Results 221 to 230 of about 96,348 (272)

Automated Detection of Quality Deviations in Poultry Processing Using Step-Specific YOLOv12 Models. [PDF]

Foods
Einsiedel D, Vita M, Kaltenecker F, Dunnewind B, Meulendijks J, Krupitzer C.   +5 more
europepmc   +1 more source

Transfer learning with Bayesian optimization for colorectal cancer histopathology classification. [PDF]

BMC Med Imaging
ALGhafri HS, Lim CS.
europepmc   +1 more source

Adaptive deep clustering integrating DINOv2 embeddings, graph attention, and bio-inspired optimization. [PDF]

Sci Rep
Abdrabo M, Refaat H, Abdallah M, Farouk O.   +3 more
europepmc   +1 more source

Optimized CNN framework with VGG19, EfficientNet, and Bayesian optimization for early colon cancer detection. [PDF]

Sci Rep
Rahman T, Deb N, Larguech S, Moniruzzaman M, Kumer Ghosh A, Ahmed Sami A, Abd Aziz AB, Salem Al-Bawri S.   +7 more
europepmc   +1 more source

Improving acute lymphoblastic leukemia diagnosis through CBAM-enhanced VGG19 deep learning. [PDF]

Sci Rep
Rahman SIU, Abbas N, Ali S, Jan S, Lee IE, Salman M, Salhi A, Alkhyyat A.   +7 more
europepmc   +1 more source

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Population-Based Hyperparameter Tuning With Multitask Collaboration

IEEE Transactions on Neural Networks and Learning Systems, 2023
Population-based optimization methods are widely used for hyperparameter (HP) tuning for a given specific task. In this work, we propose the population-based hyperparameter tuning with multitask collaboration (PHTMC), which is a general multitask collaborative framework with parallel and sequential phases for population-based HP tuning methods.
Wendi Li, Ting Wang, Wing W. Y. Ng
openaire   +2 more sources

Kriging Hyperparameter Tuning Strategies

AIAA Journal, 2008
Response surfaces have been extensively used as a method of building effective surrogate models of high-fidelity computational simulations. Of the numerous types of response surface models, kriging is perhaps one of the most effective, due to its ability to model complicated responses through interpolation or regression of known data while providing an
Toal, David J.J., Bressloff, Neil W., Keane, Andy J.   +2 more
openaire   +2 more sources

Convolutional Neural Networks Hyperparameters Tuning

2021
Digital images have revolutionized work in numerous scientific fields such as healthcare, astronomy, biology, agriculture as well as in every day life. One of the frequent tasks in applications with digital images is image classification which is a very challenging task. Major progress was made when convolution neural networks were introduced.
Eva Tuba, Nebojša Bačanin, Ivana Strumberger, Milan Tuba   +3 more
openaire   +1 more source

Beyond Manual Tuning of Hyperparameters

KI - Künstliche Intelligenz, 2015
The success of hand-crafted machine learning systems in many applications raises the question of making machine learning algorithms more autonomous, i.e., to reduce the requirement of expert input to a minimum. We discuss two strategies towards this goal: (1) automated optimization of hyperparameters (including mechanisms for feature selection ...
Hutter, Frank, Lücke, Jörg, Schmidt-Thieme, Lars   +2 more
openaire   +2 more sources

Sequential Model-Free Hyperparameter Tuning

2015 IEEE International Conference on Data Mining, 2015
Hyperparameter tuning is often done manually but current research has proven that automatic tuning yields effective hyperparameter configurations even faster and does not require any expertise. To further improve the search, recent publications propose transferring knowledge from previous experiments to new experiments.
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme   +2 more
openaire   +1 more source