Results 101 to 110 of about 113,519 (298)

Quantum circuit autoencoder

Physical Review A
13 pages, 7 ...
Jun Wu, Hao Fu, Mingzheng Zhu, Haiyue Zhang, Wei Xie, Xiang-Yang Li   +5 more
openaire   +2 more sources

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Review of: "A Novel Framework for Concept Drift Detection using Autoencoders for Classification Problems in Data Streams" [PDF]

, 2022
Sathish Kumar Nagarajan
openalex   +1 more source

Poisson Variational Autoencoder

Advances in Neural Information Processing Systems 37
Published as a NeurIPS 2024 Spotlight paper (https://openreview.net/forum?id=ektPEcqGLb)
Vafaii, Hadi, Galor, Dekel, Yates, Jacob L.   +2 more
openaire   +3 more sources

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Identification of Exhaled Volatile Organic Compounds Biomarkers for Lung Cancer Under Data‐Limited Conditions Using Data Augmentation and Multi‐View Feature Selection

Advanced Intelligent Discovery, EarlyView.
This work introduces a novel framework for identifying non‐small cell lung cancer biomarkers from hundreds of volatile organic compounds in breath, analyzed via gas chromatography‐mass spectrometry. This method integrates generative data augmentation and multi‐view feature selection, providing a stable and accurate solution for biomarker discovery in ...
Guancheng Ren, Bohao Liu, Zhiyuan Qu, Xue Jiang, Heng Zhao, Danyao Qu, Ruizhi Ning, Chen Su, Taoping Liu, Guangjian Zhang, Weiwei Wu   +10 more
wiley   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

Representation Learning of Multi-Spectral Earth Observation Time Series and Evaluation for Crop Type Classification

Remote Sensing
Remote sensing (RS) spectral time series provide a substantial source of information for the regular and cost-efficient monitoring of the Earth’s surface. Important monitoring tasks include land use and land cover classification, change detection, forest
Andrea González-Ramírez, Clement Atzberger, Deni Torres-Roman, Josué López   +3 more
doaj   +1 more source

Multimodal Sensor Fusion Using Symmetric Skip Autoencoder Via an Adversarial Regulariser [PDF]

, 2020
Snigdha Bhagat, Shiv Dutt Joshi, Brejesh Lall, Smriti Gupta   +3 more
openalex   +1 more source

Machine Learning Driven Inverse Design of Broadband Acoustic Superscattering

Advanced Intelligent Discovery, EarlyView.
Multilayer acoustic superscatterers are designed using machine learning to achieve broadband superscattering and strong sound insulation. By incorporating a weighted mean absolute error into the loss function, the forward and inverse neural networks accurately map structural parameters to spectral responses.
Lijuan Fan, Xiangliang Zhang, Ying Wu
wiley   +1 more source