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001439657 019__ $$a1268574439
001439657 020__ $$a9783030865207$$q(electronic bk.)
001439657 020__ $$a3030865207$$q(electronic bk.)
001439657 020__ $$z9783030865191$$q(print)
001439657 0247_ $$a10.1007/978-3-030-86520-7$$2doi
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001439657 1112_ $$aECML PKDD (Conference)$$d(2021 :$$cOnline)
001439657 24510 $$aMachine learning and knowledge discovery in databases :$$bResearch track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings.$$nPart II /$$cNuria Oliver, Fernando Pérez-Cruz, Stefan Kramer, Jesse Read, Jose A. Lozano (eds.).
001439657 2463_ $$aECML PKDD 2021
001439657 2463_ $$aResearch track
001439657 264_1 $$aCham, Switzerland :$$bSpringer,$$c2021.
001439657 300__ $$a1 online resource (xxxv, 817 pages) :$$billustrations (some color)
001439657 336__ $$atext$$btxt$$2rdacontent
001439657 337__ $$acomputer$$bc$$2rdamedia
001439657 338__ $$aonline resource$$bcr$$2rdacarrier
001439657 347__ $$atext file
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001439657 4901_ $$aLecture notes in artificial intelligence
001439657 4901_ $$aLecture notes in computer science ;$$v12976
001439657 4901_ $$aLNCS sublibrary, SL 7, Artificial intelligence
001439657 500__ $$a"Unfortunately it had to be held online and we could only meet each other virtually."--Preface
001439657 500__ $$aIncludes author index.
001439657 5050_ $$aIntro -- Preface -- Organization -- Contents -- Part II -- Generative Models -- Non-exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Methodology -- 5 Experiments -- 6 Conclusion -- References -- Unsupervised Learning of Joint Embeddings for Node Representation and Community Detection -- 1 Introduction -- 2 Related Work -- 2.1 Community Detection -- 2.2 Node Representation Learning -- 2.3 Joint Community Detection and Node Representation Learning -- 3 Methodology -- 3.1 Problem Formulation
001439657 5058_ $$a3.2 Variational Model -- 3.3 Design Choices -- 3.4 Practical Aspects -- 3.5 Complexity -- 4 Experiments -- 4.1 Synthetic Example -- 4.2 Datasets -- 4.3 Baselines -- 4.4 Settings -- 4.5 Discussion of Results -- 4.6 Hyperparameter Sensitivity -- 4.7 Training Time -- 4.8 Visualization -- 5 Conclusion -- References -- GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Proposed Algorithm -- 4.1 GAN Modeling -- 4.2 Architecture -- 4.3 Training Procedure -- 5 Datasets -- 6 Experiments -- 6.1 Baselines
001439657 5058_ $$a6.2 Comparative Evaluation -- 6.3 Side-by-Side Diagnostics -- 7 Conclusion -- References -- The Bures Metric for Generative Adversarial Networks -- 1 Introduction -- 2 Method -- 3 Empirical Evaluation of Mode Collapse -- 3.1 Artificial Data -- 3.2 Real Images -- 4 High Quality Generation Using a ResNet Architecture -- 5 Conclusion -- References -- Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More -- 1 Introduction -- 2 Background and Related Work -- 2.1 Energy-Based Models -- 2.2 Alternatives to the Softmax Classifier -- 3 Methodology
001439657 5058_ $$a3.1 Approach 1: Discriminative Training -- 3.2 Approach 2: Generative Training -- 3.3 Approach 3: Joint Training -- 3.4 GMMC for Inference -- 4 Experiments -- 4.1 Hybrid Modeling -- 4.2 Calibration -- 4.3 Out-Of-Distribution Detection -- 4.4 Robustness -- 4.5 Training Stability -- 4.6 Joint Training -- 5 Conclusion and Future Work -- References -- Gaussian Process Encoders: VAEs with Reliable Latent-Space Uncertainty -- 1 Introduction -- 1.1 Contributions -- 2 Background -- 2.1 Variational Autoencoder -- 2.2 Latent Variance Estimates of NN
001439657 5058_ $$a2.3 Mismatch Between the Prior and Approximate Posterior -- 3 Methodology -- 3.1 Gaussian Process Encoder -- 3.2 The Implications of a Gaussian Process Encoder -- 3.3 Out-of-Distribution Detection -- 4 Experiments -- 4.1 Log Likelihood -- 4.2 Uncertainty in the Latent Space -- 4.3 Benchmarking OOD Detection -- 4.4 OOD Polution of the Training Data -- 4.5 Synthesizing Variants of Input Data -- 4.6 Interpretable Kernels -- 5 Related Work -- 6 Conclusion -- References -- Variational Hyper-encoding Networks -- 1 Introduction -- 2 Variational Autoencoder (VAE) -- 3 Variational Hyper-encoding Networks
001439657 506__ $$aAccess limited to authorized users.
001439657 520__ $$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.
001439657 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 16, 2021).
001439657 650_0 $$aMachine learning$$vCongresses.
001439657 650_0 $$aData mining$$vCongresses.
001439657 650_6 $$aApprentissage automatique$$vCongrès.
001439657 650_6 $$aExploration de données (Informatique)$$vCongrès.
001439657 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001439657 655_7 $$aConference papers and proceedings.$$2lcgft
001439657 655_7 $$aActes de congrès.$$2rvmgf
001439657 655_0 $$aElectronic books.
001439657 7001_ $$aOliver, Nuria,$$d1970-$$eeditor$$1https://orcid.org/0000-0001-5985-691X
001439657 7001_ $$aPérez-Cruz, Fernando,$$eeditor.$$0(orcid)0000-0001-8996-5076$$1https://orcid.org/0000-0001-8996-5076
001439657 7001_ $$aKramer, Stefan,$$cProf. Dr.,$$eeditor.
001439657 7001_ $$aRead, Jesse,$$eeditor$$0(orcid)0000-0002-1013-6724$$1https://orcid.org/0000-0002-1013-6724
001439657 7001_ $$aLozano, José A.,$$d1968-$$eeditor.$$1https://orcid.org/0000-0002-4683-8111
001439657 77608 $$iPrint version: $$z9783030865191
001439657 77608 $$iPrint version: $$z9783030865214
001439657 830_0 $$aLecture notes in computer science.$$pLecture notes in artificial intelligence.
001439657 830_0 $$aLecture notes in computer science ;$$v12976.
001439657 830_0 $$aLNCS sublibrary.$$nSL 7,$$pArtificial intelligence.
001439657 852__ $$bebk
001439657 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-86520-7$$zOnline Access$$91397441.1
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