Results 111 to 120 of about 1,858,266 (298)

Selected backbone-specific optimization and loss hyperparameters.

Selected backbone-specific optimization and loss hyperparameters.
Shafiq Ul Rehman (23115148), Bassam W. Aboshosha (24376183), Lamees N. Mahmoud (17901488), Ibrahim Sadek (12515794)   +3 more
core   +1 more source

3D Printing of Soft Robotic Systems: Advances in Fabrication Strategies and Future Trends

Advanced Robotics Research, EarlyView.
Collectively, this review systematically examines 3D‐printed soft robotics, encompassing material selections, function integration, and manufacturing methodologies. Meanwhile, fabrication strategies are analyzed in order of increasing complexity, highlighting persistent challenges with proposed solutions.
Changjiang Liu, Bin Cui, Xumin Huang, Xiaoyu (Rayne) Zheng, T. Thang Vo‐Doan, Ruirui Qiao   +5 more
wiley   +1 more source

Hyperparameters of machine learning model obtained by Bayesian optimization.

Hyperparameters of machine learning model obtained by Bayesian optimization.
Xue Liu (420033), Chen Zhu (445894), Dong Chen (448284)   +2 more
core   +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

A Statistical Approach to Provide Explainable Convolutional Neural Network Parameter Optimization

International Journal of Computational Intelligence Systems, 2019
Algorithms based on convolutional neural networks (CNNs) have been great attention in image processing due to their ability to find patterns and recognize objects in a wide range of scientific and industrial applications.
Saman Akbarzadeh, Selam Ahderom, Kamal Alameh   +2 more
doaj   +1 more source

Hyperparameter Optimization in Machine Learning

Foundations and Trends® in Machine Learning
Hyperparameters are configuration variables controlling the behavior of machine learning algorithms. They are ubiquitous in machine learning and artificial intelligence and the choice of their values determines the effectiveness of systems based on these technologies.
Franceschi, Luca, Donini, Michele, Perrone, Valerio, Klein, Aaron, Archambeau, Cédric, Seeger, Matthias, Pontil, Massimiliano, Frasconi, Paolo   +7 more
openaire   +4 more sources

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
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

Feature-Based Population Initialization for Evolutionary Optimization of Machine Learning Models in Short-Term Solar Power Forecasting

Computation
Nowadays, solar energy is becoming one of the most popular sources of renewable energy worldwide. Traditional fossil fuels cause pollution and climate change, while solar power offers a clean and sustainable alternative.
Aleksei Vakhnin, Harri Niska, Anders V. Lindfors, Mikko Kolehmainen   +3 more
doaj   +1 more source

Bayesian Optimization of Hyperparameters Using Gaussian Processes

, 2019
The goal of this thesis was to implement a practical tool for optimizing hy- perparameters of neural networks using Bayesian optimization. We show the theoretical foundations of Bayesian optimization, including the necessary math- ematical background for
Arnold, Jakub
core