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Les avant-gardes en Catalogne (1916-1930) / / Serge Salaün, Elisée Trenc
| Les avant-gardes en Catalogne (1916-1930) / / Serge Salaün, Elisée Trenc |
| Autore | Ballester Elisée Trenc |
| Pubbl/distr/stampa | Paris, : Presses Sorbonne Nouvelle, 2017 |
| Descrizione fisica | 1 online resource (134 p.) |
| Altri autori (Persone) |
OliverNuria
PrudonMontserrat XerriCatherine SalaünSerge TrencElisée |
| Soggetto topico |
Catalan poetry - 20th century - History and criticism
Avant-garde (Aesthetics) - Spain - Catalonia Arts, Catalan - 20th century |
| Soggetto non controllato |
art
XXème siècle modernisme littérature avant-garde Catalogne Espagne |
| ISBN | 2-87854-730-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | fre |
| Altri titoli varianti | avant-gardes en Catalogne |
| Record Nr. | UNINA-9910169187803321 |
Ballester Elisée Trenc
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| Paris, : Presses Sorbonne Nouvelle, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine learning and knowledge discovery in databases : research track : European conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, proceedings, part III / / Nuria Oliver [and four others], editors
| Machine learning and knowledge discovery in databases : research track : European conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, proceedings, part III / / Nuria Oliver [and four others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (857 pages) |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Machine learning
Data mining |
| ISBN | 3-030-86523-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910503007103321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine learning and knowledge discovery in databases : research track : European conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, proceedings, part III / / Nuria Oliver [and four others], editors
| Machine learning and knowledge discovery in databases : research track : European conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, proceedings, part III / / Nuria Oliver [and four others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (857 pages) |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Machine learning
Data mining |
| ISBN | 3-030-86523-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464501603316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
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]
| 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] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2021] |
| Descrizione fisica | 1 online resource (845 pages) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Artificial intelligence - Research
Artificial intelligence - Data processing |
| ISBN | 3-030-86520-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 -- 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 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 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. |
| Record Nr. | UNINA-9910502985903321 |
| Cham, Switzerland : , : Springer International Publishing, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico 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 [and four others]
| 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] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2021] |
| Descrizione fisica | 1 online resource (845 pages) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Artificial intelligence - Research
Artificial intelligence - Data processing |
| ISBN | 3-030-86520-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 -- 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 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 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. |
| Record Nr. | UNISA-996464527703316 |
| Cham, Switzerland : , : Springer International Publishing, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Pervasive Computing Paradigms for Mental Health : Selected Papers from MindCare 2016, Fabulous 2016, and IIoT 2015 / / edited by Nuria Oliver, Silvia Serino, Aleksandar Matic, Pietro Cipresso, Nenad Filipovic, Liljana Gavrilovska
| Pervasive Computing Paradigms for Mental Health : Selected Papers from MindCare 2016, Fabulous 2016, and IIoT 2015 / / edited by Nuria Oliver, Silvia Serino, Aleksandar Matic, Pietro Cipresso, Nenad Filipovic, Liljana Gavrilovska |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XV, 174 p. 57 illus.) |
| Disciplina | 502.85 |
| Collana | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering |
| Soggetto topico |
Medical informatics
Computer engineering Computer networks Artificial intelligence Health Informatics Computer Engineering and Networks Artificial Intelligence Computer Communication Networks |
| ISBN |
9783319749358
3319749358 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Computer-Based Programs as Suitable Intervention Tools for Older People with Mental Disorders -- ONParkinson – Innovative mHealth to support the triad: patient, carer and health professional -- A Process for Selecting and Validating Awe-Inducing Audio-Visual Stimuli -- A Play Therapy based Full-body Interaction Intervention Tool for Children with Autism -- Stress Detection from Speech Using Spectral Slope Measurements -- Technological devices as an opportunity for people with Parkinson -- just Physio kidding - NUI and Gamification based Therapeutic Intervention for Children with Special Needs -- An innovative virtual reality-based training program for the rehabilitation of cognitive frail patients -- COLLEGO: An Interactive Platform for Studying Joint Action During an Ecological Collaboration Task -- Mobile applications as good intervention tools for individuals with depression -- Assessment of mechanical stiffness of jumping using force plate -- Numerical modeling of drug delivery in organs:from CT scans to FE model -- FPGA Implementation of Face Recognition Algorithm -- Persons Counting and Monitoring System based on Passive Infrared Sensors and Ultrasonic Sensors (PIRUS) -- Personalized and intelligent sleep lifestyle reasoner with web application for improving quality of sleep part of AAL architecture -- Implementation of Daily Functioning and Habits Building Reasoner part of AAL Architecture -- 5G-TCP: Enhanced transport protocol for Future Mobile Networks -- Monitoring the Black Sea Region using Satellite Earth Observation and Ground Telemetry -- Multi-microphone Noise Reduction System Integrating Nonlinear Multi-band Spectral Subtraction -- Practical Implementation Aspects of the Data Timed Sending (DTS) Protocol Using Wake-up Radio (WuR) -- Intrusion Prevention System Evaluation for SDN-enabled IoT Networks -- A Privacy Scheme for Monitoring Devices in the Internet of Things -- How to Deal with Interoperability Testing in The Challenging and Ever-changing Context of IoT -- Wi-Suite: Tools and Services for Managing IoT Infrastructures. . |
| Record Nr. | UNINA-9910299274603321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare / / general chairs, Nuria Oliver, Mary Czerwinski
| Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare / / general chairs, Nuria Oliver, Mary Czerwinski |
| Pubbl/distr/stampa | New York : , : ACM, , 2017 |
| Descrizione fisica | 1 online resource (503 pages) |
| Disciplina | 004 |
| Soggetto topico |
Ubiquitous computing
Medical informatics Medical telematics |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | PervasiveHealth '17 : 11th EAI International Conference on Pervasive Computing Technologies for Healthcare : Barcelona, Spain, May 23-2 |
| Record Nr. | UNINA-9910376033203321 |
| New York : , : ACM, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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User Modeling, Adaptation and Personalization : 19th International Conference, UMAP 2011, Girona, Spain, July 11-15, 2011 / / edited by Joseph Konstan, Ricardo Conejo, Jose L. Marzo, Nuria Oliver
| User Modeling, Adaptation and Personalization : 19th International Conference, UMAP 2011, Girona, Spain, July 11-15, 2011 / / edited by Joseph Konstan, Ricardo Conejo, Jose L. Marzo, Nuria Oliver |
| Edizione | [1st ed. 2011.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2011 |
| Descrizione fisica | 1 online resource (XIX, 464 p.) |
| Disciplina | 005.7 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Application software
Information storage and retrieval Artificial intelligence Computer communication systems Algorithms Database management Information Systems Applications (incl. Internet) Information Storage and Retrieval Artificial Intelligence Computer Communication Networks Algorithm Analysis and Problem Complexity Database Management |
| ISBN | 3-642-22362-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | UMAP'11 |
| Record Nr. | UNISA-996465502703316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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