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Concepts13

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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

Disentangled Representations

Disentangled representations aim to encode independent factors of variation (like shape, size, or color) into separate coordinates of a latent vector.

#disentangled representations#independent factors#total correlation+12
📚TheoryIntermediate

Self-Supervised Learning Theory

Self-supervised learning (SSL) teaches models to learn useful representations from unlabeled data by solving proxy tasks created directly from the data.

#self-supervised learning
12
Advanced
Filtering by:
#mutual information
#contrastive learning
#infonce
+12
📚TheoryIntermediate

Contrastive Learning

Contrastive learning teaches models by pulling together similar examples (positives) and pushing apart dissimilar ones (negatives).

#contrastive learning#infonce#nt-xent+12
📚TheoryAdvanced

Information Bottleneck in Deep Learning

The Information Bottleneck (IB) principle formalizes learning compact representations T that keep only the information about X that is useful for predicting Y.

#information bottleneck#variational information bottleneck#mutual information+11
📚TheoryAdvanced

Rate-Distortion Theory

Rate–distortion theory tells you the minimum number of bits per symbol needed to represent data while keeping average distortion below a target D.

#rate-distortion#mutual information#blahut-arimoto+12
📚TheoryAdvanced

Information Bottleneck

The Information Bottleneck (IB) principle formalizes the tradeoff between compressing an input X and preserving information about a target Y using the objective min_{p(t|x)} I(X;T) - \beta I(T;Y).

#information bottleneck#mutual information#kl divergence+12
📚TheoryAdvanced

Information-Theoretic Lower Bounds

Information-theoretic lower bounds tell you the best possible performance any learning algorithm can achieve, regardless of cleverness or compute.

#information-theoretic lower bounds#fano inequality#le cam method+12
📚TheoryAdvanced

Representation Learning Theory

Representation learning aims to automatically discover features that make downstream tasks easy, often without human-provided labels.

#representation learning#contrastive learning#infonce+12
📚TheoryAdvanced

Information Bottleneck Theory

Information Bottleneck (IB) studies how to compress an input X into a representation Z that still preserves what is needed to predict Y.

#information bottleneck#mutual information#variational information bottleneck+12
📚TheoryIntermediate

Contrastive Learning Theory

Contrastive learning learns representations by pulling together positive pairs and pushing apart negatives using a softmax-based objective.

#contrastive learning#infonce#nt-xent+12
📚TheoryIntermediate

Mutual Information

Mutual Information (MI) measures how much knowing one random variable reduces uncertainty about another.

#mutual information#entropy#kl divergence+12
📚TheoryIntermediate

Shannon Entropy

Shannon entropy quantifies the average uncertainty or information content of a random variable in bits when using base-2 logarithms.

#shannon entropy#information gain#mutual information+12