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⚙️AlgorithmIntermediate

Sharpness-Aware Minimization (SAM)

Sharpness-Aware Minimization (SAM) trains models to perform well even when their weights are slightly perturbed, seeking flatter minima that generalize better.

#sharpness-aware minimization#sam optimizer#robust optimization+11
📚TheoryIntermediate

Loss Landscape Analysis

A loss landscape is the “terrain” of a model’s loss as you move through parameter space; valleys are good solutions and peaks are bad ones.

#loss landscape
Intermediate
Advanced
Filtering by:
#logistic regression
Group:
Optimization
#sharpness
#hessian eigenvalues
+12
⚙️AlgorithmIntermediate

Momentum Methods

Momentum methods add an exponentially weighted memory of past gradients to make descent steps smoother and faster, especially in ravines and ill-conditioned problems.

#momentum#heavy-ball#polyak momentum+12
⚙️AlgorithmIntermediate

Stochastic Gradient Descent (SGD)

Stochastic Gradient Descent (SGD) updates model parameters using small random subsets (mini-batches) of data, making learning faster and more memory-efficient.

#stochastic gradient descent#mini-batch#random shuffling+12
⚙️AlgorithmIntermediate

Gradient Descent

Gradient descent is a simple, repeatable way to move downhill on a loss surface by stepping in the opposite direction of the gradient.

#gradient descent#batch gradient descent#learning rate+12