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Concepts172

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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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∑MathAdvanced

f-Divergences

An f-divergence measures how different two probability distributions P and Q are by averaging a convex function f of the density ratio p(x)/q(x) under Q.

#f-divergence#csiszar divergence#kullback–leibler+11
∑MathAdvanced

Copulas & Dependency Structures

A copula is a function that glues together marginal distributions to form a multivariate joint distribution while isolating dependence from the margins.

#copula
12345
Advanced
#sklar's theorem
#gaussian copula
+12
📚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
∑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
📚TheoryAdvanced

Mamba & Selective State Spaces

Mamba uses a state-space model whose parameters are selected (gated) by the current input token, letting the model adapt its memory dynamics at each step.

#mamba#selective state space#ssm+12
📚TheoryAdvanced

CTC Loss (Connectionist Temporal Classification)

CTC loss trains sequence models when you do not know the alignment between inputs (frames) and outputs (labels).

#ctc loss#connectionist temporal classification#forward backward+12
⚙️AlgorithmAdvanced

Wake-Sleep Algorithm

The Wake–Sleep algorithm trains a pair of models: a generative model that explains how data are produced and a recognition model that guesses hidden causes from observed data.

#wake-sleep#helmholtz machine#generative model+12
📚TheoryAdvanced

Variational Dropout & Bayesian Deep Learning

Dropout can be interpreted as variational inference in a Bayesian neural network, where applying random masks approximates sampling from a posterior over weights.

#bayesian neural networks#variational inference#dropout+12
📚TheoryAdvanced

Normalizing Flow Variational Inference

Normalizing-flow variational inference enriches the variational family by transforming a simple base distribution through a sequence of invertible, differentiable mappings.

#normalizing flows#variational inference#elbo+12
⚙️AlgorithmAdvanced

Stochastic Variational Inference

Stochastic Variational Inference (SVI) scales variational inference to large datasets by taking noisy but unbiased gradient steps using minibatches.

#stochastic variational inference#elbo#variational inference+12