Results 41 to 50 of about 42,332 (292)
On the Performance of Differential Evolution for Hyperparameter Tuning [PDF]
2019 International Joint Conference on Neural Networks (IJCNN), 2019 2019 International Joint Conference on Neural Networks (IJCNN)
Mischa Schmidt +5 more
openaire +2 more sources
Hyperparameter tuning result by fold: CNN.
, 2021 Hyperparameter tuning result by fold: CNN.
Sang Ok Choi (11426728) +2 more
core +1 more source
BiLTCN model results with hyperparameter tuning.
, 2022 BiLTCN model results with hyperparameter tuning.
Furqan Rustam (10196722) +5 more
core +1 more source
EMPLOYING GENETIC ALGORITHM INSPIRED HYPERPARAMETER OPTIMIZATION IN MOBILE NET V2 ARCHITECTURE [PDF]
Proceedings on Engineering SciencesThis paper presents a novel approach for hyperparameter optimization for the MobileNetV2 architecture using a genetic algorithm. The proposed approach aims to automate the hyperparameter tuning leading to performance enhancement.
Baljinder Kaur +3 more
doaj +1 more source
A hyperparameter‐tuning approach to automated inverse planning [PDF]
Medical Physics, 2022 AbstractBackgroundIn current practice, radiotherapy inverse planning often requires treatment planners to modify multiple parameters in the treatment planning system's objective function to produce clinically acceptable plans. Due to the manual steps in this process, plan quality can vary depending on the planning time available and the planner's ...
Maass, Kelsey +2 more
openaire +3 more sources
Hyperparameter tuning result of control group.
, 2021 Hyperparameter tuning result of control group.
Sang Ok Choi (11426728) +2 more
core +1 more source
Hyperparameter tuning result by fold: Random forest.
, 2021 Hyperparameter tuning result by fold: Random forest.
Sang Ok Choi (11426728) +2 more
core +1 more source
Hyperparameter tuning in echo state networks
Proceedings of the Genetic and Evolutionary Computation Conference, 2022 Echo State Networks represent a type of recurrent neural network with a large randomly generated reservoir and a small number of readout connections trained via linear regression. The most common topology of the reservoir is a fully connected network of up to thousands of neurons.
openaire +2 more sources
Hyperparameter Tuning with Renyi Differential Privacy
CoRR, 2021 For many differentially private algorithms, such as the prominent noisy stochastic gradient descent (DP-SGD), the analysis needed to bound the privacy leakage of a single training run is well understood. However, few studies have reasoned about the privacy leakage resulting from the multiple training runs needed to fine tune the value of the training ...
Nicolas Papernot, Thomas Steinke 0002
openaire +3 more sources
Mango: A Python Library for Parallel Hyperparameter Tuning [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 5 pages, 3 figures, ICASSP ...
Sandeep Singh Sandha +3 more
openaire +2 more sources