Title Machine learning and knowledge discovery in databases : Applied data science track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings. Part V / Yuxiao Dong, Nicolas Kourtellis, Barbara Hammer, Jose A. Lozano (eds.)

Published Cham, Switzerland : Springer, 2021

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Description 1 online resource (xxxiv, 516 pages) : illustrations (some color) Series Lecture notes in artificial intelligence Lecture notes in computer science ; 12979 LNCS sublibrary, SL 7, Artificial intelligence Lecture notes in computer science. Lecture notes in artificial intelligence. Lecture notes in computer science ; 12979. LNCS sublibrary. SL 7, Artificial intelligence. Contents Intro -- 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 5.2 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 4.2 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 Methods 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 3.2 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 Summary The 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 Notes "Unfortunately it had to be held online and we could only meet each other virtually."--Preface Includes author index Online resource; title from PDF title page (SpringerLink, viewed September 16, 2021) Subject Machine learning -- Congresses Data mining -- Congresses Data mining Machine learning Genre/Form proceedings (reports) Conference papers and proceedings Conference papers and proceedings. Actes de congrès. Form Electronic book Author Dong, Yuxiao, editor. Kourtellis, Nicolas, editor Hammer, Barbara, 1970- editor. Lozano, José A., 1968- editor. ISBN 9783030865177 3030865177

Other Titles ECML PKDD 2021 Applied data science track

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