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Jing Liu, Xuesong Hai, Keqin Li • Future Internet
(2023)
Massive amounts of data drive the performance of deep learning models, but in practice, data resources are often highly dispersed and bound by data privacy and security concerns, making it difficult for multiple data sources to share their ...
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Open Access (DOAJ)
DOI: 10.3390/fi16010006
Jan Kukačka, Vladimir Golkov, Daniel Cremers • arXiv (Cornell University)
(2017)
Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a systematic, unifying t...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1710.10686
Li Liu, Wanli Ouyang, Xiaogang Wang, Paul Fieguth et al. • International Journal of Computer Vision
(2019)
Abstract Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a ...
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DOI: https://doi.org/10.1007/s11263-019-01247-4
Francis Quintal Lauzon • International Conference on Information Science, Signal Processing and their Applications (ISSPA)
(2012)
Deep learning allows automatically learning multiple levels of representations of the underlying distribution of the data to be modeled. In this work, a specific implementation called stacked denoising autoencoders is explored. We contribut...
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DOI: https://doi.org/10.1109/isspa.2012.6310529
Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Sepp Hochreiter • Frontiers in Environmental Science
(2016)
The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 differen...
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DOI: https://doi.org/10.3389/fenvs.2015.00080
Longqian Huang, Ruichen Luo, Xü Liu, Xiang Hao • Light Science & Applications
(2022)
The goal of spectral imaging is to capture the spectral signature of a target. Traditional scanning method for spectral imaging suffers from large system volume and low image acquisition speed for large scenes. In contrast, computational sp...
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DOI: https://doi.org/10.1038/s41377-022-00743-6
PMC: 8927154
John Cristian Borges Gamboa • arXiv (Cornell University)
(2017)
In many real-world application, e.g., speech recognition or sleep stage classification, data are captured over the course of time, constituting a Time-Series. Time-Series often contain temporal dependencies that cause two otherwise identica...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1701.01887
Bryan Y. Lim, Stefan Zohren • Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
(2021)
Numerous deep learning architectures have been developed to accommodate the diversity of time-series datasets across different domains. In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-hori...
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Open Access
DOI: https://doi.org/10.1098/rsta.2020.0209
Sai Sri Hemantha Konala, Srinivas Koppu • Frontiers in Medicine
(2026)
IntroductionKidney-related disorders are one of the global health concerns that require timely detection to prevent severe health complications. The use of computed tomography (CT) images for accurate classification of kidney diseases is im...
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Open Access (DOAJ)
DOI: 10.3389/fmed.2026.1893419
Qiang Zhao, Rundong Guo, Xiaowei Feng, Weifeng Hu et al. • Mathematics
(2022)
Legal judgement prediction (LJP) is a crucial part of legal AI, and its goal is to predict the outcome of a case based on the information in the description of criminal facts. This paper proposes a decision prediction method based on causal...
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Open Access (DOAJ)
DOI: 10.3390/math10132281
Saleh Albahli • Future Internet
(2019)
Since the introduction of just-in-time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods, which can predict the defect inducing changes in a software product. In order to predict thes...
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Open Access (DOAJ)
DOI: 10.3390/fi11120246
Hao Shi, Guo Cao, Youqiang Zhang, Zixian Ge et al. • Remote Sensing
(2022)
Deep learning (DL) has recently been a core ingredient in modern computer vision tasks, triggering a wave of revolutions in various fields. The hyperspectral image (HSI) classification task is no exception. A wide range of DL-based methods ...
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Open Access (DOAJ)
DOI: 10.3390/rs14174235
Ruichen Rong, Yonglong Wei, Lin Li, Tao Wang et al. • EBioMedicine
(2023)
Summary: Background: Tissues such as the liver lobule, kidney nephron, and intestinal gland exhibit intricate patterns of zonated gene expression corresponding to distinct cell types and functions. To quantitatively understand zonation, it...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.ebiom.2023.104698
Zhi He, Chengle Zhou, Liwei Zou, Suhong Zhou et al. • International Journal of Digital Earth
(2025)
Accurately monitoring disaster effects is a crucial task in relief efforts (e.g. typhoon rescue). Social media data plays a vital role in disaster management, while deep learning-based methods gain more attention in typhoon disaster researc...
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Open Access (DOAJ)
DOI: 10.1080/17538947.2025.2517790
Yofri Furqani Hakim, Fuan Tsai • Remote Sensing
(2025)
The demand for large-scale topographic maps in Indonesia has significantly increased due to the implementation of several government initiatives that necessitate the utilization of spatial data in development planning. Currently, the nation...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/rs17030473
Sidharth Mudgal, Han Li, Theodoros Rekatsinas, AnHai Doan et al. • ACM SIGMOD International Conference on Management of Data (SIGMOD)
(2018)
Entity matching (EM) finds data instances that refer to the same real-world entity. In this paper we examine applying deep learning (DL) to EM, to understand DL's benefits and limitations. We review many DL solutions that have been develope...
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DOI: https://doi.org/10.1145/3183713.3196926
Anton S. Becker, Magda Marcon, Soleen Ghafoor, Moritz C. Wurnig et al. • Investigative Radiology
(2017)
OBJECTIVES: The aim of this study was to evaluate the diagnostic accuracy of a multipurpose image analysis software based on deep learning with artificial neural networks for the detection of breast cancer in an independent, dual-center mam...
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DOI: https://doi.org/10.1097/rli.0000000000000358
Rocio Vargas, Amir Hosein Mosavi, Ramon Ruiz • Preprints.org
(2018)
Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learn- ing methodology applies nonlinear transformations and model abstractions of high level i...
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DOI: https://doi.org/10.20944/preprints201810.0218.v1
Shutao Li A, Weiwei Song, Leyuan Fang, Yushi Chen et al. • IEEE Transactions on Geoscience and Remote Sensing
(2019)
Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine ...
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Open Access
DOI: https://doi.org/10.1109/tgrs.2019.2907932
Chaoyun Zhang, Paul Patras, Hamed Haddadi • IEEE Communications Surveys & Tutorials
(2019)
The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traff...
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Open Access
DOI: https://doi.org/10.1109/comst.2019.2904897