Hyperparameter optimization
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How to choose hyper-parameters for optimisation methods in practice? I know that there are hyper-parameter optimisation techniques such as gradient-based or bayesian methods, but for instance it is not practical for deep learning.
How do authors choose hyperparameters for optimizer in deep learning articles? Do they only use grid search on max 5 "reasonable" values.
Also there is a question how to compare two different optimisation methods? How parameters of different methods are selected(for example, nestetov accelerated gradient and Adam)?
optimization numerical-optimization
asked Nov 15 at 20:30
Albert Nagapetyan
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up vote
0
down vote
favorite
How to choose hyper-parameters for optimisation methods in practice? I know that there are hyper-parameter optimisation techniques such as gradient-based or bayesian methods, but for instance it is not practical for deep learning.
How do authors choose hyperparameters for optimizer in deep learning articles? Do they only use grid search on max 5 "reasonable" values.
Also there is a question how to compare two different optimisation methods? How parameters of different methods are selected(for example, nestetov accelerated gradient and Adam)?
optimization numerical-optimization
asked Nov 15 at 20:30
Albert Nagapetyan
84
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
How to choose hyper-parameters for optimisation methods in practice? I know that there are hyper-parameter optimisation techniques such as gradient-based or bayesian methods, but for instance it is not practical for deep learning.
How do authors choose hyperparameters for optimizer in deep learning articles? Do they only use grid search on max 5 "reasonable" values.
Also there is a question how to compare two different optimisation methods? How parameters of different methods are selected(for example, nestetov accelerated gradient and Adam)?
optimization numerical-optimization
asked Nov 15 at 20:30
Albert Nagapetyan
84
How to choose hyper-parameters for optimisation methods in practice? I know that there are hyper-parameter optimisation techniques such as gradient-based or bayesian methods, but for instance it is not practical for deep learning.
How do authors choose hyperparameters for optimizer in deep learning articles? Do they only use grid search on max 5 "reasonable" values.
Also there is a question how to compare two different optimisation methods? How parameters of different methods are selected(for example, nestetov accelerated gradient and Adam)?
optimization numerical-optimization
optimization numerical-optimization
asked Nov 15 at 20:30
Albert Nagapetyan
84
asked Nov 15 at 20:30
Albert Nagapetyan
84
edited Nov 16 at 12:39
asked Nov 15 at 20:30
Albert Nagapetyan
84
asked Nov 15 at 20:30
Albert Nagapetyan
84
asked Nov 15 at 20:30
Albert Nagapetyan
84
84
add a comment |
add a comment |
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