[Lisa_seminaires] Talk: David Duvenaud on Gradient-based hyperparameter optimization through reversible learning
Jörg Bornschein
bornj at iro.umontreal.ca
Jeu 4 Fév 12:57:47 EST 2016
Hi,
next *Thursday*, Feb. 11th at 3:30 pm we will have David Duvenaud give a
talk at the department colloquium.
Title: Gradient-based hyperparameter optimization through reversible
learning
Who: David Duvenaud
When: Thursday, Feb. 11th; 3:30 pm
Where: AA 3195
Tuning hyperparameters of learning algorithms is hard because gradients are
usually unavailable. We compute exact gradients of cross-validation
performance with respect to all hyperparameters by chaining derivatives
backwards through the entire training procedure. This lets us optimize
thousands of hyperparameters, including step-size and momentum schedules,
weight initialization distributions, richly parameterized regularization
schemes, and neural net architectures. We compute hyperparameter gradients
by exactly reversing the dynamics of stochastic gradient descent with
momentum. We'll also discuss related applications to nonlinear filtering
in model-based reinforcement learning.
Looking forward to see you there,
j
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