Results 151 to 160 of about 2,584 (186)

Evolving spiking neural networks: the role of neuron models and encoding schemes in neuromorphic learning. [PDF]

Front Neurosci
Loyola-Jara B, Fernández-Rodríguez G, Baladron J.   +2 more
europepmc   +1 more source

Breast Cancer Prediction Using Rotation Forest Algorithm Along with Finding the Influential Causes. [PDF]

Bioengineering (Basel)
Das P, Sarker P, Tiang JJ, Nahid AA.
europepmc   +1 more source

Physics-informed TVAE workflow for data augmentation, mechanical validation, optimization of CFRP-strengthened CFST beams. [PDF]

MethodsX
Admasu MB, Admassu AM, Biresaw TH, Tarekegn AG.   +3 more
europepmc   +1 more source

Gene driven analytical learning model for accurate breast cancer diagnosis. [PDF]

Sci Rep
Hesham F, Abbassy MM, Abdalla M.
europepmc   +1 more source

Diabetic retinopathy as the primary predictor of mild cognitive impairment in type 2 diabetes: Insights from machine learning models. [PDF]

PLoS One
Rhmari Tlemçani FZ, Aitlahbib F, Laidi S, Alidrissi N, Aasfara J, Elamari S, Chadli A, Motaib I.   +7 more
europepmc   +1 more source

Optimizing electric vehicle energy consumption prediction through machine learning and ensemble approaches. [PDF]

Sci Rep
Hussain I, Ching KB, Uttraphan C, Tay KG, Noor A, Memon SA.   +5 more
europepmc   +1 more source

Deep learning-based gait phase detection using shank-mounted IMU data: Classification approach. [PDF]

PLoS One
Choi W, Choi MT.
europepmc   +1 more source

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Automating hyperparameter optimization in geophysics with Optuna: A comparative study

Geophysical Prospecting
AbstractDeep learning has gained attraction amongst geophysicists for solving complex longstanding problems. Nevertheless, proper hyperparameter optimization methodologies remain critically underexplored in geophysical deep learning research. This paper attempts to first highlight the importance of hyperparameter optimization and then showcase two ...
Hussain Almarzooq
exaly   +2 more sources

Scaling Up Optuna: P2P Distributed Hyperparameters Optimization

Concurrency Computation Practice and Experience
ABSTRACT In machine learning (ML), hyperparameter optimization (HPO) is the process of choosing a tuple of values that ensures an efficient deployment and training of an AI model. In practice, HPO not only applies to ML tuning but can also be used to tune complex numerical simulations.
exaly   +2 more sources