Results 121 to 130 of about 6,929,542 (320)

Hyperparameter optimization: Classics, acceleration, online, multi-objective, and tools.

Mathematical biosciences and engineering : MBE
Hyperparameter optimization (HPO) has been well-developed and evolved into a well-established research topic over the decades. With the success and wide application of deep learning, HPO has garnered increased attention, particularly within the realm of ...
Jiatong Tan, Haoran Liao, Wei Liu, Changjun Fan, Jincai Huang, Zhong Liu, Jun-Chi Yan   +6 more
semanticscholar   +1 more source

Data‐Driven Materials Science for Energy‐Sustainable Applications

Advanced Materials, EarlyView.
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley   +1 more source

Comparison of gridsearchcv and bayesian hyperparameter optimization in random forest algorithm for diabetes prediction

Journal of Soft Computing Exploration
Diabetes Mellitus (DM) is a chronic disease whose complications have a significant impact on patients and the wider community. In its early stages, diabetes mellitus usually does not cause significant symptoms, but if it is detected too late and not ...
Rini Muzayanah, D. Pertiwi, Muazam Ali, M. Muslim   +3 more
semanticscholar   +1 more source

Descriptors to Dynamics: A Materials and Device Perspective on in‐Materio Physical Reservoir Computing for Neuromorphic Edge Intelligence

Advanced Materials, EarlyView.
Intrinsic material dynamics are harnessed as computational resources for neuromorphic in‐materio physical reservoir computing. Defects, ionic motion, interfaces, percolation, geometry, and biasing shape transient states that provide fading memory, nonlinearity, and high‐dimensional projection for simple readout. A descriptor‐to‐dynamics framework links
Kshitij RB Singh, Alif Syafiq Kamarol Zaman, Muzhen Xu, Yuki Usami, Saptarshi Das, Hirofumi Tanaka   +5 more
wiley   +1 more source

Prediction of concrete compressive strength using deep neural networks based on hyperparameter optimization

Cogent Engineering
This paper describes deep neural network (DNN) models based on hyperparameter optimization for the prediction of the compressive strength of concrete. The novelty of this research lies in the implementation of optimized hyperparameters to train the DNN ...
Mohammed Naved, Mohammed Asim, Tanvir Ahmad   +2 more
semanticscholar   +1 more source

Automatic hyperparameter tuning of topology optimization algorithms using surrogate optimization

This paper presents a new approach that automates the tuning process in topology optimization of parameters that are traditionally defined by the user. The new method draws inspiration from hyperparameter optimization in machine learning.
Ha, Dat, Carstensen, Josephine
core   +1 more source

Wavelength‐Multiplexed 2D Beam Steering via a Passive Diffractive Network

Advanced Optical Materials, EarlyView.
Illustration of a wavelength‐multiplexed diffractive beam steering system, which is composed of K cascaded diffractive layers, each containing phase‐modulating elements that are jointly optimized using deep learning–based optimization. When illuminated with a set of wavelengths {λ1,λ2,…,λNw}$\{ {{{\lambda }_1},{{\lambda }_2},\ldots ,{{\lambda }_{{{N}_w}
Che‐Yung Shen, Yuhang Li, Çağatay Işıl, Tianyi Gan, Mona Jarrahi, Aydogan Ozcan   +5 more
wiley   +1 more source

Hyperparameter Optimization

The Journal of The Institute of Image Information and Television Engineers, 2023
Marc Becker, Lennart Schneider, Sebastian Fischer   +2 more
openaire   +2 more sources

A comparative analysis of hyperparameter optimization using LSTM-based deep learning models for urban air quality predictions

Ain Shams Engineering Journal
Air pollution poses significant threats to human health and the environment, necessitating accurate prediction models for effective management and mitigation strategies.
Beytullah Eren, Caner Erden, Ayşegül Atalı, Serkan Ozdemir   +3 more
doaj   +1 more source

Improving the Robustness of Visual Teach‐and‐Repeat Navigation Using Drift Error Correction and Event‐Based Vision for Low‐Light Environments

Advanced Robotics Research, EarlyView.
Visual teach‐and‐repeat (VTR) navigation allows robots to learn and follow routes without building a full metric map. We show that navigation accuracy for VTR can be improved by integrating a topological map with error‐drift correction based on stereo vision.
Fuhai Ling, Ze Huang, Tony J. Prescott
wiley   +1 more source