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Concepts532

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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
45678
#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
📚TheoryIntermediate

Embedding Spaces & Distributed Representations

Embedding spaces map discrete things like words or products to dense vectors so that similar items are close together.

#embeddings#dense vectors#cosine similarity+12
📚TheoryIntermediate

Classifier-Free Guidance

Classifier-Free Guidance (CFG) steers diffusion sampling toward a condition (like a text prompt) without needing a separate classifier.

#classifier-free guidance#diffusion models#epsilon prediction+11
📚TheoryAdvanced

Energy-Based Models (EBM)

Energy-Based Models (EBMs) define probabilities through an energy landscape: low energy means high probability, with p(x) = exp(-E(x)) / Z.

#energy-based models#partition function#langevin dynamics+12
📚TheoryIntermediate

Flow Matching

Flow matching learns a time-dependent vector field v_t(x, c) whose ODE transports simple noise to complex data, enabling fast, deterministic sampling.

#flow matching#conditional flow matching#rectified flow+11
📚TheoryIntermediate

Autoregressive Models

Autoregressive (AR) models represent a joint distribution by multiplying conditional probabilities in a fixed order, using the chain rule of probability.

#autoregressive#ar model#n-gram+11
∑MathAdvanced

Stochastic Differential Equations for Generation

A forward stochastic differential equation (SDE) models a state that drifts deterministically and is shaken by random Brownian noise over time.

#stochastic differential equation#diffusion model#euler maruyama+12
📚TheoryAdvanced

Diffusion Models (Score-Based)

Score-based diffusion models corrupt data by gradually adding Gaussian noise and then learn to reverse this process by estimating the score, the gradient of the log-density.

#diffusion models#score-based modeling#ddpm+7
📚TheoryAdvanced

Normalizing Flows

Normalizing flows transform a simple base distribution (like a standard Gaussian) into a complex target distribution using a chain of invertible functions.

#normalizing flows#change of variables#jacobian determinant+12
∑MathIntermediate

Wasserstein Distance & Optimal Transport

Wasserstein distance (Earth Mover’s Distance) measures how much “work” is needed to transform one probability distribution into another by moving mass with minimal total cost.

#wasserstein distance#earth mover's distance#optimal transport+12