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Machine Learning and Knowledge Discovery in Databases. Research Track : European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part III / / edited by Albert Bifet, Jesse Davis, Tomas Krilavičius, Meelis Kull, Eirini Ntoutsi, Indrė Žliobaitė
| Machine Learning and Knowledge Discovery in Databases. Research Track : European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part III / / edited by Albert Bifet, Jesse Davis, Tomas Krilavičius, Meelis Kull, Eirini Ntoutsi, Indrė Žliobaitė |
| Autore | Bifet Albert |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (510 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
DavisJesse
KrilavičiusTomas KullMeelis NtoutsiEirini ŽliobaitėIndrė |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Computer engineering Computer networks Computers Image processing - Digital techniques Computer vision Software engineering Artificial Intelligence Computer Engineering and Networks Computing Milieux Computer Imaging, Vision, Pattern Recognition and Graphics Software Engineering |
| ISBN | 3-031-70352-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Invited Talks Abstracts -- The Dynamics of Memorization and Unlearning -- The Emerging Science of Benchmarks -- Enhancing User Experience with AI-Powered Search and Recommendations at Spotify -- How to Utilize (and Generate) Player Tracking Data in Sport -- Resource-Aware Machine Learning-A User-Oriented Approach -- Contents - Part III -- Research Track -- Interpretable and Generalizable Spatiotemporal Predictive Learning with Disentangled Consistency -- 1 Introduction -- 2 Related Works -- 2.1 Spatiotemporal Predictive Learning -- 2.2 Disentangled Representation -- 3 Methods -- 3.1 Preliminaries -- 3.2 Context-Motion Disentanglement -- 3.3 Disentangled Consistency -- 3.4 Practical Implementation -- 4 Experiments -- 4.1 Standard Spatiotemporal Predictive Learning -- 4.2 Generalizing to Unknown Scenes -- 4.3 Ablation Study -- 5 Limitations -- 5.1 Reverse Problem -- 5.2 Handling of Irregularly Sampled Data -- 5.3 Adaptability to Dynamic Views -- 6 Conclusion -- References -- Reinventing Node-centric Traffic Forecasting for Improved Accuracy and Efficiency -- 1 Introduction -- 2 Preliminaries -- 2.1 Formulations -- 2.2 Graph-Centric Approaches -- 2.3 Node-centric Approaches -- 3 Empirical Comparisons on Graph-Centric and Node-centric Methods -- 3.1 Results Analysis -- 4 The Proposed Framework -- 4.1 Local Proximity Modeling -- 4.2 Node Correlation Learning -- 4.3 Temporal Encoder and Predictor -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Comparisons on Common Benchmarks -- 5.3 Comparisons on the CA Dataset -- 5.4 Ablation Studies -- 5.5 Case Study -- 6 Conclusion and Future Work -- References -- Direct-Effect Risk Minimization for Domain Generalization -- 1 Introduction -- 2 Preliminaries -- 2.1 Correlation Shift -- 2.2 Problem Setting -- 3 Method -- 3.1 Recovering Indirect Effects.
3.2 Eliminating Indirect Effects in Training (TB) -- 3.3 Model Selection (VB) -- 4 Experiments -- 4.1 Datasets -- 4.2 Results -- 4.3 Foundation Models and O.o.d. Generalization -- 4.4 Visual Explanation -- 5 Related Works -- 6 Conclusion -- References -- Federated Frank-Wolfe Algorithm -- 1 Introduction -- 2 Related Work -- 3 Federated Frank-Wolfe Algorithm -- 3.1 Convergence Guarantees -- 3.2 Privacy and Communication Benefits -- 4 Design Variants of FedFW -- 4.1 FedFW with stochastic gradients -- 4.2 FedFW with Partial Client Participation -- 4.3 FedFW with Split Constraints for Stragglers -- 4.4 FedFW with Augmented Lagrangian -- 5 Numerical Experiments -- 5.1 Comparison of Algorithms in the Convex Setting -- 5.2 Comparison of Algorithms in the Non-convex Setting -- 5.3 Comparison of Algorithms in the Stochastic Setting -- 5.4 Impact of Hyperparameters -- 6 Conclusions -- References -- Bootstrap Latents of Nodes and Neighbors for Graph Self-supervised Learning -- 1 Introduction -- 2 Related Work -- 2.1 Graph Self-supervised Learning -- 2.2 Generation of Positive and Negative Pairs -- 3 Preliminary -- 3.1 Problem Statement -- 3.2 Graph Homophily -- 3.3 Bootstrapped Graph Latents -- 4 Methodology -- 4.1 Motivation -- 4.2 Bootstrap Latents of Nodes and Neighbors -- 5 Experiments -- 5.1 Experiment Setup -- 5.2 Experiment Results -- 6 Conclusion -- References -- Deep Sketched Output Kernel Regression for Structured Prediction -- 1 Introduction -- 2 Deep Sketched Output Kernel Regression -- 2.1 Learning Neural Networks with Infinite-Dimensional Outputs -- 2.2 The Pre-image Problem at Inference Time -- 3 Experiments -- 3.1 Analysis of DSOKR on Synthetic Least Squares Regression -- 3.2 SMILES to Molecule: SMI2Mol -- 3.3 Text to Molecule: ChEBI-20 -- 4 Conclusion -- References -- Hyperbolic Delaunay Geometric Alignment -- 1 Introduction -- 2 Related Work. 3 Background -- 3.1 Voronoi Cells and Delaunay Graph -- 3.2 The Klein-Beltrami Model -- 4 Method -- 4.1 Conversion to Klein-Beltrami -- 4.2 Hyperbolic Voronoi Diagram in Kn -- 4.3 HyperDGA -- 5 Experiments -- 5.1 Synthetic Data with Hyperbolic VAE -- 5.2 Real-Life Biological Data With Poincaré Embedding -- 6 Conclusions, Limitations and Future Work -- References -- ApmNet: Toward Generalizable Visual Continuous Control with Pre-trained Image Models -- 1 Introduction -- 2 Related Work -- 2.1 Pre-trained Models for Policy Learning -- 2.2 Data Augmentation for Policy Learning -- 3 Preliminaries -- 3.1 Continuous Control from Image -- 3.2 Masked Autoencoders -- 4 Method -- 4.1 ApmNetArchitecture -- 4.2 Asymmetric Policy Learning -- 5 Experiments -- 5.1 Environments Setup -- 5.2 Evaluation on Generalization Ability -- 5.3 Evaluation on Sample Efficiency -- 5.4 Ablation Study -- 6 Conclusion and Future Work -- References -- AdaHAT: Adaptive Hard Attention to the Task in Task-Incremental Learning -- 1 Introduction -- 2 Related Work -- 3 Task-Incremental Learning with Adaptive Hard Attention to the Task -- 3.1 The Algorithm: Adaptive Updates to the Parameters in the Network with Summative Attention to Previous Tasks -- 4 Experiments -- 4.1 Setups -- 4.2 Results -- 4.3 Ablation Study -- 4.4 Hyperparameters -- 5 Conclusion -- References -- Probabilistic Circuits with Constraints via Convex Optimization -- 1 Introduction -- 2 Probabilistic Circuits -- 3 Probabilistic Circuits with Constraints -- 4 Experiments -- 4.1 Scarce Datasets -- 4.2 Experiments with Missing Values -- 4.3 Fairness Experiments -- 5 Conclusions and Future Work -- References -- FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification -- 1 Introduction -- 2 Related Work -- 3 Problem Setup -- 3.1 Basic Algorithm of FL -- 3.2 Motivation. 4 FedAR Algorithm -- 5 Theoretical Analysis of FedAR -- 5.1 Convex Loss Function -- 5.2 Non-convex Loss Function -- 6 Experiments and Evaluations -- 6.1 Experimental Setup -- 6.2 Experimental Results -- 7 Conclusion -- References -- Selecting from Multiple Strategies Improves the Foreseeable Reasoning of Tool-Augmented Large Language Models -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Problem Formulation -- 3.2 Method Components -- 4 Token Consumption Estimation -- 5 Experiments -- 5.1 Benchmarks -- 5.2 Baselines -- 5.3 Action Space -- 5.4 Evaluation Metrics -- 5.5 Experimental Setup -- 6 Results -- 6.1 Benchmarking Prompting Methods -- 6.2 Impact of the Multi-strategy Mechanism -- 6.3 Error Analysis -- 7 Discussion -- 7.1 Observation-Dependent Reasoning Vs. Foreseeable Reasoning -- 7.2 Single Vs. Multiple Reasoning Trajectories -- 8 Conclusions, Future Work, and Ethical Statement -- References -- Estimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interference -- 1 Introduction -- 2 Preliminaries -- 2.1 Notations and Definitions -- 2.2 Assumptions -- 3 Spatio-Temporal Causal Inference Network (STCINet) -- 3.1 Latent Factor Model for Temporal Confounding -- 3.2 Double Attention Mechanism -- 3.3 U-Net for Spatial Interference -- 4 Experiments -- 4.1 Synthetic Dataset -- 4.2 Evaluation Metrics -- 4.3 Experimental Setup -- 4.4 Ablation Study -- 4.5 Comparison with Baseline Methods -- 4.6 Case Study on Real-World Arctic Data -- 5 Related Work -- 6 Conclusion -- References -- Continuous Geometry-Aware Graph Diffusion via Hyperbolic Neural PDE -- 1 Introduction -- 2 Preliminaries -- 3 Hyperbolic Numerical Integrators -- 3.1 Hyperbolic Projective Explicit Scheme -- 3.2 Hyperbolic Projective Implicit Scheme -- 3.3 Interpolation on Curved Space -- 4 Diffusing Graphs in Hyperbolic Space. 4.1 Hyperbolic Graph Diffusion Equation -- 4.2 Convergence of Dirichlet Energy -- 5 Empirical Results -- 5.1 Experiment Setup -- 5.2 Experiment Results -- 5.3 Ablation Study -- 6 Conclusion -- References -- SpanGNN: Towards Memory-Efficient Graph Neural Networks via Spanning Subgraph Training -- 1 Introduction -- 2 Preliminary -- 2.1 Graph Neural Networks -- 2.2 Spanning Subgraph GNN Training -- 3 SpanGNN: Memory-Efficient Full-Graph GNN Learning -- 4 Fast Quality-Aware Edge Selection -- 4.1 Variance-Minimized Sampling Strategy -- 4.2 Gradient Noise-Reduced Sampling Strategy -- 4.3 Two-Step Edge Sampling Method -- 5 Connection to Curriculum Learning -- 6 Experimental Studies -- 6.1 Experimental Setups -- 6.2 Performance of SpanGNN -- 6.3 Ablation Studies -- 6.4 Efficiency of SpanGNN -- 6.5 Performance of SpanGNN Compared to Mini-batch Training -- 7 Related Work -- 7.1 Memory-Efficient Graph Neural Networks -- 7.2 Curriculum Learning on GNN -- 8 Conclusion -- References -- AKGNet: Attribute Knowledge Guided Unsupervised Lung-Infected Area Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 Medical Image Segmentation -- 2.2 Vision-Language Based Segmentation -- 3 Method -- 3.1 Overall Framework -- 3.2 Coarse Mask Generation -- 3.3 Text Attribute Knowledge Learning Module -- 3.4 Attribute-Image Cross-Attention Module -- 3.5 Self-training Mask Refinement -- 3.6 Loss Function -- 4 Experimental Results -- 4.1 Experimental Settings -- 4.2 Comparison Results -- 4.3 Ablation Studies -- 4.4 Qualitative Evaluation Results -- 5 Conclusion -- References -- Diffusion Model in Normal Gathering Latent Space for Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 Time Series Anomaly Detection -- 2.2 Diffusion Model for Time Series Analysis -- 3 Problem Formulation -- 4 Methodology -- 4.1 Overview -- 4.2 Autoencoder. 4.3 Normal Gathering Latent Space. |
| Record Nr. | UNINA-9910886077003321 |
Bifet Albert
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning and Knowledge Discovery in Databases. Research Track : European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part VI / / edited by Albert Bifet, Jesse Davis, Tomas Krilavičius, Meelis Kull, Eirini Ntoutsi, Indrė Žliobaitė
| Machine Learning and Knowledge Discovery in Databases. Research Track : European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part VI / / edited by Albert Bifet, Jesse Davis, Tomas Krilavičius, Meelis Kull, Eirini Ntoutsi, Indrė Žliobaitė |
| Autore | Bifet Albert |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (509 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
DavisJesse
KrilavičiusTomas KullMeelis NtoutsiEirini ŽliobaitėIndrė |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Computer engineering Computer networks Computers Image processing - Digital techniques Computer vision Software engineering Artificial Intelligence Computer Engineering and Networks Computing Milieux Computer Imaging, Vision, Pattern Recognition and Graphics Software Engineering |
| ISBN | 3-031-70365-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Invited Talks Abstracts -- The Dynamics of Memorization and Unlearning -- The Emerging Science of Benchmarks -- Enhancing User Experience with AI-Powered Search and Recommendations at Spotify -- How to Utilize (and Generate) Player Tracking Data in Sport -- Resource-Aware Machine Learning-A User-Oriented Approach -- Contents - Part VI -- Research Track -- Rejection Ensembles with Online Calibration -- 1 Introduction -- 2 Notation and Related Work -- 2.1 Related Work -- 3 A Theoretical Investigation of Rejection -- 3.1 Three Distinct Situations Can Occur When Training the Rejector -- 3.2 Even a Perfect Rejector Will Overuse Its Budget -- 3.3 A Rejector Should Not Trust fs and fb -- 4 Training a Rejector for a Rejection Ensemble -- 5 Experiments -- 5.1 Experiments with Deep Learning Models -- 5.2 Experiments with Decision Trees -- 5.3 Conclusion from the Experiments -- 6 Conclusion -- References -- Lighter, Better, Faster Multi-source Domain Adaptation with Gaussian Mixture Models and Optimal Transport -- 1 Introduction -- 2 Preliminaries -- 2.1 Gaussian Mixtures -- 2.2 Domain Adaptation -- 2.3 Optimal Transport -- 3 Methodological Contributions -- 3.1 First Order Analysis of MW2 -- 3.2 Supervised Mixture-Wasserstein Distances -- 3.3 Mixture Wasserstein Barycenters -- 3.4 Multi-source Domain Adaptation Through GMM-OT -- 4 Experiments -- 4.1 Toy Example -- 4.2 Multi-source Domain Adaptation -- 4.3 Lighter, Better, Faster Domain Adaptation -- 5 Conclusion -- References -- Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense Question Answering -- 1 Introduction -- 2 Related Work -- 2.1 Commonsense Question Answering -- 2.2 Graph-Text Alignment -- 3 Task Formulation -- 4 Methods -- 4.1 Graph-Text Alignment -- 4.2 Subgraph Retrieval Module -- 4.3 Prediction -- 5 Experiments -- 5.1 Datasets.
5.2 Baselines -- 5.3 Implementation Details -- 5.4 Main Results -- 5.5 Ablation Study -- 5.6 Low-Resource Setting -- 5.7 Evaluation with other GNNs -- 5.8 Hyper-parameter Analysis -- 6 Ethical Considerations and Limitations -- 6.1 Ethical Considerations -- 6.2 Limitations -- 7 Conclusion -- References -- HetCAN: A Heterogeneous Graph Cascade Attention Network with Dual-Level Awareness -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Heterogeneous Information Network -- 3.2 Graph Neural Networks -- 3.3 Transformer-Style Architecture -- 4 The Proposed Model -- 4.1 Overall Architecture -- 4.2 Type-Aware Encoder -- 4.3 Dimension-Aware Encoder -- 4.4 Time Complexity Analysis -- 5 Experiments -- 5.1 Experimental Setups -- 5.2 Node Classification -- 5.3 Link Prediction -- 5.4 Model Analysis -- 6 Conclusion -- References -- Interpetable Target-Feature Aggregation for Multi-task Learning Based on Bias-Variance Analysis -- 1 Introduction -- 2 Preliminaries -- 2.1 Related Works: Dimensionality Reduction, Multi-task Learning -- 3 Bias-Variance Analysis: Theoretical Results -- 4 Multi-task Learning via Aggregations: Algorithms -- 5 Experimental Validation -- 5.1 Synthetic Experiments and Ablation Study -- 5.2 Real World Datasets -- 6 Conclusions and Future Developments -- References -- The Simpler The Better: An Entropy-Based Importance Metric to Reduce Neural Networks' Depth -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 How Layers Can Degenerate -- 3.2 Entropy for Rectifier Activations -- 3.3 EASIER -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Results -- 4.3 Ablation Study -- 4.4 Limitations and Future Work -- 5 Conclusion -- References -- Towards Few-Shot Self-explaining Graph Neural Networks -- 1 Introduction -- 2 Problem Definition -- 3 The Proposed MSE-GNN -- 3.1 Architecture of MSE-GNN -- 3.2 Optimization Objective. 3.3 Meta Training -- 4 Experiments -- 4.1 Datasets and Experimental Setup -- 5 Related Works -- 6 Conclusion -- References -- Uplift Modeling Under Limited Supervision -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Uplift Modeling with Graph Neural Networks (UMGNet) -- 3.2 Active Learning for Uplift GNNs (UMGNet-AL) -- 4 Experimental Evaluation -- 4.1 Datasets -- 4.2 Benchmark Models -- 4.3 Experiments -- 5 Conclusion -- References -- Self-supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 STEN: Spatial-Temporal Normality Learning -- 3.1 Problem Statement -- 3.2 Overview of The Proposed Approach -- 3.3 OTN: Order Prediction-Based Temporal Normality Learning -- 3.4 DSN: Distance Prediction-Based Spatial Normality Learning -- 3.5 Training and Inference -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Main Results -- 4.3 Ablation Study -- 4.4 Qualitative Analysis -- 4.5 Sensitivity Analysis -- 4.6 Time Efficiency -- 5 Conclusion -- References -- Modeling Text-Label Alignment for Hierarchical Text Classification -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Text Encoder -- 3.2 Graph Encoder -- 3.3 Generation of Composite Representation -- 3.4 Loss Functions -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Experimental Results -- 4.4 Analysis -- 5 Conclusion -- A Details of Statistical Test -- B Performance Analysis on Additional Datasets -- References -- Secure Aggregation Is Not Private Against Membership Inference Attacks -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Privacy Analysis of Secure Aggregation -- 4.1 Threat Model -- 4.2 SecAgg as a Noiseless LDP Mechanism -- 4.3 Asymptotic Privacy Guarantee -- 4.4 Upper Bounding M() via Dominating Pairs of Distributions. 4.5 Lower Bounding M() and Upper Bounding fM() via Privacy Auditing -- 5 Experiments and Discussion -- 6 Conclusions -- A Correlated Gaussian Mechanism -- A.1 Optimal LDP Curve: Proof of Theorem 2 -- A.2 The Case Sd={xRd:||x||2 rd} -- A.3 Trade-Off Function: Proof of Proposition 1 -- B LDP Analysis of the Mechanism (1) in a Special Case: Proof of Theorem 3 -- References -- Evaluating Negation with Multi-way Joins Accelerates Class Expression Learning -- 1 Introduction -- 2 Preliminaries -- 2.1 The Description Logic ALC -- 2.2 Class Expression Learning -- 2.3 Semantics and Properties of SPARQL -- 2.4 Worst-Case Optimal Multi-way Join Algorithms -- 3 Mapping ALC Class Expressions to SPARQL Queries -- 4 Negation in Multi-way Joins -- 4.1 Rewriting Rule for Negation and UNION Normal Form -- 4.2 Multi-way Join Algorithm -- 4.3 Implementation -- 5 Experimental Results -- 5.1 Systems, Setup and Execution -- 5.2 Datasets and Queries -- 5.3 Results and Discussion -- 6 Related Work -- 7 Conclusion And Future Work -- References -- LayeredLiNGAM: A Practical and Fast Method for Learning a Linear Non-gaussian Structural Equation Model -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 LiNGAM -- 3.2 DirectLiNGAM -- 4 LayeredLiNGAM -- 4.1 Generalization of Lemma 2 -- 4.2 Algorithm -- 4.3 Adaptive Thresholding -- 5 Experiments -- 5.1 Datasets and Evaluation Metrics -- 5.2 Determining Threshold Parameters -- 5.3 Results on Synthetic Datasets -- 5.4 Results on Real-World Datasets -- 6 Conclusion -- References -- Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties -- 1 Introduction -- 2 Background -- 2.1 Bayesian Optimization -- 2.2 Rank GP Distributions -- 3 Framework -- 3.1 Expert Preferential Inputs on Abstract Properties -- 3.2 Augmented GP with Abstract Property Preferences -- 3.3 Overcoming Inaccurate Expert Inputs. 4 Convergence Remarks -- 5 Experiments -- 5.1 Synthetic Experiments -- 5.2 Real-World Experiments -- 6 Conclusion -- References -- Enhancing LLM's Reliability by Iterative Verification Attributions with Keyword Fronting -- 1 Introduction -- 2 Related Work -- 2.1 Retrieval-Augmented Generation -- 2.2 Text Generation Attribution -- 3 Methodology -- 3.1 Task Formalization -- 3.2 Overall Framework -- 3.3 Keyword Fronting -- 3.4 Attribution Verification -- 3.5 Iterative Optimization -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Main Results -- 4.3 Ablation Studies -- 4.4 Impact of Hyperparameters -- 4.5 The Performance of the Iteration -- 5 Conclusion -- References -- Reconstructing the Unseen: GRIOT for Attributed Graph Imputation with Optimal Transport -- 1 Introduction -- 2 Related Works -- 3 Multi-view Optimal Transport Loss for Attribute Imputation -- 3.1 Notations -- 3.2 Optimal Transport and Wasserstein Distance -- 3.3 Definition of the `3́9`42`"̇613A``45`47`"603AMultiW Loss Function -- 3.4 Instantiation of `3́9`42`"̇613A``45`47`"603AMultiW Loss with Attributes and Structure -- 4 Imputing Missing Attributes with `3́9`42`"̇613A``45`47`"603AMultiW Loss -- 4.1 Architecture of GRIOT -- 4.2 Accelerating the Imputation -- 5 Experimental Analysis -- 5.1 Experimental Protocol -- 5.2 Imputation Quality v.s. Node Classification Accuracy -- 5.3 Imputing Missing Values for Unseen Nodes -- 5.4 Time Complexity -- 6 Conclusion and Perspectives -- References -- Introducing Total Harmonic Resistance for Graph Robustness Under Edge Deletions -- 1 Introduction -- 2 Problem Statement and a New Robustness Measure -- 2.1 Problem Statement and Notation -- 2.2 Robustness Measures -- 3 Related Work -- 4 Comparison of Exact Solutions -- 5 Greedy Heuristic for k-GRoDel -- 5.1 Total Harmonic Resistance Loss After Deleting an Edge. 5.2 Forest Index Loss After Deleting an Edge. |
| Record Nr. | UNINA-9910886089803321 |
|
Bifet Albert
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning and Knowledge Discovery in Databases. Research Track : European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part VII / / edited by Albert Bifet, Jesse Davis, Tomas Krilavičius, Meelis Kull, Eirini Ntoutsi, Indrė Žliobaitė
| Machine Learning and Knowledge Discovery in Databases. Research Track : European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part VII / / edited by Albert Bifet, Jesse Davis, Tomas Krilavičius, Meelis Kull, Eirini Ntoutsi, Indrė Žliobaitė |
| Autore | Bifet Albert |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (503 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
DavisJesse
KrilavičiusTomas KullMeelis NtoutsiEirini ŽliobaitėIndrė |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Computer engineering Computer networks Computers Image processing - Digital techniques Computer vision Software engineering Artificial Intelligence Computer Engineering and Networks Computing Milieux Computer Imaging, Vision, Pattern Recognition and Graphics Software Engineering |
| ISBN | 3-031-70368-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Invited Talks Abstracts -- The Dynamics of Memorization and Unlearning -- The Emerging Science of Benchmarks -- Enhancing User Experience with AI-Powered Search and Recommendations at Spotify -- How to Utilize (and Generate) Player Tracking Data in Sport -- Resource-Aware Machine Learning-A User-Oriented Approach -- Contents - Part VII -- Research Track -- Data with Density-Based Clusters: A Generator for Systematic Evaluation of Clustering Algorithms -- 1 Introduction -- 2 Related Work -- 3 A Reliable Data Generator for Density-Based Clusters -- 3.1 Main Concept of DENSIRED -- 3.2 Generation of Skeletons -- 3.3 Instantiating Data Points -- 3.4 Delimitations -- 3.5 Analysis Intrinsic Dimensionality -- 4 Experiments -- 4.1 Discussion of the Data Generator -- 4.2 Benchmarking -- 5 Conclusion -- References -- Model-Based Reinforcement Learning with Multi-task Offline Pretraining -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 Method -- 4.1 Why Model-Based RL for Domain Transfer? -- 4.2 Multi-task Offline Pretraining -- 4.3 Domain-Selective Dynamics Transfer -- 4.4 Domain-Selective Behavior Transfer -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Main Results -- 5.3 Ablation Studies -- 5.4 Analyses of Task Relations -- 5.5 Results on CARLA Environment -- 5.6 Results with Medium Offline Data -- 6 Conclusion -- References -- Advancing Graph Counterfactual Fairness Through Fair Representation Learning -- 1 Introduction -- 2 Related Work -- 2.1 Graph Neural Networks -- 2.2 Fairness in Graph -- 3 Notations -- 4 Methodology -- 4.1 Causal Model -- 4.2 Framework Overview -- 4.3 Fair Ego-Graph Generation Module -- 4.4 Counterfactual Data Augmentation Module -- 4.5 Fair Disentangled Representation Learning Module -- 4.6 Final Optimization Objectives -- 5 Experiment -- 5.1 Datasets.
5.2 Evaluation Metrics -- 5.3 Baselines -- 5.4 Experiment Results -- 6 Conclusion -- References -- Continuously Deep Recurrent Neural Networks -- 1 Introduction -- 2 Shallow and Deep Echo State Networks -- 3 Continuously Deep Echo State Networks -- 4 Analysis of Deep Dynamics -- 5 Mathematical Analysis -- 6 Experiments -- 6.1 Memory Capacity -- 6.2 Time-Series Reconstruction -- 7 Conclusions -- References -- Dynamics Adaptive Safe Reinforcement Learning with a Misspecified Simulator -- 1 Introduction -- 2 Related Work -- 2.1 Safe Reinforcement Learning -- 2.2 Sim-to-Real Reinforcement Learning -- 3 Problem Formulation -- 4 Method -- 4.1 Theoretical Motivation -- 4.2 Value Estimation Alignment with an Inverse Dynamics Model -- 4.3 Conservative Cost Critic Learning via Uncertainty Estimation -- 5 Experiments -- 5.1 Baselines and Environments -- 5.2 Overall Performance Comparison -- 5.3 Ablation Studies and Data Sensitivity Study -- 5.4 Visualization Analysis -- 5.5 Parameter Sensitivity Studies -- 6 Final Remarks -- References -- CRISPert: A Transformer-Based Model for CRISPR-Cas Off-Target Prediction -- 1 Introduction -- 2 Computational Methods for Off-Target Prediction -- 3 Method -- 3.1 Problem Formalisation -- 3.2 Model Architecture -- 3.3 CRISPR-Cas Binding Concentration Features -- 3.4 Data Imbalance Handling -- 3.5 Model Implementation -- 4 Experimental Setting -- 4.1 Data -- 4.2 Test Scenarios -- 4.3 Hyper-parameter Optimisation -- 4.4 Pre-training -- 5 Results and Analysis -- 6 Conclusion -- References -- Improved Topology Features for Node Classification on Heterophilic Graphs -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Notation -- 3.2 Motivations -- 3.3 Bin of Paths Embedding -- 3.4 Confidence and Class-Wise Training Accuracy Weighting -- 4 Evaluation -- 4.1 Experimental Settings -- 4.2 Node Classification. 4.3 Improvements on Base GNN Models -- 4.4 Distribution of CCAW Weights -- 4.5 Class-Wise Node Classification Accuracy -- 4.6 Ablations -- 4.7 Hyperparameter Analysis -- 4.8 Efficiency Analysis -- 5 Conclusion -- References -- Fast Redescription Mining Using Locality-Sensitive Hashing -- 1 Introduction -- 2 The Algorithm -- 2.1 The ReReMi Algorithm -- 2.2 Primer on LSH -- 2.3 Finding Initial Pairs -- 2.4 Extending Initial Pairs -- 2.5 Time Complexity -- 3 Experimental Evaluation -- 3.1 Experimental Setup -- 3.2 Finding Initial Pairs -- 3.3 Extending Initial Pairs -- 3.4 Building Full Redescriptions -- 4 Conclusions -- References -- sigma-GPTs: A New Approach to Autoregressive Models -- 1 Introduction -- 2 Methodology -- 2.1 sigma-GPTs: Shuffled Autoregression -- 2.2 Double Positional Encodings -- 2.3 Conditional Probabilities and Infilling -- 2.4 Token-Based Rejection Sampling -- 2.5 Other Orders -- 2.6 Denoising Diffusion Models -- 3 Results -- 3.1 General Performance -- 3.2 Training Efficiency -- 3.3 Curriculum Learning -- 3.4 Open Text Generation: t-SNE of Generated Sequences -- 3.5 Training and Generating in Fractal Order -- 3.6 Memorizing -- 3.7 Infilling and Conditional Density Estimation -- 3.8 Token-Based Rejection Sampling Scheme -- 4 Related Works -- 5 Conclusion -- References -- FairFlow: An Automated Approach to Model-Based Counterfactual Data Augmentation for NLP -- 1 Introduction -- 2 Background and Related Literature -- 3 Approach -- 3.1 Attribute Classifier Training -- 3.2 Generating Word-Pair List -- 3.3 Error Correction -- 3.4 Training the Generative Model -- 4 Experimental Set-Up -- 4.1 Training Set-Up -- 4.2 Evaluation Datasets -- 4.3 Comparative Techniques -- 5 Evaluation and Results -- 5.1 Utility -- 5.2 Extrinsic Bias Mitigation -- 5.3 Task Performance -- 5.4 Qualitative Analysis and Key Observations -- 6 Conclusion. References -- GrINd: Grid Interpolation Network for Scattered Observations -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Fourier Interpolation Layer -- 3.2 NeuralPDE -- 3.3 GrINd -- 4 Experiments -- 4.1 Data -- 4.2 Baseline Models -- 4.3 Model Configuration -- 4.4 Training -- 5 Results and Discussion -- 5.1 Interpolation Accuracy -- 5.2 DynaBench -- 5.3 Limitations -- 6 Conclusion and Future Work -- References -- MEGA: Multi-encoder GNN Architecture for Stronger Task Collaboration and Generalization -- 1 Introduction -- 2 Related Works -- 3 Methods -- 3.1 Preliminaries -- 3.2 Task Interference Problem in MT-SSL -- 3.3 MEGA Architecture -- 3.4 Pretext Tasks -- 4 Experiments -- 4.1 Experiment Setting -- 4.2 Results -- 5 Conclusion -- References -- MetaQuRe: Meta-learning from Model Quality and Resource Consumption -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Automated Algorithm Selection -- 3.2 Incorporating Resource Awareness -- 3.3 Relative Index Scaling -- 3.4 Compositional Meta-learning -- 3.5 Additional Remarks -- 4 Data on Model Quality and Resource Consumption -- 5 Experimental Results -- 5.1 Insights from MetaQuRe -- 5.2 Learning from MetaQuRe -- 6 Conclusion -- References -- Propagation Structure-Semantic Transfer Learning for Robust Fake News Detection -- 1 Introduction -- 2 Related Work -- 3 Propagation Structure-Semantic Transfer Learning Framework -- 3.1 Overview -- 3.2 Dual Teacher Models -- 3.3 Local-Global Propagation Interaction Enhanced Student Model -- 3.4 Multi-channel Knowledge Distillation Training Objective -- 4 Experiment -- 4.1 Experimental Setups -- 4.2 Main Results -- 4.3 Ablation Study -- 4.4 Generalization Evaluation -- 4.5 Robustness Evaluation -- 4.6 Parameter Analysis -- 5 Conclusion -- References -- Exploring Contrastive Learning for Long-Tailed Multi-label Text Classification -- 1 Introduction. 2 Related Work -- 2.1 Supervised Contrastive Learning -- 2.2 Multi-label Classification -- 2.3 Supervised Contrastive Learning for Multi-label Classification -- 3 Method -- 3.1 Contrastive Baseline LBase -- 3.2 Motivation -- 3.3 Multi-label Supervised Contrastive Loss -- 4 Experimental Setup -- 4.1 Datasets -- 4.2 Comparison Baselines -- 4.3 Implementation Details -- 5 Experimental Results -- 5.1 Comparison with Standard MLTC Losses -- 5.2 Fine-Tuning After Supervised Contrastive Learning -- 5.3 Representation Analysis -- 6 Conclusion -- References -- Simultaneous Linear Connectivity of Neural Networks Modulo Permutation -- 1 Introduction -- 2 Methods -- 2.1 Preliminaries -- 2.2 Aligning Networks via Permutation -- 3 Related Work -- 4 Notions of Linear Connectivity Modulo Permutation -- 5 Empirical Findings -- 5.1 Training Trajectories Are Simultaneously Weak Linearly Connected Modulo Permutation -- 5.2 Iteratively Sparsified Networks Are Simultaneously Weak Linearly Connected Modulo Permutation -- 5.3 Evidence for Strong Linear Connectivity Modulo Permutation -- 6 Algorithmic Aspects of Network Alignment -- 7 Conclusion -- References -- Fast Fishing: Approximating Bait for Efficient and Scalable Deep Active Image Classification -- 1 Introduction -- 2 Related Work -- 3 Notation -- 4 Time and Space Complexity of Bait -- 5 Approximations -- 5.1 Expectation -- 5.2 Gradient -- 6 Experimental Results -- 6.1 Setup -- 6.2 Assessment of Approximations -- 6.3 Benchmark Experiments -- 7 Conclusion -- References -- Understanding Domain-Size Generalization in Markov Logic Networks -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Basic Definitions -- 3.2 First-Order Logic -- 4 Learning in Markov Logic -- 5 Markov Logic Across Domain Sizes -- 6 Domain-Size Generalization -- 7 Experiments -- 7.1 Datasets -- 7.2 Methodology -- 7.3 Results -- 8 Conclusion. References. |
| Record Nr. | UNINA-9910886096803321 |
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Bifet Albert
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| 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 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part I / / edited by Albert Bifet, Jesse Davis, Tomas Krilavičius, Meelis Kull, Eirini Ntoutsi, Indrė Žliobaitė
| Machine Learning and Knowledge Discovery in Databases. Research Track : European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part I / / edited by Albert Bifet, Jesse Davis, Tomas Krilavičius, Meelis Kull, Eirini Ntoutsi, Indrė Žliobaitė |
| Autore | Bifet Albert |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (514 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
DavisJesse
KrilavičiusTomas KullMeelis NtoutsiEirini ŽliobaitėIndrė |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Computer engineering Computer networks Computers Image processing - Digital techniques Computer vision Software engineering Artificial Intelligence Computer Engineering and Networks Computing Milieux Computer Imaging, Vision, Pattern Recognition and Graphics Software Engineering |
| ISBN | 3-031-70341-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Invited Talks Abstracts -- The Dynamics of Memorization and Unlearning -- The Emerging Science of Benchmarks -- Enhancing User Experience with AI-Powered Search and Recommendations at Spotify -- How to Utilize (and Generate) Player Tracking Data in Sport -- Resource-Aware Machine Learning-A User-Oriented Approach -- Contents - Part I -- Research Track -- Adaptive Sparsity Level During Training for Efficient Time Series Forecasting with Transformers -- 1 Introduction -- 2 Background -- 2.1 Sparse Neural Networks -- 2.2 Time Series Forecasting -- 2.3 Problem Formulation and Notations -- 3 Analyzing Sparsity Effect in Transformers for Time Series Forecasting -- 4 Proposed Methodology: PALS -- 5 Experiments and Results -- 5.1 Experimental Settings -- 5.2 Results -- 6 Discussion -- 6.1 Performance Comparison with Pruning and Sparse Training Algorithms -- 6.2 Hyperparameter Sensitivity -- 7 Conclusions -- References -- RumorMixer: Exploring Echo Chamber Effect and Platform Heterogeneity for Rumor Detection -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Overview -- 3.2 Echo Chamber Extraction and Representation Learning -- 3.3 Neural Architecture Search for Platform Heterogeneity -- 4 Experiments -- 4.1 Experimental Setting -- 4.2 Performance Comparison (RQ1) -- 4.3 Ablation Study (RQ2) -- 4.4 Parameter Analysis (RQ3) -- 4.5 Early Rumor Detection (RQ4) -- 5 Conclusion -- References -- Diversified Ensemble of Independent Sub-networks for Robust Self-supervised Representation Learning -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Robust Self-supervised Learning via Independent Sub-networks -- 3.2 Empirical Analysis of Diversity -- 3.3 Computational Cost and Efficiency Analysis -- 4 Experimental Setup -- 5 Results and Discussion -- 6 Ablation Study -- 7 Conclusion -- References.
Modular Debiasing of Latent User Representations in Prototype-Based Recommender Systems -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Experiment Setup -- 5 Results and Analysis -- 6 Conclusion and Future Directions -- References -- A Mathematics Framework of Artificial Shifted Population Risk and Its Further Understanding Related to Consistency Regularization -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Revisiting Data Augmentation with Empirical Risk -- 3.2 The Augmented Neighborhood -- 3.3 The Artificial Shifted Population Risk -- 3.4 Understanding the Decomposition of Shifted Population Risk -- 4 Experiment -- 4.1 Experiment Implementation -- 4.2 Experimental Results -- 5 Conclusion and Discussion -- References -- Attention-Driven Dropout: A Simple Method to Improve Self-supervised Contrastive Sentence Embeddings -- 1 Introduction -- 2 Background and Related Work -- 3 Method -- 3.1 Attention Rollout Aggregation -- 3.2 Static Dropout Rate -- 3.3 Dynamic Dropout Rate -- 4 Experiment -- 4.1 Datasets and Tasks -- 4.2 Training Procedure -- 5 Result and Discussion -- 5.1 Ablation Study -- 6 Conclusion -- References -- AEMLO: AutoEncoder-Guided Multi-label Oversampling -- 1 Introduction -- 1.1 Research Goal -- 1.2 Motivation -- 1.3 Summary -- 2 Related Work -- 2.1 Multi-label Classification -- 2.2 Multi-label Imbalance Learning -- 2.3 Deep Sampling Method -- 3 Multi-label AutoEncoder Oversampling -- 3.1 Method Description and Overview -- 3.2 Loss Function -- 3.3 Generate Instances and Post-processing -- 4 Experiments and Analysis -- 4.1 Datasets -- 4.2 Experiment Setup -- 4.3 Experimental Analysis -- 4.4 Parameter Analysis -- 4.5 Sampling Time -- 5 Conclusion -- References -- MANTRA: Temporal Betweenness Centrality Approximation Through Sampling -- 1 Introduction -- 2 Related Work -- 3 Preliminaries. 4 MANTRA: Temporal Betweenness Centrality Approxi-mation Through Sampling -- 4.1 Temporal Betweenness Estimator -- 4.2 Sample Complexity Bounds -- 4.3 Fast Approximation of the Characteristic Quantities -- 4.4 The MANTRA Framework -- 5 Experimental Evaluation -- 5.1 Experimental Setting -- 5.2 Networks -- 5.3 Experimental Results -- 6 Conclusions -- References -- Dimensionality-Induced Information Loss of Outliers in Deep Neural Networks -- 1 Introduction -- 2 Problem Setting and Related Work -- 2.1 Stable Rank of the Matrix -- 2.2 Feature-Based Detection -- 2.3 Projection-Based Detection -- 2.4 Similarity of DNN Representations -- 2.5 Noise Sensitivity in the DNN -- 3 Results -- 3.1 Overview of the Experiments and a Possible Picture -- 3.2 Observation of Dimensionality via Stable Ranks -- 3.3 Transition of OOD Detection Performance -- 3.4 Block Structure of CKA -- 3.5 Instability of OOD Samples to Noise Injection -- 3.6 Dataset Bias-Induced Imbalanced Inference -- 3.7 Quantitative Comparison of OOD Detection Performance -- 4 Discussion -- 5 Summary and Conclusion -- References -- Towards Open-World Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach -- 1 Introduction -- 2 Methodology -- 2.1 Problem Formulation -- 2.2 Embedding Encoder -- 2.3 Denoising Interest-Aware Network -- 2.4 Fusion Gate Unit -- 2.5 Model Training -- 2.6 Inductive Representation Generator -- 3 Experiments -- 3.1 Datasets -- 3.2 Experiment Setting -- 3.3 Performance Comparisons (RQ1) -- 3.4 Ablation Study (RQ2) -- 3.5 Online Evaluation (RQ3) -- 3.6 Model Analyses (RQ4) -- 3.7 Parameter Sensitivity (RQ5) -- 4 Related Work -- 5 Conclusion -- References -- MixerFlow: MLP-Mixer Meets Normalising Flows -- 1 Introduction -- 2 Related Works -- 3 Preleminaries -- 4 MixerFlow Architecture and Its Components -- 5 Experiments. 5.1 Density Estimation on 3232 Datasets -- 5.2 Density Estimation on 6464 Datasets -- 5.3 Enhancing MAF with the MixerFlow -- 5.4 Datasets with Specific Permutations -- 5.5 Hybrid Modelling -- 5.6 Integration of Powerful Architecture -- 6 Conclusion and Limitations -- 7 Future Work and Broader Impact -- References -- Handling Delayed Feedback in Distributed Online Optimization: A Projection-Free Approach -- 1 Introduction -- 1.1 Our Contribution -- 1.2 Related Work -- 2 Projection-Free Algorithms Under Delayed Feedback -- 2.1 Preliminaries -- 2.2 Centralized Algorithm -- 2.3 Distributed Algorithm -- 3 Numerical Experiments -- 4 Concluding Remarks -- References -- Secure Dataset Condensation for Privacy-Preserving and Efficient Vertical Federated Learning -- 1 Introduction -- 2 Related Work -- 2.1 Vertical Federated Learning -- 2.2 Privacy Protection in VFL -- 2.3 Dataset Size Reduction in FL -- 3 Preliminaries -- 3.1 Problem Formulation -- 3.2 Dataset Condensation -- 3.3 Secure Aggregation -- 3.4 Differential Privacy -- 4 Proposed Approach -- 4.1 Overview -- 4.2 Class-Wise Secure Aggregation -- 4.3 VFDC Algorithm -- 4.4 Privacy Analysis -- 5 Experimental Study -- 5.1 Experimental Setup -- 5.2 Visualization of Condensed Dataset -- 5.3 Performance Comparison -- 5.4 Efficiency Improvement -- 5.5 Impact of Hyperparameters -- 6 Conclusion and Future Directions -- References -- Neighborhood Component Feature Selection for Multiple Instance Learning Paradigm -- 1 Introduction -- 2 Methods -- 2.1 The Lazy Learning Approach for Multiple Instance Learning Setting -- 2.2 Neighborhood Component Feature Selection for Single Instance Learning Setting -- 2.3 Our Proposal: Neighborhood Component Feature Selection for the Multiple Instance Learning Setting -- 3 Datasets -- 3.1 Musk Dataset -- 3.2 DEAP Dataset -- 4 Experimental Procedure -- 5 Experimental Results. 5.1 Musk Dataset -- 5.2 DEAP Dataset -- 5.3 Comparison with State-of-the-Art -- 5.4 Statistical Significance -- 5.5 Computational Complexity -- 6 Conclusions -- References -- MESS: Coarse-Grained Modular Two-Way Dialogue Entity Linking Framework -- 1 Introduction -- 2 Related Work -- 2.1 Mention-to-Entities -- 2.2 Transferred EL -- 3 Our MESS Framework -- 3.1 M2E Module -- 3.2 E2M Module -- 3.3 SS Module -- 3.4 Dialogue Module -- 4 Experiments -- 4.1 Setting -- 4.2 Results -- 4.3 Ablation Studies -- 5 Conclusion -- References -- Session Target Pair: User Intent Perceiving Networks for Session-Based Recommendation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Statement -- 3.2 Session-Level Intent Representation Module -- 3.3 Target-Level Intent Representation Module -- 3.4 Intent Alignment Mechanism Module -- 3.5 Prediction and Training -- 4 Experiments -- 4.1 Experiment Setups -- 4.2 Overall Performance -- 4.3 Model Analysis and Discussion -- 5 Conclusion -- References -- Hierarchical Fine-Grained Visual Classification Leveraging Consistent Hierarchical Knowledge -- 1 Introduction -- 2 Related Work -- 2.1 Fine-Grained Visual Classification -- 2.2 Hierarchical Multi-granularity Classification -- 2.3 Graph Representation Learning -- 3 Approach -- 3.1 Problem Setting -- 3.2 Multi-granularity Graph Convolutional Neural Network -- 3.3 Hierarchy-Aware Conditional Supervised Learning -- 3.4 Loss Function -- 3.5 Tree-Structured Granularity Consistency Rate -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Settings -- 4.3 Compared Methods -- 4.4 Ablation Study -- 4.5 Comparison with State-of-the-Art Method -- 4.6 Qualitative Analysis -- 5 Conclusion -- References -- Backdoor Attacks with Input-Unique Triggers in NLP -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Problem Formulation -- 3.2 NURA: Input-Unique Backdoor Attack. 3.3 Model Training. |
| Record Nr. | UNINA-9910886100803321 |
|
Bifet Albert
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track : European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part VIII / / edited by Albert Bifet, Povilas Daniušis, Jesse Davis, Tomas Krilavičius, Meelis Kull, Eirini Ntoutsi, Kai Puolamäki, Indrė Žliobaitė
| Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track : European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part VIII / / edited by Albert Bifet, Povilas Daniušis, Jesse Davis, Tomas Krilavičius, Meelis Kull, Eirini Ntoutsi, Kai Puolamäki, Indrė Žliobaitė |
| Autore | Bifet Albert |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (487 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
DaniusisPovilas
DavisJesse KrilavičiusTomas KullMeelis NtoutsiEirini PuolamäkiKai ŽliobaitėIndrė |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Computer engineering Computer networks Computers Image processing - Digital techniques Computer vision Software engineering Artificial Intelligence Computer Engineering and Networks Computing Milieux Computer Imaging, Vision, Pattern Recognition and Graphics Software Engineering |
| ISBN | 3-031-70371-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910886080803321 |
|
Bifet Albert
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||