Results 71 to 80 of about 452,378 (262)

Robust and Adaptive Incremental Learning for Varying Feature Space

IEEE Access
Real-world multiple or streaming tabular datasets, such as electronic health records from various sources and internet-of-things data generated from different devices, typically exhibit varied feature spaces depending on the datasets. Batch-mode learning
Cheol Ho Kim, Jung-Hoon Lee, Hawon Shin, Ock Kee Baek   +3 more
doaj   +1 more source

Alkali Ion‐Incorporated HfO2 Dielectrics for Reconfigurable Neuromorphic Computing

Advanced Functional Materials, EarlyView.
This work presents an indium gallium zinc oxide (IGZO) transistor with an alkali cation‐integrated hafnium dioxide (HfO2) dielectric exhibiting synaptic behavior via ion retention. The solution‐based film fabrication strategy overcomes the limitations of atomic layer deposition (ALD) and precursor coating, enabling the control of synaptic retention ...
Seung Yeon Ki, In Cheol Kwak, Seonkwon Kim, Jihyun Kim, Dong Gue Roe, Se‐Jin Kim, Joohoon Kang, Jeong Ho Cho   +7 more
wiley   +1 more source

Recomposable Layered Metasurfaces for Wavelength‐Multiplexed Optical Encryption via Modular Diffractive Deep Neural Networks

Advanced Functional Materials, EarlyView.
Modular diffractive deep neural network metasurfaces encode and reconstruct holograms across layer combinations and wavelengths, enabling secure, multifunctional operation. Each layer acts independently yet composes jointly, yielding up to m(2N −1) channels for m wavelengths and N layers.
Cherry Park, Youngsun Jeon, Seokho Lee, Yeseul Kim, Junsuk Rho   +4 more
wiley   +1 more source

Efficient Incremental Learning Using Dynamic Correction Vector

IEEE Access, 2020
One major challenge for modern artificial neural networks (ANNs) is that they typically does not handle incremental learning well. In other words, while learning the new features, the performances of existing features usually deteriorate. This phenomenon
Yun Xiang, Yongbiao Miao, Jingyin Chen, Qi Xuan   +3 more
doaj   +1 more source

A Practical Incremental Learning Framework For Sparse Entity Extraction [PDF]

, 2018
This work addresses challenges arising from extracting entities from textual data, including the high cost of data annotation, model accuracy, selecting appropriate evaluation criteria, and the overall quality of annotation.
Al-Olimat, Hussein S., Gustafson, Steven, Mackay, Jason, Sheth, Amit, Thirunarayan, Krishnaprasad   +4 more
core   +2 more sources

Spectrally Tuned Floating‐Gate Synapse Based on Blue‐ and Red‐Absorbing Organic Molecules for Wavelength‐Selective Neural Networks and Fashion Image Classifications

Advanced Functional Materials, EarlyView.
The characteristics of a vertical floating gate heterostructure transistor device that exhibits neuromorphic potentiation under visible light illumination are investigated. Due to spectrally‐tuned absorbance properties of each thin film layer and introduction of tunneling dielectric, the device enables wavelength‐selective tuning of synaptic plasticity
Seungme Kang, Jisoo Park, Jinwoong Hong, Jinmin Park, Jeongbo Lee, Hyeonjung Kim, Wonjun Shin, Eva Bestelink, Radu A. Sporea, Seyong Oh, Chung Whan Lee, Yun‐Hi Kim, Hocheon Yoo   +12 more
wiley   +1 more source

S²IL: Structurally Stable Incremental Learning

IEEE Access
Feature Distillation (FD) strategies are proven to be effective in mitigating Catastrophic Forgetting (CF) seen in Class Incremental Learning (CIL).
S. Balasubramanian, P. Yedu Krishna, Talasu Sai Sriram, M. Sai Subramaniam, Manepalli Pranav Phanindra Sai, Ravi Mukkamala   +5 more
doaj   +1 more source

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets

Advanced Functional Materials, EarlyView.
Permanent magnets derive their extraordinary strength from deep, universal electronic‐structure principles that control magnetization, anisotropy, and intrinsic performance. This work uncovers those governing rules, examines modern modeling and AI‐driven discovery methods, identifies critical bottlenecks, and reveals electronic fingerprints shared ...
Prashant Singh
wiley   +1 more source

Learning Automata Based Incremental Learning Method for Deep Neural Networks

IEEE Access, 2019
Deep learning methods have got fantastic performance on lots of large-scale datasets for machine learning tasks, such as visual recognition and neural language processing. Most of the progress on deep learning in recent years lied on supervised learning,
Haonan Guo, Shilin Wang, Jianxun Fan, Shenghong Li   +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