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Record Nr. |
UNINA9910502985903321 |
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Titolo |
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 [and four others] |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer International Publishing, , [2021] |
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©2021 |
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ISBN |
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Descrizione fisica |
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1 online resource (845 pages) |
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Collana |
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Lecture Notes in Computer Science ; ; v.12976 |
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Disciplina |
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Soggetti |
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Artificial intelligence - Research |
Artificial intelligence - Data processing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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 |
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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 -- 3.1 Hyper-auto-encoding Problem -- 3.2 Hyper-encoding Problem -- 3.3 Minimum Description Length -- 3.4 Compact Hyper-decoder Architecture -- 3.5 Applications -- 4 Experiments -- 4.1 Data Sets -- 4.2 Model Settings -- 4.3 Model Behavior -- 4.4 Robust Outlier Detection -- 4.5 Novelty Discovery -- 5 Related Work -- 6 Conclusion -- References -- Principled Interpolation in Normalizing Flows -- 1 Introduction -- 2 An Intuitive Solution -- 3 Normalizing Flows -- 4 Base Distributions on p-Norm Spheres -- 4.1 The Case p = 1 -- 4.2 The Case p = 2 -- 5 Experiments -- 5.1 Performance Metrics and Setup -- 5.2 Data -- 5.3 Architecture -- 5.4 Quantitative Results -- 5.5 Qualitative Results -- 6 Related Work -- 7 Conclusion -- References -- CycleGAN Through the Lens of (Dynamical) Optimal Transport -- 1 Introduction. |
2 Desiderata for UDT and Analysis of CycleGAN -- 2.1 What Should Be the Properties of a UDT Solution? -- 2.2 CycleGAN Is Biased Towards Low Energy Transformations -- 3 UDT as Optimal Transport -- 3.1 A (Dynamical) OT Model for UDT -- 3.2 Regularity of OT Maps -- 3.3 Computing the Inverse -- 4 A Residual Instantiation from Dynamical OT -- 4.1 Linking the Dynamical Formulation with CycleGAN -- 4.2 A Typical UDT Task -- 4.3 Imbalanced CelebA task -- 5 Related Work -- 6 Discussion and Conclusion -- References -- Decoupling Sparsity and Smoothness in Dirichlet Belief Networks -- 1 Introduction -- 2 Preliminary Knowledge -- 3 Sparse and Smooth Dirichlet Belief Networks -- 3.1 Generative Process -- 3.2 Necessity of Fixing biK(l)=1 -- 4 Related Work -- 5 ssDirBN for Relational Modelling -- 5.1 Inference -- 5.2 Experimental Results -- 6 Conclusion -- References -- Algorithms and Learning Theory -- Self-bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound -- 1 Introduction -- 2 Majority Vote Learning -- 2.1 Notations and Setting -- 2.2 Gibbs Risk, Joint Error and C-Bound -- 2.3 Related Works -- 3 PAC-Bayesian C-Bounds -- 3.1 An Intuitive Bound-McAllester's View -- 3.2 A Tighter Bound-Seeger's View -- 3.3 Another Tighter Bound-Lacasse's View -- 4 Self-bounding Algorithms for PAC-Bayesian C-Bounds -- 4.1 Algorithm Based on McAllester's View -- 4.2 Algorithm Based on Seeger's View -- 4.3 Algorithm Based on Lacasse's View -- 5 Experimental Evaluation -- 5.1 Empirical Setting -- 5.2 |
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Analysis of the Results -- 6 Conclusion and Future Work -- References -- Midpoint Regularization: From High Uncertainty Training Labels to Conservative Classification Decisions -- 1 Introduction -- 2 Related Work -- 3 Label Smoothing over Midpoint Samples -- 3.1 Preliminaries -- 3.2 Midpoint Generation. |
3.3 Learning Smoothing Distribution for Midpoints -- 3.4 Optimization -- 4 Experiments -- 4.1 Datasets, Baselines, and Settings -- 4.2 Predictive Accuracy -- 4.3 Ablation Studies -- 4.4 Uncertainty Label and Conservative Classification -- 4.5 Testing on Out-of-Distribution Data -- 5 Conclusion and Future Work -- References -- Learning Weakly Convex Sets in Metric Spaces -- 1 Introduction -- 2 Preliminaries -- 3 Weak Convexity in Metric Spaces -- 3.1 Some Basic Properties of Weakly Convex Sets -- 4 Learning in the Extensional Problem Setting -- 4.1 Application Scenario: Vertex Classification -- 5 The Intensional Problem Setting -- 5.1 Learning Weakly Convex Boolean Functions -- 5.2 Learning Weakly Convex Axis-Aligned Hyperrectangles -- 6 Concluding Remarks -- References -- Disparity Between Batches as a Signal for Early Stopping -- 1 Introduction -- 2 Related Work -- 3 Generalization Penalty -- 4 Gradient Disparity -- 5 Early Stopping Criterion -- 6 Discussion and Final Remarks -- References -- Learning from Noisy Similar and Dissimilar Data -- 1 Introduction -- 2 Problem Setup -- 3 Loss Correction Approach -- 4 Weighted Classification Approach -- 5 Experiments -- 6 Conclusion and Future Work -- References -- Knowledge Distillation with Distribution Mismatch -- 1 Introduction -- 2 Related Works -- 3 Framework -- 3.1 Problem Definition -- 3.2 Proposed Method KDDM -- 4 Experiments and Discussions -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Results on MNIST -- 4.4 Results on CIFAR-10 -- 4.5 Results on CIFAR-100 -- 4.6 Distillation When Teacher-Data and Student-Data Are Identical -- 5 Conclusion -- References -- Certification of Model Robustness in Active Class Selection -- 1 Introduction -- 1.1 Active Class Selection Constitutes a Domain Gap -- 1.2 A Qualitative Intuition from Information Theory -- 2 A Quantitative Perspective from Learning Theory. |
2.1 Quantification of the Domain Gap -- 2.2 Certification of Domain Robustness for Binary Predictors -- 3 Experiments -- 3.1 Binary (, ) Certificates Are Tight -- 3.2 Binary (, ) Certificates in Astro-Particle Physics -- 4 Related Work -- 5 Conclusion -- References -- Graphs and Networks -- Inter-domain Multi-relational Link Prediction -- 1 Introduction -- 2 Preliminary -- 2.1 RESCAL -- 2.2 Optimal Transport -- 2.3 Maximum Mean Discrepancy -- 3 Problem Setting and Proposed Method -- 3.1 Problem Setting -- 3.2 Proposed Objective Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Methods and Baselines -- 4.3 Implementation Details -- 4.4 Experimental Results -- 5 Related Work -- 6 Conclusion and Future Work -- References -- GraphSVX: Shapley Value Explanations for Graph Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Preliminary Concepts and Background -- 3.1 Graph Neural Networks -- 3.2 The Shapley Value -- 4 A Unified Framework for GNN Explainers -- 5 Proposed Method -- 5.1 Mask and Graph Generators -- 5.2 Explanation Generator -- 5.3 Decomposition Model -- 5.4 Efficient Approximation Specific to GNNs -- 5.5 Desirable Properties of Explanations -- 6 Experimental Evaluation -- 6.1 Synthetic and Real Datasets with Ground Truth -- 6.2 Real-World Datasets Without Ground Truth -- 7 Conclusion -- References -- Multi-view Self-supervised Heterogeneous Graph Embedding -- 1 Introduction -- 2 Related Work -- 2.1 Self-supervised Learning on Graphs -- 2.2 Heterogeneous Graph Embedding -- 3 The Proposed Model -- 3.1 Model Framework -- 3.2 Heterogeneous |
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Context Encoding -- 3.3 Multi-view Contrastive Learning -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Node Classification -- 4.3 Link Prediction -- 4.4 Ablation Study -- 4.5 Visualization -- 5 Conclusion -- References. |
Semantic-Specific Hierarchical Alignment Network for Heterogeneous Graph Adaptation. |
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