Results 111 to 120 of about 2,019,981 (302)

Bayesian optimization of hyperparameters from noisy marginal likelihood estimates

, 2023
Bayesian models often involve a small set of hyperparameters determined by maximizing the marginal likelihood. Bayesian optimization is an iterative method where a Gaussian process posterior of the underlying function is sequentially updated by new ...
Mattias Villani, Stockhammar, Pär,, Gustafsson, Oskar,, Villani, Mattias,, Oskar Gustafsson, Stockhammar, Paer,, Pär Stockhammar   +6 more
core   +1 more source

DRIVE‐SAFE: Data‐Driven Robustness and Informed Validation for Evolving Specifications via Formal Evaluation

Advanced Robotics Research, EarlyView.
DRIVE‐SAFE evaluates learning‐based, black‐box autonomous driving policies against evolving temporal safety requirements using Signal Temporal Logic robustness metrics. It aggregates distributional robustness measures with domain‐informed weights to guide iterative retraining.
Kristy Sakano, Jianyu An, Dinesh Manocha, Huan Xu   +3 more
wiley   +1 more source

Intelligent Sky Guardians (InSkyGuard): An Aerial Robotic Swarm for Autonomous Detection and Entrapment of Rogue Multirotors

Advanced Robotics Research, EarlyView.
Intelligent Sky Guardians (InSkyGuard) is introduced as a four‐drone swarm that autonomously detects, tracks, and safely captures rogue drones using a coordinated net system. Computer vision and leader–follower control architecture enable synchronized enclosure, while integrated failsafes enhance system reliability. Validated through closed‐environment
Joshua Hastings, Levanya Dewanarayana, Iline Shaju, Haider Al‐Wizni, Sampson Marquis, Weicheng Huang, Pooya Sareh   +6 more
wiley   +1 more source

Accelerating hyperparameter optimization with a secretary

Neurocomputing
Hyperparameter optimization (HPO) is a vital step in machine learning (ML) for enhancing model performance. However, the vast and complex nature of the search space makes HPO both challenging and resource-intensive. Automatic HPO methods have demonstrated their ability to efficiently explore high-dimensional hyperparameter spaces and identify optimal ...
Víctor Muñoz, Carmen Ballester, Dorin Sabin Copaci, Luis Moreno 0001, Dolores Blanco   +4 more
openaire   +3 more sources

Bilevel hyperparameter optimization for nonlinear support vector machines

, 2023
While the problem of tuning the hyperparameters of a support vector machine (SVM) via cross-validation is easily understood as a bilevel optimization problem, so far, the corresponding literature has mainly focused on the linear-kernel case.
Zemkoho, Alain
core   +1 more source

Learning‐Based Soft Robotic Grasping: Recent Progress and Remaining Challenges

Advanced Robotics Research, EarlyView.
This review analyzes learning‐based soft robotic grasping from a pipeline‐oriented perspective, encompassing soft gripper design, multimodal sensing, and learning‐based planning and control. It surveys key neural network architectures and benchmark datasets and identifies critical challenges such as sim‐to‐real transfer, generalization, and continual ...
Arnab Majumder, Jelizaveta Konstantinova, Graham Deacon, Kaspar Althoefer   +3 more
wiley   +1 more source

Machine Learning Hyperparameters Optimization for Accurate Arabic Sentiment Classification

Proceedings of the International Conference on Applied Innovations in IT
An improved model performance is achieved by optimizing hyperparameters for Arabic sentiment classification based on machine learning. The use of RNNs, LSTMs, and GRUs, as well as Logistic Regression, Random Forests, and Support Vector Machines as meta ...
Irwan Lakawa, Hujiyanto Hujiyanto, Abbas Oudah Waheed Alomairi   +2 more
doaj   +1 more source

A two-stage renal disease classification based on transfer learning with hyperparameters optimization. [PDF]

Front Med (Lausanne), 2023
Badawy M, Almars AM, Balaha HM, Shehata M, Qaraad M, Elhosseini M.   +5 more
europepmc   +1 more source

Enhancing Amphibious Flipper‐Based Locomotion Through Terrain‐Dependent Bayesian Optimization of Central Pattern Generator Control

Advanced Robotics Research, EarlyView.
A sea turtle‐inspired amphibious robot uses simulation‐guided optimization to adapt its flipper coordination to different environments. Sensitivity analysis and multiobjective Bayesian optimization identify terrain‐dependent controllers that are validated on hardware.
Nnamdi C. Chikere, Yasemin Ozkan‐Aydin
wiley   +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