Statements (58)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Artificial_Intelligence
|
| gptkbp:adapted_into |
learning rates based on past gradients
|
| gptkbp:can_be_combined_with |
other optimization techniques
|
| gptkbp:can_be_sensitive_to |
hyperparameter tuning
|
| gptkbp:can_be_used_for |
convex optimization problems
|
| gptkbp:can_lead_to |
rapid convergence
overfitting in some cases |
| gptkbp:designed_for |
stochastic gradient descent
|
| gptkbp:has_variants |
gptkb:Ada_Delta
|
| https://www.w3.org/2000/01/rdf-schema#label |
Ada Grad
|
| gptkbp:improves |
learning rate adaptation
|
| gptkbp:introduced_in |
gptkb:2011
|
| gptkbp:is_a_first-order_optimization_algorithm |
nan
|
| gptkbp:is_a_foundational_algorithm_for |
many other optimizers
|
| gptkbp:is_a_key_component_of |
many machine learning frameworks
|
| gptkbp:is_applied_in |
gptkb:neural_networks
support vector machines |
| gptkbp:is_beneficial_for |
large datasets
sparse data |
| gptkbp:is_characterized_by |
accumulating past gradients
|
| gptkbp:is_effective_against |
feature selection
high-dimensional data large-scale machine learning multi-class classification problems |
| gptkbp:is_implemented_in |
gptkb:Tensor_Flow
gptkb:Keras gptkb:Py_Torch |
| gptkbp:is_known_for |
its simplicity
its robustness |
| gptkbp:is_less_effective_for |
non-sparse data
|
| gptkbp:is_often_compared_to |
SGD
|
| gptkbp:is_often_used_in |
image processing
financial modeling data mining |
| gptkbp:is_popular_for |
training deep learning models
|
| gptkbp:is_popular_in |
natural language processing
|
| gptkbp:is_related_to |
RMSProp
|
| gptkbp:is_used_in |
gptkb:machine_learning
computer vision deep learning time series analysis reinforcement learning recommendation systems |
| gptkbp:proposed_by |
Duchi et al.
|
| gptkbp:requires |
more memory than standard SGD
|
| gptkbp:sensitivity |
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 |
6
|