Results 41 to 50 of about 131,851 (273)

Immunocomputing-Based Approach for Optimizing the Topologies of LSTM Networks

IEEE Access, 2021
This paper aims to automatically design optimal LSTM topologies using the clonal selection algorithm (CSA) to solve text classification tasks such as sentiment analysis and SMS spam classification.
Ali Al Bataineh, Devinder Kaur
doaj   +1 more source

Hyperparameter Importance Across Datasets

, 2018
With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond ...
Bergstra J., Bonilla E. V., Brazdil P., Demvsar J., Feurer M., Jamieson K., Joachims T., Klein A., Li L., Loshchilov I., Sobol I. M., van Rijn J. N., van Rijn J. N., Wistuba M.   +13 more
core   +1 more source

SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization

, 2018
Computer vision is experiencing an AI renaissance, in which machine learning models are expediting important breakthroughs in academic research and commercial applications.
Kinnison, Jeff, Kremer-Herman, Nathaniel, Scheirer, Walter, Thain, Douglas   +3 more
core   +1 more source

Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones

Advanced Engineering Materials, EarlyView.
A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell, Martha Leach, Hongtao Song, Carolyn Seepersad   +3 more
wiley   +1 more source

Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimizationb

Journal of Electronic Science and Technology, 2019
Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models.
Jia Wu, Xiu-Yun Chen, Hao Zhang, Li-Dong Xiong, Hang Lei, Si-Hao Deng   +5 more
doaj   +1 more source

Circular‐Polarization‐Sensitive Organic Photodetectors with a Chiral Nanopatterned Electrode Inverse‐Designed by Genetic Algorithm

Advanced Functional Materials, EarlyView.
A chiral photodetector capable of selectively distinguishing left‐ and right‐handed circularly polarized light is experimentally demonstrated. The device, which features a nanopatterned electrode inverse‐designed by a genetic algorithm within a metal–dielectric–metal nanocavity that incorporates a vacuum‐deposited small‐molecule multilayer, exhibits ...
Kyung Ryoul Park, Jewoong Lee, In‐Suk Choi, Changsoon Kim   +3 more
wiley   +1 more source

Optimizing Deep Learning Models with Improved BWO for TEC Prediction

Biomimetics
The prediction of total ionospheric electron content (TEC) is of great significance for space weather monitoring and wireless communication. Recently, deep learning models have become increasingly popular in TEC prediction.
Yi Chen, Haijun Liu, Weifeng Shan, Yuan Yao, Lili Xing, Haoran Wang, Kunpeng Zhang   +6 more
doaj   +1 more source

Predicting Atomic Charges in MOFs by Topological Charge Equilibration

Advanced Functional Materials, EarlyView.
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi, Sumukh Shankar Sharadaprasad, Rochus Schmid   +2 more
wiley   +1 more source

An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion

, 2013
© 2014 IEEE.The design of gaits and corresponding control policies for bipedal walkers is a key challenge in robot locomotion. Even when a viable controller parametrization already exists, finding near-optimal parameters can be daunting.
Calandra, R, Deisenroth, MP, Peters, J, Seyfarth, A   +3 more
core   +1 more source

Automatic Termination for Hyperparameter Optimization

, 2021
Bayesian optimization (BO) is a widely popular approach for the hyperparameter optimization (HPO) in machine learning. At its core, BO iteratively evaluates promising configurations until a user-defined budget, such as wall-clock time or number of iterations, is exhausted. While the final performance after tuning heavily depends on the provided budget,
Makarova, Anastasia, Shen, Huibin, Perrone, Valerio, Klein, Aaron, Faddoul, Jean Baptiste, Krause, Andreas, Seeger, Matthias, Archambeau, Cedric   +7 more
openaire   +3 more sources