Machine learning and knowledge discovery in databases : Research track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings. Part II

Machine learning and knowledge discovery in databases : Part II / Research track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings. Nuria Oliver, Fernando Pérez-Cruz, Stefan Kramer, Jesse Read, Jose A. Lozano (eds.).

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Dettagli Bibliografici
Ente Autore: ECML PKDD (Conference) Online)
Altri autori: Oliver, Nuria, 1970- (Redattore), Pérez-Cruz, Fernando (Redattore), Kramer, Stefan, Prof. Dr (Redattore), Read, Jesse (Redattore), Lozano, José A., 1968- (Redattore)
Natura: eBook
Lingua:English
Pubblicazione: Cham, Switzerland : Springer, 2021.
Serie:Lecture notes in computer science. Lecture notes in artificial intelligence.
Lecture notes in computer science ; 12976.
LNCS sublibrary. Artificial intelligence.
Soggetti:
Machine learning > Congresses.
Data mining > Congresses.
Data mining
Machine learning
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Accesso online:Click for online access
Sommario:
  • Intro
  • 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
  • 3.2 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
  • 6.2 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
  • 3.1 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
  • 2.3 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

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