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AI & MLSpecializedAIML-INT-006

Superposition and Sparse Dictionary Features

Networks pack more features than dimensions; sparse autoencoders try to recover interpretable features.

Difficulty
5 / 5
Importance
3 / 5
Study time
1–2 h
Kind of work
Mixed

Why it matters

The learner can explain superposition with a toy model and describe how a sparse autoencoder's sparsity penalty trades reconstruction for interpretability.

The route to this topic

Route AIML-INT-006
Natural Numbers What Is a Model? Variables & Constants Learning from Data Tasks: Regression, Classification, and Beyond Features and Targets Solving Linear Equations Function Notation & Composition Datasets and Splits Classification Framing Numeric and Categorical Data Linear Regression I: Prediction and Residuals Embeddings Complex Numbers Scores and Logits Encoding Categories Loss and Fitting: Squared Error Similarity in Vector Space Basic Ratios Sigmoid and Probabilities Multiclass Scores and Softmax The Artificial Neuron Linear Regression II: Least Squares Fitting Distributed Representations Query, Key, Value Polynomial Functions Logistic Regression Overfitting and Underfitting Activation Functions Principal Component Analysis Word Embeddings Similarity Scores Definition of the Derivative Differentiation Rules Regularization: Ridge The Multilayer Perceptron Latent Representations Weighted Aggregation Contextual Representations Chain Rule Regularization: Lasso and Sparsity Forward Propagation Probing Representations Self-Attention Computational Graphs The Attention Block The Chain Rule in Neural Nets The Causal Transformer Backpropagation Reading a Transformer Autoencoders Mechanistic Interpretability: Circuits Superposition and Sparse Dictionary Features