Filters








6,169 Hits in 4.2 sec

Author Index

2010 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition  
: Sparse Semi-Supervised Learning for Perceptual Grouping Hontani, Hidekata Point-Based Non-Rigid Surface Registration with Accuracy Estimation Hoque, Mohammed E.  ...  Learning to Recognize Shadows in Monochromatic Natural Images Context-Constrained Hallucination for Image Super-Resolution Optimizing One-Shot Recognition with Micro-Set Learning Tardos, Éva Globally  ... 
doi:10.1109/cvpr.2010.5539913 fatcat:y6m5knstrzfyfin6jzusc42p54

Progress in the Application of Machine Learning in Combustion Studies

Zhi-Hao Zheng, Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China, Xiao-Dong Lin, Ming Yang, Ze-Ming He, Ergude Bao, Hang Zhang, Zhen-Yu Tian, University of Chinese Academy of Sciences, Beijing 100049, China, Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China, University of Chinese Academy of Sciences, Beijing 100049, China, Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China (+8 others)
2020 ES Energy & Environment  
Studies of combustion theory include computational fluid dynamics (CFD) simulation, combustion phenomenon and fuel.  ...  Combustion studies combined with ML can be divided into theoretical and industrial aspects.  ...  Acknowledgements The authors are thankful for the financial support from the NSFC (No. 51888103/51976216/51606192), MOST (2017YFA0402800), Recruitment Program of Global Youth Experts and the CAS Pioneer  ... 
doi:10.30919/esee8c795 fatcat:ezb4yc7iwvdjfmpsjy3p6djvma

Machine Learning for Fluid Mechanics

Steven L. Brunton, Bernd R. Noack, Petros Koumoutsakos
2019 Annual Review of Fluid Mechanics  
Machine learning (ML) offers a wealth of techniques to extract information from data that can be translated into knowledge about the underlying fluid mechanics.  ...  Expected final online publication date for the Annual Review of Fluid Mechanics, Volume 52 is January 5, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.  ...  Unsupervised Learning This learning task implies the extraction of features from the data by specifying certain global criteria, without the need for supervision or a ground-truth label for the results  ... 
doi:10.1146/annurev-fluid-010719-060214 fatcat:j6ghhpilorayfceysakiwxqgri

Machine Learning for Fluid Mechanics [article]

Steven Brunton and Bernd Noack and Petros Koumoutsakos
2019 arXiv   pre-print
This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics.  ...  We outline fundamental machine learning methodologies and discuss their uses for understanding, modeling, optimizing, and controlling fluid flows.  ...  We are grateful for discussions with Nathan Kutz (  ... 
arXiv:1905.11075v2 fatcat:brszpilzezc3xmbttdcla7zome

DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data

Jiaxing Chen, ChinWang Cheong, Liang Lan, Xin Zhou, Jiming Liu, Aiping Lyu, William K Cheung, Lu Zhang
2021 Briefings in Bioinformatics  
We compared DeepDRIM with nine GRN reconstruction algorithms designed for either bulk or single-cell RNA-seq data.  ...  The available algorithms for reconstructing GRNs are commonly designed for bulk RNA-seq data, and few of them are applicable to analyze scRNA-seq data by dealing with the dropout events and cellular heterogeneity  ...  Author contributions statement Acknowledgments We also thank Research Grants Council of Hong Kong, Hong Kong Baptist University and HKBU Research Committee for their kind support of this project.  ... 
doi:10.1093/bib/bbab325 pmid:34424948 pmcid:PMC8499812 fatcat:nvu5io2xzje6tpxl53tbwyup5u

Physics-based Deep Learning [article]

Nils Thuerey and Philipp Holl and Maximilian Mueller and Patrick Schnell and Felix Trost and Kiwon Um
2021 arXiv   pre-print
Beyond standard supervised learning from data, we'll look at physical loss constraints, more tightly coupled learning algorithms with differentiable simulations, as well as reinforcement learning and uncertainty  ...  As much as possible, all topics come with hands-on code examples in the form of Jupyter notebooks to quickly get started.  ...  Previous work has demonstrated that the concept of learned self-supervision extends to space-time solutions, e.g., in the context of super-resolution for fluid simulations [XFCT18].  ... 
arXiv:2109.05237v2 fatcat:dm2wyckg6fcxzhsxi4hmo76sny

Generative Adversarial Networks for Spatio-temporal Data: A Survey [article]

Nan Gao, Hao Xue, Wei Shao, Sichen Zhao, Kyle Kai Qin, Arian Prabowo, Mohammad Saiedur Rahaman, Flora D. Salim
2021 arXiv   pre-print
We summarise the application of popular GAN architectures for spatio-temporal data and the common practices for evaluating the performance of spatio-temporal applications with GANs.  ...  In this paper, we have conducted a comprehensive review of the recent developments of GANs for spatio-temporal data.  ...  A new pooling mechanism was proposed to learn a 'global' pooling vector that encodes the subtle cues for all people involved in a scene.  ... 
arXiv:2008.08903v3 fatcat:pbhxbfgw65bodksjdmwazwo4dq

The Sensitivity of Estimates of Multiphase Fluid and Solid Properties of Porous Rocks to Image Processing

Gaetano Garfi, Cédric M. John, Steffen Berg, Samuel Krevor
2019 Transport in Porous Media  
The sensitivity of the property estimates increases with the complexity of its definition and its relationship to boundary shape.  ...  In this work, we assess the sensitivity of porosity, permeability, specific surface area, in situ contact angle measurements, fluid-fluid interfacial curvature measurements and mineral composition to processing  ...  Moreover, they compared the performances of seven machine learning algorithms (either unsupervised and supervised) to segment four μ-CT imaged samples, concluding that the use of K-means to guide the construction  ... 
doi:10.1007/s11242-019-01374-z fatcat:so2a2zfgufbtdif3htb7gaj2zq

A Review of Complex Systems Approaches to Cancer Networks [article]

Abicumaran Uthamacumaran
2020 arXiv   pre-print
Machine learning, network science and algorithmic information dynamics are discussed as current tools for cancer network reconstruction.  ...  Deep Learning architectures and computational fluid models are proposed for better forecasting gene expression patterns in cancer ecosystems.  ...  Therefore, Deep Learning architectures trained for mapping complex fluid flows can be optimized for detecting strange attractors within cancer networks.  ... 
arXiv:2009.12693v2 fatcat:kt3e4bqaufgwlbhx2wbgzftnpe

Machine Learning for Naval Architecture, Ocean and Marine Engineering [article]

J P Panda
2021 arXiv   pre-print
We discuss the applications of machine learning algorithms for different problems such as wave height prediction, calculation of wind loads on ships, damage detection of offshore platforms, calculation  ...  Commonplace machine learning algorithms utilized in Scientific Machine Learning (SciML) include neural networks, regression trees, random forests, support vector machines, etc.  ...  Supervised Learning In supervised learning, correct information is available to the ML algorithm.  ... 
arXiv:2109.05574v1 fatcat:lb3ghjbme5glxlxv5tf2pvz454

Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

Heung-Il Suk, Seong-Whan Lee, Dinggang Shen
2013 Brain Structure and Function  
Combining the latent information with the original features helps build a robust model in AD/MCI classification, with high diagnostic accuracy.  ...  latent feature representation with a stacked auto-encoder (SAE).  ...  From a learning perspective, we aim to minimize the reconstruction error between the input x i and the output z i with respect to the parameters. Let and denote a reconstruction error.  ... 
doi:10.1007/s00429-013-0687-3 pmid:24363140 pmcid:PMC4065852 fatcat:ynfewlq3grh5fdhwdjcqbmvg4q

Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear [chapter]

Jianing Wang, Yiyuan Zhao, Jack H. Noble, Benoit M. Dawant
2018 Lecture Notes in Computer Science  
Global Distributions and Optimal Transport Arnold Gomez*; Maureen Stone; Philip Bayly; Jerry Prince T-5 Cardiac Motion Scoring with Segment-and Subject-level Non-Local Modeling Wufeng Xue; Gary Brahm  ...  Huo; JinHyeong Park; Bennett A Landman; Andy Milkowski; Sasa Grbic; Shaohua Zhou T-46 Deep Active Self-paced Learning for Accurate Pulmonary Nodule Segmentation Wenzhe Wang; Yifei Lu; Bian Wu; Tingting  ...  T-129 Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training Wen  ... 
doi:10.1007/978-3-030-00928-1_1 fatcat:ypoj3zplm5awljf6u5c2spgiea

Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning [article]

Steven L. Brunton, J. Nathan Kutz, Krithika Manohar, Aleksandr Y. Aravkin, Kristi Morgansen, Jennifer Klemisch, Nicholas Goebel, James Buttrick, Jeffrey Poskin, Agnes Blom-Schieber, Thomas Hogan, Darren McDonald
2020 arXiv   pre-print
Importantly, we will focus on the critical need for interpretable, generalizeable, explainable, and certifiable machine learning techniques for safety-critical applications.  ...  , and that improve with increasing volumes of data.  ...  We are also grateful for advice and interviews with Ryan Smith, Laura Garnett, Phil Crothers, Nia Jetter, Howard McKenzie, and Darren Macer.  ... 
arXiv:2008.10740v1 fatcat:wjbnr2wqibhebe64qnlyhvykdm

MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework [article]

Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar
2020 arXiv   pre-print
We propose MeshfreeFlowNet, a novel deep learning-based super-resolution framework to generate continuous (grid-free) spatio-temporal solutions from the low-resolution inputs.  ...  MeshfreeFlowNet allows for: (i) the output to be sampled at all spatio-temporal resolutions, (ii) a set of Partial Differential Equation (PDE) constraints to be imposed, and (iii) training on fixed-size  ...  Furthermore, this super-resolution process can be effectively modeled using deep learning models that learn such statistical correlations in a self-supervised manner from low-resolution and high-resolution  ... 
arXiv:2005.01463v2 fatcat:uraeupygnbct7bhzryyyzjri2e

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
Semi-supervised Deep Learning Techniques for Spectrum Reconstruction Hong, Hanbin; Bao, Wentao; Hong, Yuan; Kong, Yu 1187 Privacy Attributes-Aware Message Passing Neural Network for Visual Privacy Attributes  ...  to Rank for Active Learning: A Listwise Approach DAY 3 -Jan 14, 2021 -DAY 3 -Jan 14, 2021 Live Wang, Chen; Deng, Chengyuan 2336 PS T1.12 On the Global Self-Attention Mechanism for Graph Convolutional  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm
« Previous Showing results 1 — 15 out of 6,169 results