Results 261 to 270 of about 2,307,608 (312)

Accelerated discovery of CO2 electrocatalysts using active machine learning

Nature, 2020
M. Zhong, Kevin Tran, Yimeng Min, Chuanhao Wang, Ziyun Wang, C. Dinh, Phil de Luna, Zongqian Yu, A. Rasouli, Peter Brodersen, Song Sun, O. Voznyy, Chih-Shan Tan, M. Askerka, Fanglin Che, Min Liu, A. Seifitokaldani, Y. Pang, S. Lo, Alexander H. Ip, Zachary W. Ulissi, E. Sargent   +21 more
semanticscholar   +3 more sources

Sequential active learning using meta-cognitive extreme learning machine

Neurocomputing, 2016
This paper proposes a fast and effective sequential active learning method using meta-cognitive extreme learning machine (SEAL-ELM). The proposed algorithm consists of two components, namely the cognitive component and the meta-cognitive component.
Yong Zhang, M. Er
semanticscholar   +2 more sources

Spatial-prior generalized fuzziness extreme learning machine autoencoder-based active learning for hyperspectral image classification

, 2020
Hyperspectral imaging has been extensively utilized in several fields, and it benefits from detailed spectral information contained in each pixel, generating a thematic map for classification to assign a unique label to each sample.
Muhammad Ahmad, Sidrah Shabbir, D. Oliva, M. Mazzara, Salvatore Distefano   +4 more
semanticscholar   +1 more source

An Automated Machine Learning-Genetic Algorithm Framework With Active Learning for Design Optimization

, 2021
The use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid ...
Opeoluwa Owoyele, P. Pal, A. V. Torreira
semanticscholar   +1 more source

Machine learning for active matter

Nature Machine Intelligence, 2020
The availability of large datasets has boosted the application of machine learning in many fields and is now starting to shape active-matter research as well. Machine learning techniques have already been successfully applied to active-matter data—for example, deep neural networks to analyse images and track objects, and recurrent nets and random ...
F. Cichos, K. Gustavsson, B. Mehlig, G. Volpe   +3 more
semanticscholar   +2 more sources

Active Learning of Introductory Machine Learning

Proceedings. Frontiers in Education. 36th Annual Conference, 2006
This paper describes a computer-based training program for active learning of Agent Technology, Expert Systems, Neural Networks and Case-Based Reasoning by undergraduate students using a simple agent framework. While many Machine Learning (ML) and Artificial Intelligence (AI) courses teach ML and AI concepts by means of programming assignments, these ...
Maja Pantic, Reinier Zwitserloot
openaire   +1 more source

Active learning relevant vector machine for reliability analysis

, 2021
In this study, an adaptive relevant vector machine, which is developed within a probabilistic Bayesian learning framework, is combined with Monte Carlo simulation (MCS) to perform reliability analysis with high efficiency and accuracy.
T. Z. Li, Q. Pan, Daniel Dias
semanticscholar   +1 more source

Activation-Kernel Extraction through Machine Learning

2017 New Generation of CAS (NGCAS), 2017
Machine Learning flooded many research fields, including Electronic Design Automation (EDA). The availability of algorithms that can solve complex problems through generic rule formulations represent a fresh opportunity to improve existing design paradigms. In this work we investigate the use of machine learning to manipulate logic circuits.
Tenace, Valerio, Calimera, Andrea
openaire   +1 more source

Autonomous Construction of Phase Diagrams of Block Copolymers by Theory-Assisted Active Machine Learning.

ACS Macro Letters, 2021
Equilibrium phase diagrams serve as blueprints for rational design of nanostructured materials of block copolymers, but their construction is time-consuming and requires profound expertise.
Shuochen Zhao, Tianyu Cai, Liangshun Zhang, Weihua Li, Jiaping Lin   +4 more
semanticscholar   +1 more source

Interactive Machine Learning for Data Exfiltration Detection: Active Learning with Human Expertise

IEEE International Conference on Systems, Man and Cybernetics, 2020
Data exfiltration is a serious threat to organizations. Such exfiltrations cause breach events that can lead to millions of dollars of loss. Perimeter defense is not enough by itself since successful exploits from insiders can also be very damaging ...
Mu-Huan Chung, M. Chignell, Lu Wang, Alexandra Jovicic, Abhay Raman   +4 more
semanticscholar   +1 more source