(n)-equivariant neural networks are models whose outputs transform predictably when inputs are rotated, translated, or reflected in n-dimensional Euclidean space.

#e(n)-equivariance#euclidean group#so(n) and o(n)+12
📚TheoryAdvanced

Gauge Equivariant Networks

Gauge equivariant networks are neural networks that respect local symmetries (gauges) on manifolds, such as how vectors rotate when you change the local reference frame on a surface.

#gauge equivariant networks#geometric deep learning#manifold learning+12
📚TheoryIntermediate

Group Convolution

Group convolution combines two functions defined on a group by summing over products aligned by the group operation, generalizing the usual circular convolution on integers modulo n.

#group convolution#finite group#circular convolution+10
📚TheoryIntermediate

Equivariance & Invariance

Equivariance means that applying a transformation before a function is the same as applying a corresponding transformation after the function.

#equivariance#invariance#group action+12
∑MathIntermediate

Group Theory for Neural Networks

Group theory gives a precise language for symmetries, and neural networks can exploit these symmetries to learn faster and generalize better.

#group theory#neural networks#equivariance+12