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Concepts10

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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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📚TheoryIntermediate

Mixture of Experts (MoE)

A Mixture of Experts (MoE) routes each input to a small subset of specialized models called experts, enabling conditional computation.

#mixture of experts#moe#gating network+12
📚TheoryIntermediate

Key-Value Memory Systems

Key-Value memory systems store information as pairs where keys are used to look up values by similarity rather than exact match.

#key-value memory
Advanced
Group:
Attention & Transformer Theory
#attention
#scaled dot-product
+12
∑MathIntermediate

Softmax & Temperature Scaling

Softmax turns arbitrary real-valued scores (logits) into probabilities that sum to one.

#softmax#temperature scaling#logits+12
📚TheoryAdvanced

In-Context Learning Theory

In-context learning (ICL) means a model learns from examples provided in the input itself, without updating its parameters.

#in-context learning#transformer#attention+12
📚TheoryAdvanced

Transformer Expressiveness

Transformer expressiveness studies what kinds of sequence-to-sequence mappings a Transformer can represent or approximate.

#transformer expressiveness#universal approximation#self-attention+12
∑MathIntermediate

Positional Encoding Mathematics

Sinusoidal positional encoding represents each token’s position using pairs of sine and cosine waves at exponentially spaced frequencies.

#positional encoding#sinusoidal#transformer+11
⚙️AlgorithmIntermediate

Efficient Attention Mechanisms

Standard softmax attention costs O(n²) in sequence length because every token compares with every other token.

#linear attention#efficient attention#kernel trick+12
📚TheoryIntermediate

Self-Attention as Graph Neural Network

Self-attention can be viewed as message passing on a fully connected graph where each token (node) sends a weighted message to every other token.

#self-attention#graph neural network#message passing+11
📚TheoryIntermediate

Multi-Head Attention

Multi-Head Attention runs several attention mechanisms in parallel so each head can focus on different relationships in the data.

#multi-head attention#scaled dot-product attention#transformer+12
📚TheoryIntermediate

Scaled Dot-Product Attention

Scaled dot-product attention scores how much each value V should contribute to a query by taking dot products with keys K, scaling by \(\sqrt{d_k}\), applying softmax, and forming a weighted sum.

#scaled dot-product attention#softmax#transformer+10