Results 61 to 70 of about 440,724 (260)

Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods

Automatika, 2019
Focussing on the problem that redundant nodes in the kernel incremental extreme learning machine (KI-ELM) which leads to ineffective iteration increase and reduce the learning efficiency, a novel improved hybrid intelligent deep kernel incremental ...
Di Wu, Zong Shun Qu, Feng Jiao Guo, Xiao Lin Zhu, Qin Wan   +4 more
doaj   +1 more source

Incremental cluster validity index-guided online learning for performance and robustness to presentation order [PDF]

, 2021
Leonardo Enzo Brito da Silva, Nagasharath Rayapati, Donald C. Wunsch   +2 more
openalex   +1 more source

DeeSIL: Deep-Shallow Incremental Learning

, 2018
Incremental Learning (IL) is an interesting AI problem when the algorithm is assumed to work on a budget. This is especially true when IL is modeled using a deep learning approach, where two com- plex challenges arise due to limited memory, which induces
AL Ginsca, B Thomee, M Mccloskey, O Russakovsky, Z Li   +4 more
core   +2 more sources

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Class-Incremental Learning Based on Feature Extraction of CNN With Optimized Softmax and One-Class Classifiers

IEEE Access, 2019
With the development of deep convolutional neural networks in recent years, the network structure has become more and more complicated and varied, and there are very good results in pattern recognition, image classification, scene classification, and ...
Xin Ye, Qiuyu Zhu
doaj   +1 more source

Automatic event detection in microblogs using incremental machine learning

, 2017
The global popularity of microblogs has led to an increasing accumulation of large volumes of text data on microblogging platforms such as Twitter. These corpora are untapped resources to understand social expressions on diverse subjects.
Alahakoon, Damminda, Bandaragoda, Tharindu Rukshan, De Silva, Daswin   +2 more
core   +1 more source

Artificial Intelligence‐Driven Development in Rechargeable Battery Materials: Progress, Challenges, and Future Perspectives

Advanced Functional Materials, EarlyView.
AI is transforming the research paradigm of battery materials and reshaping the entire landscape of battery technology. This comprehensive review summarizes the cutting‐edge applications of AI in the advancement of battery materials, underscores the critical challenges faced in harnessing the full potential of AI, and proposes strategic guidance for ...
Qingyun Hu, Junyuan Lu, Jian Hui, Ziyuan Rao, Yang Ren, Hong Wang   +5 more
wiley   +1 more source

Hierarchical Frequency-Guided Knowledge Reconstruction for SAR Incremental Target Detection

Remote Sensing
Synthetic Aperture Radar (SAR) incremental target detection faces challenges from the limits of incremental learning frameworks and distinctive properties of SAR imagery.
Yu Tian, Zongyong Cui, Zheng Zhou, Zongjie Cao   +3 more
doaj   +1 more source

Validity Criteria of a Standardized Test as an Opportunity for Efficient Assessment Created from the Teleological Perspective of Incremental Learning [PDF]

, 2023
Geanina Havârneanu
openalex   +1 more source

Polymer‐Incorporated Mechanically Compliant Carbon Nanotube Microelectrode Arrays for Multichannel Neural Signal Recording

Advanced Functional Materials, EarlyView.
This work presents a soft microelectrode array based on vertically aligned carbon nanotube (CNT) forests, combining high conductivity with mechanical softness. A densification process and air‐pressure‐assisted flexibilization improve structural integrity, ensuring stable insertion and reduced inflammation.
Hyeonhee Roh, Kwangjun Kim, Chaesung Kim, Minju Kim, Minwook Kim, Sangjun Sim, Jongbaeg Kim, Jong G. Ok, Maesoon Im   +8 more
wiley   +1 more source