AutoCL comprises methods for automatic feature selection and hyperparameter optimization for concept learners. We demonstrate its effectiveness with SML-Bench, a benchmarking framework for structured machine learning. Our approach leads to better predictive performance across concept learners, in terms of ...
iotmachine-learningrandom-forestsvmdata-streamxgboosthyperparameter-optimizationlightgbmdriftbayesian-optimizationparticle-swarm-optimizationonline-learningreal-time-analyticsconcept-driftchange-detectorintrusion-detection-systemanomaly-detectionnsl-kddiot-data-analyticsdrift-detection ...
Note on hyper-parameter tuning.To minimize performance differences due to sub-optimal hyper-parameters, we use theOptunahyperparameter optimization framework to tune the learning rate and weight decay hyper-parameters when training a classifier. We sample 30 learning rate and weight decay pairs and pe...
In all cases, we train using the Adam optimizer with hyperparameter (0.9, 0.999) and initial learning rate \(l=2\times 10^{-4}\). 1.4 Appendix D.4: Distributions of inherent concepts on the MNIST dataset See Fig. 11 Fig. 11 Encoded distributions on the MNIST dataset Full size image ...
This is because metric optimization often leads to manipulation, gaming, and a focus on short-term quantities at the expense of longer-term concerns. When developing a post-production strategy, it’s important to use a slate of metrics to gain a fuller picture of a model’s true impact, ...
The network growth as a parameter of the network monitoring challenge is studied in (Oleksii and Volodymir, 2017). Larger networks require higher investment. The study shows that the higher the traffic, the bigger the required resources to monitor the network. With 5G user plane traffic ...
The range of hyperparameters and full specification of final hyperparameters are available in Supplementary Table 1. We predicted the probability of a parameter belonging to each of the concepts and collected the top 10 most probable labels with a probability greater than zero. We calculated ...
After the hyperparameter search, 12 attention heads showed little performance improvement compared to 8 attention heads but much higher training and inference time; therefore, only 8 attention heads were used in TransformerCPI2.0. Atom embedding calculation Each of the atom features was initially ...
Common hyperparameters in our experiments are list as follows:HyperparameterValuesDescription client_num 20 or 100 20 with full participation or 100 with 20% participation sample_ratio 1 or 0.2 full participation or 20% participation dataset Fashion-MNIST or CIFAR-10 or CINIC-10 Three datasets in...
A graph learning method, called Constrained Laplacian Rank (CLR), was proposed to explore the intrinsic geometric structure of data, whose goal is to learn an optimal graph model [14]. Therefore, the CLR method is formulated by the following optimization problem: ...