How deeply does Inside Deep Learning: Math, Algorithms, Models cover ensemble?

Explains snapshot timing, the Tmax formula, and parameter averaging, but does not provide ensemble-prediction code.

Ensemble

As explained in Inside Deep Learning: Math, Algorithms, Models

A collection of multiple models whose predictions are combined, typically to improve accuracy or robustness.

An ensemble combines predictions from multiple models to improve accuracy. Instead of training many neural networks independently, cosine annealing can produce several model snapshots from one training run. section 5.2.3

Cosine-annealing snapshots provide a practical ensemble workflow:

Train one model with a learning rate that repeatedly decreases to a small value and rises again. section 5.2.3
Copy the weights whenever the learning rate reaches the bottom of a dip; each copy becomes one ensemble member. section 5.2.3
For 20 snapshots over T epochs, set Tmax = T/(20·2−1) to obtain 20 dips. section 5.2.3
Use stochastic weight averaging to average parameters into one model with ensemble-like accuracy and the storage cost of one model. section 5.2.3

Snapshot ensemble

A 20-network ensemble would normally require training and storing 20 networks. Cosine annealing instead stores weights at 20 learning-rate minima during one run, then averages their predictions. section 5.2.3

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Related concepts

random forestincludes · A random forest is an ensemble model that combines predictions from multiple decision trees built with randomized data samples, feature selections, or both.
gradient boostingincludes · Gradient boosting is an ensemble-learning technique that sequentially adds predictive models, typically weak learners, to reduce a loss by fitting its gradient.
baggingincludes · Bagging, or bootstrap aggregation, is an ensemble technique that trains multiple models on bootstrap samples of data and combines their predictions.
language modeluses · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
decision treeuses · A decision tree is a branching structure that represents sequential tests or choices, with paths leading to outcomes, predictions, or actions.
diversityuses · Diversity is the degree of variation among members of a set or population in relevant characteristics, perspectives, behaviors, or outcomes.

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