In machine learning, a hyperparameter is a parameter whose value is used to control learning process, and HPO is the problem of choosing a set of optimal hyperparameters for a learning algorithm.
Source: Algorithms for Hyper-Parameter OptimizationPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Bayesian Optimization | 26 | 21.67% |
BIG-bench Machine Learning | 13 | 10.83% |
Benchmarking | 7 | 5.83% |
Reinforcement Learning (RL) | 5 | 4.17% |
Federated Learning | 4 | 3.33% |
Fairness | 4 | 3.33% |
Image Classification | 4 | 3.33% |
Interpretable Machine Learning | 3 | 2.50% |
Knowledge Graphs | 2 | 1.67% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |