Statements (58)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Artificial_Intelligence
|
| gptkbp:can_be_combined_with |
momentum
|
| gptkbp:can_lead_to |
rapid decay of learning rate
|
| gptkbp:characteristic |
adaptive learning rate
|
| gptkbp:developed_by |
gptkb:D._P._Kingma
|
| gptkbp:has_applications_in |
reinforcement learning
|
| gptkbp:has_impact_on |
learning rate adjustment
|
| https://www.w3.org/2000/01/rdf-schema#label |
Adagrad
|
| gptkbp:improves |
convergence speed
|
| gptkbp:introduced_in |
gptkb:2011
|
| gptkbp:is_a |
first-order optimization algorithm
|
| gptkbp:is_adopted_by |
data scientists
industry applications |
| gptkbp:is_analyzed_in |
case studies
|
| gptkbp:is_based_on |
gradient descent
|
| gptkbp:is_challenged_by |
learning rate decay strategies
vanishing learning rates |
| gptkbp:is_compared_to |
gptkb:Ada_Delta
SGD Nesterov accelerated gradient |
| gptkbp:is_criticized_for |
poor performance on certain tasks
|
| gptkbp:is_described_as |
research papers
machine learning courses |
| gptkbp:is_documented_in |
software libraries
|
| gptkbp:is_evaluated_by |
other optimizers
empirical performance |
| gptkbp:is_explored_in |
tutorials
hyperparameter optimization |
| gptkbp:is_implemented_in |
gptkb:Tensor_Flow
gptkb:Py_Torch various platforms |
| gptkbp:is_influenced_by |
learning rate schedules
|
| gptkbp:is_integrated_with |
data processing pipelines
|
| gptkbp:is_notable_for |
its simplicity
|
| gptkbp:is_noted_for |
its effectiveness in early iterations
|
| gptkbp:is_part_of |
machine learning frameworks
gradient-based optimization methods stochastic optimization methods |
| gptkbp:is_recognized_by |
academic literature
|
| gptkbp:is_recommended_by |
large-scale machine learning
|
| gptkbp:is_related_to |
gptkb:Adam
RMSprop |
| gptkbp:is_supported_by |
theoretical analysis
|
| gptkbp:is_tested_for |
real-world problems
benchmark datasets |
| gptkbp:is_used_by |
gptkb:researchers
|
| gptkbp:is_used_in |
computer vision
natural language processing predictive modeling |
| gptkbp:is_utilized_for |
feature selection
|
| gptkbp:is_utilized_in |
online learning
|
| gptkbp:requires |
hyperparameter tuning
|
| gptkbp:suitable_for |
deep learning
sparse data non-convex problems |
| gptkbp:used_for |
training machine learning models
|
| gptkbp:bfsParent |
gptkb:Adam_optimizer
|
| gptkbp:bfsLayer |
5
|