Results 121 to 130 of about 119,888 (330)

Cross‐Modal Denoising and Integration of Spatial Multi‐Omics Data with CANDIES

Advanced Science, EarlyView.
In this paper, we introduce CANDIES, which leverages a conditional diffusion model and contrastive learning to effectively denoise and integrate spatial multi‐omics data. We conduct extensive evaluations on diverse synthetic and real datasets, CANDIES shows superior performance on various downstream tasks, including denoising, spatial domain ...
Ye Liu, Wanpeng Zou, Yuekai Li, Jiayi Wang, Mingxuan Cai, Hongmin Cai   +5 more
wiley   +1 more source

Autoencoder-Based Neural Network Model for Anomaly Detection in Wireless Body Area Networks

IoT
In medical healthcare services, Wireless Body Area Networks (WBANs) are enabler tools for tracking healthcare conditions by monitoring some critical vital signs of the human body. Healthcare providers and consultants use such collected data to assess the
Murad A. Rassam
doaj   +1 more source

Feature Learning for Multispectral Satellite Imagery Classification Using Neural Architecture Search [PDF]

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems.
Coltin, Brian J., Furlong, Padraig M., Robert Campbell   +2 more
core   +1 more source

INB3P: A Multi‐Modal and Interpretable Co‐Attention Framework Integrating Property‐Aware Explanations and Memory‐Bank Contrastive Fusion for Blood–Brain Barrier Penetrating Peptide Discovery

Advanced Science, EarlyView.
INB3P is a multimodal framework for blood–brain barrier‐penetrating peptide prediction under extreme data scarcity and class imbalance. By combining physicochemical‐guided augmentation, sequence–structure co‐attention, and imbalance‐aware optimization, it improves predictive performance and interpretability.
Jingwei Lv, Qianyang Wu, Jian Liu, Binlu Yang, Yuanhao Li, Junlin Xu, Yajie Meng, Leyi Wei, Zilong Zhang, Quan Zou, Xiulai Li, Feifei Cui   +11 more
wiley   +1 more source

A Survey on Variational Autoencoders in Recommender Systems

ACM Computing Surveys
Recommender systems have become an important instrument to connect people to information. Sparse, complex, and rapidly growing data presents new challenges to traditional recommendation algorithms.
Shangsong Liang, Zhou Pan, Wei Liu, Jian Yin, Maarten de Rijke   +4 more
semanticscholar   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Application of autoencoders artificial neural network and principal component analysis for pattern extraction and spatial regionalization of global temperature data

Machine Learning: Science and Technology
Spatial regionalization is instrumental in simplifying the spatial complexity of the climate system. To identify regions of significant climate variability, pattern extraction is often required prior to spatial regionalization with a clustering algorithm.
Chibuike Chiedozie Ibebuchi, Omon A Obarein, Itohan-Osa Abu   +2 more
doaj   +1 more source

Implicit Autoencoders

CoRR, 2018
In this paper, we describe the "implicit autoencoder" (IAE), a generative autoencoder in which both the generative path and the recognition path are parametrized by implicit distributions. We use two generative adversarial networks to define the reconstruction and the regularization cost functions of the implicit autoencoder, and derive the learning ...
openaire   +2 more sources

Discovering Interpretable Semantics from Radio Signals for Contactless Cardiac Monitoring

Advanced Science, EarlyView.
This study presents a semantic representation framework for clinically interpretable cardiac monitoring from contactless radio signals. It formulates radio semantic learning as an information‐bottleneck problem and approximates the objective via intra‐modal compression and cross‐modal alignment, structuring radio measurements into meaningful semantic ...
Jinbo Chen, Haoyu Wang, Guixin Xu, Yuqin Yuan, Xinmeng Cai, Zhengwu Wei, Dongheng Zhang, Qibin Sun, Tianwei Zhang, Cuntai Guan, Yan Chen   +10 more
wiley   +1 more source

Single-Sensor Acoustic Emission Source Localization in Plate-Like Structures Using Deep Learning

Aerospace, 2018
This paper introduces two deep learning approaches to localize acoustic emissions (AE) sources within metallic plates with geometric features, such as rivet-connected stiffeners.
Arvin Ebrahimkhanlou, Salvatore Salamone
doaj   +1 more source