1 Gradient Descent

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Model-Agnostic Meta-Learning for Fast Adaptation of Deep ...

arxiv.org

using one or more gradient descent updates on task T i. For example, when using one gradient update, 0 i = r L T i (f): The step size may be ﬁxed as a hyperparameter or meta-learned. For simplicity of notation, we will consider one gradient update for the rest of this section, but using multi-ple gradient updates is a straightforward extension.

Descent, Derating, Gradient descent

SOLUTIONS - University of California, San Diego

math.ucsd.edu

(i) Carefully draw the level curve passing through (1; 1). On this graph, draw the gradient of the function at (1; 1). (ii) Compute the directional derivative of fat (1; 1) in the direction u = 4 5; 3 5:Use this calculation to estimate f((1; 1)+:01u): (iii) Find the unit direction v of steepest descent for the function fat (1; 1).

Solutions, Descent, Derating

Translating Embeddings for Modeling Multi-relational Data

proceedings.neurips.cc

The optimization is carried out by stochastic gradient descent (in minibatch mode), over the possible h;‘ and t, with the additional constraints that the L 2-norm of the embeddings of the entities is 1 (no regularization or norm constraints are given to the label embeddings ‘).This constraint is important

Descent, Embedding, Derating, Gradient descent

Lecture 2: The SVM classifier - University of Oxford

www.robots.ox.ac.uk

optimization algorithm (such as gradient descent)? local minimum global minimum If the cost function is convex, then a locally optimal point is globally optimal (provided the optimization is over a convex set, which it is in our case) Optimization continued. Convex functions.