|
gptkbp:instance_of
|
gptkb:machine_learning
|
|
gptkbp:automated
|
Neural Architecture Search Algorithms
|
|
gptkbp:can_be_combined_with
|
gptkb:stage_adaptation
|
|
gptkbp:can_be_used_for
|
gptkb:speeches
Image Classification
Time Series Forecasting
|
|
gptkbp:can_be_used_to
|
Bayesian Optimization
Gradient-Based Methods
Optimize Hyperparameters
|
|
gptkbp:can_lead_to
|
Better Generalization
|
|
gptkbp:challenges
|
Computational Cost
Search Space Size
Lack of Interpretability
Overfitting Risks
|
|
gptkbp:developed_by
|
Research Institutions
Tech Companies
|
|
gptkbp:enhances
|
Model Performance
|
|
gptkbp:has_applications_in
|
gptkb:robotics
Finance
Healthcare
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Neural Architecture Search
|
|
gptkbp:involves
|
gptkb:machine_learning
Evolutionary Algorithms
|
|
gptkbp:is_applied_in
|
gptkb:Computer_Vision
gptkb:Natural_Language_Processing
|
|
gptkbp:is_enhanced_by
|
Domain Knowledge
|
|
gptkbp:is_evaluated_by
|
Cross-Validation
Model Complexity
Training Time
Benchmark Datasets
Validation Accuracy
|
|
gptkbp:is_explored_in
|
gptkb:Workshops
Conferences
Academic Papers
|
|
gptkbp:is_implemented_in
|
gptkb:Tensor_Flow
gptkb:Py_Torch
|
|
gptkbp:is_influenced_by
|
Neural Network Theory
|
|
gptkbp:is_optimized_for
|
Neural Network Architectures
|
|
gptkbp:is_part_of
|
gptkb:Automated_Machine_Learning_(Auto_ML)
gptkb:Deep_Learning
|
|
gptkbp:is_related_to
|
gptkb:Auto_ML
|
|
gptkbp:is_supported_by
|
gptkb:cloud_computing
High-Performance Computing
Community Contributions
Open Source Tools
|
|
gptkbp:is_used_in
|
gptkb:Hyperparameter_Tuning
|
|
gptkbp:research_areas
|
gptkb:Artificial_Intelligence
|
|
gptkbp:utilizes
|
Search Algorithms
|
|
gptkbp:bfsParent
|
gptkb:Jürgen_Schmidhuber
gptkb:Geoffrey_R._Hinton
|
|
gptkbp:bfsLayer
|
3
|