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001439653 019__ $$a1268573941
001439653 020__ $$a9783030865177$$q(electronic bk.)
001439653 020__ $$a3030865177$$q(electronic bk.)
001439653 020__ $$z9783030865160$$q(print)
001439653 0247_ $$a10.1007/978-3-030-86517-7$$2doi
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001439653 1112_ $$aECML PKDD (Conference)$$d(2021 :$$cOnline)
001439653 24510 $$aMachine learning and knowledge discovery in databases :$$bApplied data science track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings.$$nPart V /$$cYuxiao Dong, Nicolas Kourtellis, Barbara Hammer, Jose A. Lozano (eds.).
001439653 2463_ $$aECML PKDD 2021
001439653 2463_ $$aApplied data science track
001439653 264_1 $$aCham, Switzerland :$$bSpringer,$$c2021.
001439653 300__ $$a1 online resource (xxxiv, 516 pages) :$$billustrations (some color)
001439653 336__ $$atext$$btxt$$2rdacontent
001439653 337__ $$acomputer$$bc$$2rdamedia
001439653 338__ $$aonline resource$$bcr$$2rdacarrier
001439653 347__ $$atext file
001439653 347__ $$bPDF
001439653 4901_ $$aLecture notes in artificial intelligence
001439653 4901_ $$aLecture notes in computer science ;$$v12979
001439653 4901_ $$aLNCS sublibrary, SL 7, Artificial intelligence
001439653 500__ $$a"Unfortunately it had to be held online and we could only meet each other virtually."--Preface
001439653 500__ $$aIncludes author index.
001439653 5050_ $$aIntro -- Preface -- Organization -- Contents -- Part V -- Automating Machine Learning, Optimization, and Feature Engineering -- PuzzleShuffle: Undesirable Feature Learning for Semantic Shift Detection -- 1 Introduction -- 2 Related Work -- 2.1 Out-of-Distribution Detection -- 2.2 Data Augmentation -- 2.3 Uncertainty Calibration -- 3 Preliminaries -- 3.1 The Effects by Perturbation -- 3.2 Adversarial Undesirable Feature Learning -- 4 Proposed Method -- 4.1 PuzzleShuffle Augmentation -- 4.2 Adaptive Label Smoothing -- 4.3 Motivation -- 5 Experiments -- 5.1 Experimental Settings
001439653 5058_ $$a5.2 Compared Methods -- 5.3 Results -- 5.4 Analysis -- 6 Conclusion -- References -- Enabling Machine Learning on the Edge Using SRAM Conserving Efficient Neural Networks Execution Approach -- 1 Introduction -- 2 Background and Related Work -- 2.1 Deep Model Compression -- 2.2 Executing Neural Networks on Microcontrollers -- 3 Efficient Neural Network Execution Approach Design -- 3.1 Tensor Memory Mapping (TMM) Method Design -- 3.2 Loading Fewer Tensors and Tensors Re-usage -- 3.3 Finding the Cheapest NN Graph Execution Sequence -- 3.4 Core Algorithm -- 4 Experimental Evaluation -- 4.1 SRAM Usage
001439653 5058_ $$a4.2 Model Performance -- 4.3 Inference Time and Energy Consumption -- 5 Conclusion -- References -- AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge -- 1 Introduction -- 2 Challenge Setting -- 2.1 Phases -- 2.2 Protocol -- 2.3 Datasets -- 2.4 Metrics -- 2.5 Platform, Hardware and Limitations -- 2.6 Baseline -- 2.7 Results -- 3 Post Challenge Experiments -- 3.1 Reproducibility -- 3.2 Overfitting and Generalisation -- 3.3 Comparison to Open Source AutoML Solutions -- 3.4 Impact of Time Budget -- 3.5 Dataset Difficulty -- 4 Conclusion and Future Work -- References
001439653 5058_ $$aMethods for Automatic Machine-Learning Workflow Analysis -- 1 Introduction -- 2 Problem Definition -- 3 Related Work -- 4 Residual Graph-Level Graph Convolutional Networks -- 5 Datasets -- 6 Workflow Similarity -- 7 Structural Performance Prediction -- 8 Component Refinement and Suggestion -- 9 Conclusion -- References -- ConCAD: Contrastive Learning-Based Cross Attention for Sleep Apnea Detection -- 1 Introduction -- 2 Related Work -- 2.1 Sleep Apnea Detection -- 2.2 Attention-Based Feature Fusion -- 2.3 Contrastive Learning -- 3 Methodology -- 3.1 Expert Feature Extraction and Data Augmentation
001439653 5058_ $$a3.2 Feature Extractor -- 3.3 Cross Attention -- 3.4 Contrastive Learning. -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Compared Methods -- 4.3 Experiment Setup -- 4.4 Results and Discussions -- 5 Conclusions and Future Work -- References -- Machine Learning Based Simulations and Knowledge Discovery -- DeepPE: Emulating Parameterization in Numerical Weather Forecast Model Through Bidirectional Network -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Problem Definition -- 3.2 Deep Parameterization Emulator -- 3.3 Transfer Scheme -- 3.4 Training -- 4 Experiments -- 4.1 Datasets
001439653 506__ $$aAccess limited to authorized users.
001439653 520__ $$aThe multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media.
001439653 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 16, 2021).
001439653 650_0 $$aMachine learning$$vCongresses.
001439653 650_0 $$aData mining$$vCongresses.
001439653 650_6 $$aApprentissage automatique$$vCongrès.
001439653 650_6 $$aExploration de données (Informatique)$$vCongrès.
001439653 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001439653 655_7 $$aConference papers and proceedings.$$2lcgft
001439653 655_7 $$aActes de congrès.$$2rvmgf
001439653 655_0 $$aElectronic books.
001439653 7001_ $$aDong, Yuxiao,$$eeditor.
001439653 7001_ $$aKourtellis, Nicolas,$$eeditor.
001439653 7001_ $$aHammer, Barbara,$$d1970-$$eeditor.
001439653 7001_ $$aLozano, José A.,$$d1968-$$eeditor.$$1https://orcid.org/0000-0002-4683-8111
001439653 77608 $$iPrint version: $$z9783030865160
001439653 77608 $$iPrint version: $$z9783030865184
001439653 830_0 $$aLecture notes in computer science.$$pLecture notes in artificial intelligence.
001439653 830_0 $$aLecture notes in computer science ;$$v12979.
001439653 830_0 $$aLNCS sublibrary.$$nSL 7,$$pArtificial intelligence.
001439653 852__ $$bebk
001439653 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-86517-7$$zOnline Access$$91397441.1
001439653 909CO $$ooai:library.usi.edu:1439653$$pGLOBAL_SET
001439653 980__ $$aBIB
001439653 980__ $$aEBOOK
001439653 982__ $$aEbook
001439653 983__ $$aOnline
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