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Concepts152

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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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📚TheoryIntermediate

Minimum Description Length (MDL)

Minimum Description Length (MDL) picks the model that compresses the data best by minimizing L(M) + L(D|M).

#minimum description length#mdl#bic+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
12345
#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#message passing#pointnet+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
📚TheoryIntermediate

Sequence-to-Sequence with Attention

Sequence-to-sequence with attention lets a decoder focus on the most relevant parts of the input at each output step, rather than compressing everything into a single vector.

#sequence-to-sequence#attention#encoder-decoder+12
📚TheoryIntermediate

Temporal Convolutions

Temporal (causal) convolution computes each output at time t using only the current and past inputs, ensuring no future information leakage.

#temporal convolution#causal convolution#fir filter+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
📚TheoryIntermediate

LSTM & Gating Mechanisms

Long Short-Term Memory (LSTM) networks use gates (forget, input, and output) to control what information to erase, write, and reveal at each time step.

#lstm#forget gate#input gate+11
📚TheoryIntermediate

Recurrent Neural Network Theory

A Recurrent Neural Network (RNN) processes sequences by carrying a hidden state that is updated at every time step using h_t = f(W_h h_{t-1} + W_x x_t + b).

#recurrent neural network#rnn#backpropagation through time+12