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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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∑MathIntermediate

Random Walks on Graphs

A random walk on a graph moves from a node to one of its neighbors chosen uniformly at random at each step.

#random walk#transition matrix#stationary distribution+11
📚TheoryIntermediate

Graph Isomorphism & WL Test

Graph isomorphism asks whether two graphs are the same up to renaming vertices; the Weisfeiler–Leman (WL) test is a powerful heuristic that often distinguishes non-isomorphic graphs quickly.

#weisfeiler-leman
Advanced
Group:
Graph Theory for Deep Learning
#color refinement
#graph isomorphism
+10
📚TheoryIntermediate

Message Passing Framework

Message Passing Neural Networks (MPNNs) learn on graphs by letting nodes repeatedly exchange and aggregate messages from their neighbors.

#message passing neural network#mpnn#graph neural network+12
∑MathIntermediate

Graph Laplacian

The graph Laplacian translates a graph’s connectivity into a matrix that measures how much a function varies across edges.

#graph laplacian#laplacian matrix#normalized laplacian+11
📚TheoryIntermediate

Spectral Graph Theory

Spectral graph theory studies graphs by looking at eigenvalues and eigenvectors of matrices like the adjacency matrix A and Laplacians L and L_norm.

#spectral graph theory#laplacian#normalized laplacian+12