Ada Grad

GPTKB entity

Statements (58)

Predicate	Object
gptkbp:instance_of	gptkb:Artificial_Intelligence
gptkbp:adapted_into	learning rates based on past gradients
gptkbp:applies_to	gptkb:microprocessor support vector machines
gptkbp:benefits	large datasets sparse data
gptkbp:can_be_used_with	other optimization techniques
gptkbp:can_lead_to	rapid convergence overfitting in some cases
gptkbp:has_variants	gptkb:Ada_Delta
https://www.w3.org/2000/01/rdf-schema#label	Ada Grad
gptkbp:improves	learning rate adaptation
gptkbp:introduced	gptkb:2011
gptkbp:is_a_framework_for	many other optimizers
gptkbp:is_characterized_by	accumulating past gradients
gptkbp:is_designed_for	stochastic gradient descent
gptkbp:is_effective_against	feature selection high-dimensional data large-scale machine learning multi-class classification problems non-sparse data
gptkbp:is_implemented_in	gptkb:Graphics_Processing_Unit gptkb:Keras gptkb:Py_Torch
gptkbp:is_known_for	its simplicity its robustness
gptkbp:is_often_compared_to	SGD
gptkbp:is_often_used_in	image processing financial modeling data mining
gptkbp:is_optimized_for	nan
gptkbp:is_popular_in	natural language processing training deep learning models
gptkbp:is_related_to	RMS Prop
gptkbp:is_used_for	convex optimization problems
gptkbp:is_used_in	gptkb:software_framework computer vision deep learning time series analysis reinforcement learning recommendation systems
gptkbp:is_vulnerable_to	hyperparameter tuning
gptkbp:key	many machine learning frameworks
gptkbp:proposed_by	Duchi et al.
gptkbp:requires	more memory than standard SGD
gptkbp:sensor	initial learning rate
gptkbp:suitable_for	online learning real-time applications
gptkbp:technique	can be applied to various domains. can handle noisy data can improve model accuracy minimizing loss functions reduces the learning rate over time
gptkbp:type_of	adaptive learning rate method
gptkbp:uses	per-parameter learning rates
gptkbp:variant	gradient descent
gptkbp:bfsParent	gptkb:Adadelta
gptkbp:bfsLayer	5