A method of a hyperparameter server improves hyperparameter search efficiency for devices in a self-organizing network (SON) includes sending configuration for data feature collection to at least one edge device in the self-organizing network, receiving hyperparameter performance data from the at ...
We then design, implement, and evaluate a multi-stage hyperparameter search method we call Mithridates that strengthens robustness by 3-5x with only a slight impact on the model's accuracy. We show that the hyperparameters found by our method increase robustness against multiple types of back...
Weight penalty factors are one common method of providing this control. However, using weight penalties creates the additional search problem of finding the optimal penalty factors. MacKay [ 5 ] proposed an approximate Bayesian framework for training neural networks, in which penalty factors are ...
While this method is straightforward, it is time-consuming and largely relies on the programmer's intuition and expertise. 3. Grid search Grid search is a systematic approach to hyperparameter selection. It involves defining a grid of possible values for each hyperparameter and evaluating the ...
The class namePluginOptimizermust not be changed. All plugin classes must extendBasePluginOptimizer. It is recommended that you only make minimal changes to the search method and only override other functions if necessary. Table 1. BasePluginOptimizer functions.Detailed description of the BasePluginOpt...
Considering the conclusions above, to adapt to different search conditions and improve stability, we proposed a dynamic surrogate model evaluation (DSME) method. DSME consists of two parts: (1) the construction of the surrogate model library and (2) the cross-validation mechanism. By dynamically ...
They find that for this test case the TPE method outperforms GP and GP outperforms random search beyond the initial 30 models. However, they can’t explain whether TPE does better because it narrows in on good hyperparameters more quickly or conversely because it searches more randomly than GP...
While such parameter tuning is often presented as being incidental to the algorithm, correctly setting these parameter choices is frequently critical to realizing a method's full potential. Compounding matters, these parameters often must be re-tuned when the algorithm is applied to a new problem ...
The optimization process is executed by calling thesearch.searchmethod, which performs the evaluations of therunfunction with different configurations of the hyperparameters until a maximum number of evaluations (100 in this case) is reached.
An advantage of this method is that hyperparameters can be updated before model parameters have fully converged. We also give sufficient conditions for the global convergence of this method, based on regularity conditions of the involved functions and summability of errors. Finally, we validate the ...