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Machine learning and knowledge discovery in databases . Part V : Applied Data Science Track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, proceedings / / Yuxiao Dong [and three others]
| Machine learning and knowledge discovery in databases . Part V : Applied Data Science Track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, proceedings / / Yuxiao Dong [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2021] |
| Descrizione fisica | 1 online resource (542 pages) |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Computer Science Ser. |
| Soggetto topico |
Machine learning
Data mining |
| ISBN | 3-030-86517-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part V -- Automating Machine Learning, Optimization, and Feature Engineering -- PuzzleShuffle: Undesirable Feature Learning for Semantic Shift Detection -- 1 Introduction -- 2 Related Work -- 2.1 Out-of-Distribution Detection -- 2.2 Data Augmentation -- 2.3 Uncertainty Calibration -- 3 Preliminaries -- 3.1 The Effects by Perturbation -- 3.2 Adversarial Undesirable Feature Learning -- 4 Proposed Method -- 4.1 PuzzleShuffle Augmentation -- 4.2 Adaptive Label Smoothing -- 4.3 Motivation -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Compared Methods -- 5.3 Results -- 5.4 Analysis -- 6 Conclusion -- References -- Enabling Machine Learning on the Edge Using SRAM Conserving Efficient Neural Networks Execution Approach -- 1 Introduction -- 2 Background and Related Work -- 2.1 Deep Model Compression -- 2.2 Executing Neural Networks on Microcontrollers -- 3 Efficient Neural Network Execution Approach Design -- 3.1 Tensor Memory Mapping (TMM) Method Design -- 3.2 Loading Fewer Tensors and Tensors Re-usage -- 3.3 Finding the Cheapest NN Graph Execution Sequence -- 3.4 Core Algorithm -- 4 Experimental Evaluation -- 4.1 SRAM Usage -- 4.2 Model Performance -- 4.3 Inference Time and Energy Consumption -- 5 Conclusion -- References -- AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge -- 1 Introduction -- 2 Challenge Setting -- 2.1 Phases -- 2.2 Protocol -- 2.3 Datasets -- 2.4 Metrics -- 2.5 Platform, Hardware and Limitations -- 2.6 Baseline -- 2.7 Results -- 3 Post Challenge Experiments -- 3.1 Reproducibility -- 3.2 Overfitting and Generalisation -- 3.3 Comparison to Open Source AutoML Solutions -- 3.4 Impact of Time Budget -- 3.5 Dataset Difficulty -- 4 Conclusion and Future Work -- References -- Methods for Automatic Machine-Learning Workflow Analysis -- 1 Introduction.
2 Problem Definition -- 3 Related Work -- 4 Residual Graph-Level Graph Convolutional Networks -- 5 Datasets -- 6 Workflow Similarity -- 7 Structural Performance Prediction -- 8 Component Refinement and Suggestion -- 9 Conclusion -- References -- ConCAD: Contrastive Learning-Based Cross Attention for Sleep Apnea Detection -- 1 Introduction -- 2 Related Work -- 2.1 Sleep Apnea Detection -- 2.2 Attention-Based Feature Fusion -- 2.3 Contrastive Learning -- 3 Methodology -- 3.1 Expert Feature Extraction and Data Augmentation -- 3.2 Feature Extractor -- 3.3 Cross Attention -- 3.4 Contrastive Learning. -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Compared Methods -- 4.3 Experiment Setup -- 4.4 Results and Discussions -- 5 Conclusions and Future Work -- References -- Machine Learning Based Simulations and Knowledge Discovery -- DeepPE: Emulating Parameterization in Numerical Weather Forecast Model Through Bidirectional Network -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Problem Definition -- 3.2 Deep Parameterization Emulator -- 3.3 Transfer Scheme -- 3.4 Training -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Setup -- 5 Results -- 5.1 DeepPE Performance Analysis -- 5.2 Transfer Analysis -- 6 Conclusion -- References -- Effects of Boundary Conditions in Fully Convolutional Networks for Learning Spatio-Temporal Dynamics -- 1 Introduction -- 2 Method -- 2.1 Learning an Auto-Regressive Model -- 2.2 Neural Network Convolutional Architecture -- 2.3 Boundary Condition Treatment -- 2.4 Loss Function -- 3 Applications: Time-Evolving PDEs -- 3.1 Acoustic Propagation of Gaussian Pulses -- 3.2 Diffusion of Temperature Spots -- 3.3 Datasets Generation and Parameters -- 4 Results -- 5 Conclusion -- References -- Physics Knowledge Discovery via Neural Differential Equation Embedding -- 1 Introduction -- 2 Phase-Field Model -- 3 Problem Statement. 4 Neural Differential Equation Embedding -- 5 Related Work -- 6 Experiments -- 7 Conclusion -- References -- A Bayesian Convolutional Neural Network for Robust Galaxy Ellipticity Regression -- 1 Introduction -- 2 Estimating Galaxy Ellipticity from Images -- 3 A Method to Assess Uncertainty in Ellipticity Estimation -- 3.1 Estimation of Noise Related Uncertainty -- 3.2 Estimation of Blend Related Uncertainty -- 3.3 Training Protocol -- 4 Experiments -- 4.1 Estimation of Uncertainty Related to Noise -- 4.2 Estimation of Uncertainty Related to Blending -- 5 Conclusion -- References -- Precise Weather Parameter Predictions for Target Regions via Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Pertinent Background -- 4 Learning-Based Modelets for Weather Forecasting -- 4.1 Micro Model -- 4.2 Micro-Macro Model -- 5 Experiment -- 5.1 Setting -- 5.2 Overall Performance -- 5.3 Comparing to Other Methods -- 5.4 Ablation Study -- 5.5 Abnormal Weather Forecasting -- 6 Conclusion -- References -- Action Set Based Policy Optimization for Safe Power Grid Management -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 3.1 Power Grid Management -- 3.2 Search-Based Planning -- 4 Methodology -- 4.1 Search with the Action Set -- 4.2 Policy Optimization -- 4.3 Discussion on Action Set Size -- 4.4 Algorithm Summary -- 5 Experiments -- 5.1 Experiment Setup -- 5.2 Implementation -- 5.3 Competition -- 6 Conclusion -- A Grid2Op Environment -- References -- Conditional Neural Relational Inference for Interacting Systems -- 1 Introduction -- 2 Related Work -- 3 The Conditional Neural Inference Model -- 3.1 Encoding, Establishing the Body-Part Interactions -- 3.2 Decoding, Establishing the Dynamics -- 3.3 Conditional Generation -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Results -- 5 Conclusion -- References -- Recommender Systems and Behavior Modeling. MMNet: Multi-granularity Multi-mode Network for Item-Level Share Rate Prediction -- 1 Introduction -- 2 Related Works -- 3 Preliminary -- 4 Methodology -- 4.1 Overall Framework -- 4.2 Fine-Granularity Module -- 4.3 Coarse-Granularity Module -- 4.4 Meta-info Modeling Module -- 4.5 Optimization Objectives -- 5 Online Deployment -- 6 Experiments -- 6.1 Datasets -- 6.2 Baselines and Experimental Settings -- 6.3 Offline Item-Level Share Rate Prediction -- 6.4 Online A/B Tests -- 6.5 Ablation Studies -- 6.6 Parameter Analyses -- 7 Conclusion and Future Work -- References -- The Joy of Dressing Is an Art: Outfit Generation Using Self-attention Bi-LSTM -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Bayesian Personalized Ranking (MF) Embedding -- 3.2 Training Dataset Generation -- 3.3 Bi-LSTM -- 3.4 Self-attention Bi-LSTM -- 3.5 Generation of New Outfits -- 4 Results -- 5 Conclusion -- References -- On Inferring a Meaningful Similarity Metric for Customer Behaviour -- 1 Introduction -- 2 Problem Definition -- 3 SIMPRIM Framework -- 3.1 Journey Log to Journey Profiles -- 3.2 Measuring Similarity -- 3.3 Dimensionality Reduction -- 3.4 Co-learning of Metric Weights and Journey Clustering -- 3.5 Evaluation -- 4 Experimental Evaluation -- 4.1 Customer Service Process at Anonycomm -- 4.2 BPIC 2012 Real Dataset -- 5 Related Work -- 6 Conclusion -- References -- Quantifying Explanations of Neural Networks in E-Commerce Based on LRP -- 1 Introduction -- 2 Preliminaries -- 3 Formal Model of an Online Shop -- 4 Explanation Approach -- 4.1 Explanation via Layer-Wise Relevance Propagation -- 4.2 Input Analysis with Leave-One-Out Method -- 4.3 Explanation Quantity Measures -- 5 Evaluation -- 5.1 Evaluation Setting -- 5.2 Evaluation Data Set -- 5.3 Evaluation Results -- 6 Conclusion -- References -- Natural Language Processing. Balancing Speed and Accuracy in Neural-Enhanced Phonetic Name Matching -- 1 Introduction -- 1.1 Challenges -- 2 Related Work -- 3 Phonetic Name Matching Systems -- 3.1 Neural Name Transliteration -- 3.2 Neural Name Matching -- 4 Experimental Results -- 4.1 Training and Hyperparameters -- 4.2 Results -- 5 Conclusion and Future Work -- References -- Robust Learning for Text Classification with Multi-source Noise Simulation and Hard Example Mining -- 1 Introduction -- 2 Related Work -- 2.1 Noise Reduction -- 2.2 Adversarial Training -- 2.3 Training with Noisy Data -- 3 Problem -- 3.1 Notation -- 3.2 Text Classification -- 3.3 A Practical Scenario -- 3.4 OCR Noise Simulation -- 3.5 Robust Training -- 4 Approach -- 4.1 OCR Noise Simulation -- 4.2 Noise Invariance Representation -- 4.3 Hard Example Mining -- 4.4 The Overall Framework -- 5 Experiment -- 5.1 Dataset -- 5.2 Implementation -- 5.3 Results -- 6 Analysis -- 6.1 Naive Training with a Single Noise Simulation Method -- 6.2 The Impact of Different Noise Level -- 6.3 The Impact of Hard Example Mining -- 6.4 The Impact of Stability Loss -- 7 Conclusion -- References -- Topic-to-Essay Generation with Comprehensive Knowledge Enhancement -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Task Formulation -- 3.2 Model Description -- 3.3 Training and Inference -- 4 Experiments -- 4.1 Datasets -- 4.2 Settings -- 4.3 Baselines -- 4.4 Evaluation Metrics -- 4.5 Experimental Results -- 4.6 Validity of Knowledge Transfer -- 4.7 Case Study -- 5 Conclusion -- References -- Analyzing Research Trends in Inorganic Materials Literature Using NLP -- 1 Introduction -- 2 Related Work -- 3 Corpus Preparation -- 3.1 Definition of Types -- 3.2 Collecting Literature -- 3.3 Annotation -- 4 Approach -- 4.1 Sequence Labeling Architecture -- 4.2 Numeric Normalization -- 5 Results -- 5.1 Inter-Annotator Agreement. 5.2 Comparing Language Models. |
| Record Nr. | UNISA-996464522303316 |
| Cham, Switzerland : , : Springer International Publishing, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine learning and knowledge discovery in databases . Part IV : Applied data science track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings / / Yuxiao Dong [and three others], (editors)
| Machine learning and knowledge discovery in databases . Part IV : Applied data science track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings / / Yuxiao Dong [and three others], (editors) |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (579 pages) |
| Disciplina | 006.31 |
| Collana | Lecture notes in computer science |
| Soggetto topico | Machine learning |
| ISBN | 3-030-86514-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part IV -- Anomaly Detection and Malware -- Anomaly Detection: How to Artificially Increase Your F1-Score with a Biased Evaluation Protocol -- 1 Introduction -- 2 Related Work -- 3 Issues When Using F1-Score and AVPR Metrics -- 3.1 Formalism and Problem Statement -- 3.2 Definition of the Metrics -- 3.3 Evaluation Protocols: Theory vs Practice -- 3.4 Metrics Sensitivity to the Contamination Rate of the Test Set -- 3.5 How to Artificially Increase Your F1-Score and AVPR -- 3.6 F1-Score Cannot Compare Datasets Difficulty -- 4 Call for Action -- 4.1 Use AUC -- 4.2 Do Not Waste Anomalous Samples -- 5 Conclusion -- References -- Mining Anomalies in Subspaces of High-Dimensional Time Series for Financial Transactional Data -- 1 Introduction -- 2 Related Work -- 3 Definitions and Notation -- 4 System Architecture -- 4.1 Subspace Searching Module -- 4.2 Discord Mining Module -- 4.3 Discussion -- 5 Evaluation -- 5.1 Alternative Approaches -- 5.2 Synthetic Data -- 5.3 Real-World Transactional Data -- 6 Conclusion -- References -- AIMED-RL: Exploring Adversarial Malware Examples with Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 2.1 Reinforcement Learning -- 2.2 Further Approaches -- 3 AIMED-RL -- 3.1 Framework and Notation -- 3.2 Experimental Setting -- 3.3 Environment -- 4 Experimental Results -- 4.1 Diversity of Perturbations -- 4.2 Evasion Rate -- 5 Availability -- 6 Conclusion -- References -- Learning Explainable Representations of Malware Behavior -- 1 Introduction -- 2 Related Work -- 3 Problem Setting and Operating Environment -- 3.1 Network Events -- 3.2 Identification of Threats -- 3.3 Data Collection and Quantitative Analysis -- 4 Models -- 4.1 Architectures -- 4.2 Unsupervised Pre-training -- 5 Experiments -- 5.1 Hyperparameter Optimization -- 5.2 Malware-Classification Performance.
5.3 Indicators of Compromise -- 6 Conclusion -- References -- Strategic Mitigation Against Wireless Attacks on Autonomous Platoons -- 1 Introduction -- 1.1 Related Work -- 2 Message Falsification Attacks Against Platoons -- 2.1 Vehicular Platoon Control Policy -- 2.2 Attack Model -- 2.3 Attack Detection Algorithm -- 3 Security Game-Based Mitigation Framework -- 3.1 Numerical Example -- 4 Simulation Setup -- 5 Simulation Results and Discussion -- 5.1 Realistic Driving Scenario -- 6 Conclusion -- References -- DeFraudNet: An End-to-End Weak Supervision Framework to Detect Fraud in Online Food Delivery -- 1 Introduction -- 2 Related Work -- 3 The Framework: DeFraudNet -- 3.1 Problem Definition -- 3.2 Fraud Detection Pipeline -- 4 Data and Feature Processing -- 4.1 Dataset -- 4.2 Feature Engineering -- 5 Label Generation -- 5.1 Generating Noisy Labels Using LFs -- 5.2 Snorkel Generative Model -- 5.3 Class-Specific Autoencoders for Denoising -- 6 Discriminator Models -- 6.1 Multi Layer Perceptron -- 6.2 LSTM Sequence Model -- 7 Deployment and Serving Infrastructure -- 8 Ablation Experiments -- 8.1 Setup and Baseline -- 8.2 Experiments -- 9 Conclusion -- References -- Spatio-Temporal Data -- Time Series Forecasting with Gaussian Processes Needs Priors -- 1 Introduction -- 2 Gaussian Processes -- 2.1 Kernel Compositions -- 2.2 The Composition -- 2.3 Training Strategy -- 2.4 MAP Estimation -- 2.5 Forecasting -- 3 Experiments -- 4 Dealing with Multiple Seasonalities -- 5 Code and Replicability -- 6 Conclusions -- References -- Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Definition of MTL, TL, and Zero-Shot Learning -- 3.2 Proposed Method -- 4 Experimental Evaluation of the Task-Temporal Convolution Network. 4.1 GemanSolarFarm and EuropeWindFarm Dataset -- 4.2 Evaluation Measures -- 4.3 MTL Experiment -- 4.4 Zero-Shot Learning Experiment -- 4.5 Inductive TL Experiment -- 5 Conclusion and Future Work -- References -- Generating Multi-type Temporal Sequences to Mitigate Class-Imbalanced Problem -- 1 Introduction -- 2 Related Work -- 2.1 GAN for Sequence Data -- 2.2 RL for GANs with Sequences of Discrete Tokens -- 2.3 Gumbel-Softmax Distribution for GANs with Sequences of Discrete Tokens -- 3 Methodology -- 3.1 Definitions -- 3.2 RL and Policy Improvement to Train GAN -- 3.3 An Approximation with Gumbel-Softmax Distribution -- 4 Data Experiments -- 4.1 Synthetic Dataset -- 4.2 Evaluation Metric -- 4.3 Experiment Setup -- 4.4 Experiment Results -- 5 Conclusions -- References -- Recognizing Skeleton-Based Hand Gestures by a Spatio-Temporal Network -- 1 Introduction -- 2 Related Work -- 2.1 Hand Pose and Gesture Representation -- 2.2 Hand Gesture Recognition -- 3 Problem Formulation -- 3.1 Definition -- 3.2 Embedding Representation for Skeletal Data -- 4 Our Model -- 4.1 Spatio-Temporal Feature Encoder -- 4.2 Attention Scorer -- 4.3 Network-Based Classifier -- 5 Experiments -- 5.1 Datasets and Preprocessing -- 5.2 Experimental Set-Ups and Baselines -- 5.3 Comparison Results on Publicly-Available Datasets -- 5.4 Comparisons Results on TaiChi2021 -- 5.5 Ablation Study -- 6 Conclusion -- References -- E-commerce and Finance -- Smurf-Based Anti-money Laundering in Time-Evolving Transaction Networks -- 1 Introduction -- 2 Related Work -- 3 Dataset Description -- 4 Extraction of Smurf-Like Motifs from Transaction Graph -- 4.1 Proposed Pipeline -- 4.2 Results -- 5 Conclusion -- References -- Spatio-Temporal Multi-graph Networks for Demand Forecasting in Online Marketplaces -- 1 Introduction -- 2 Prior Work -- 3 Proposed Method -- 3.1 Problem Formulation. 3.2 Graph Construction -- 3.3 Graph Neural Networks -- 3.4 Sequential Model -- 4 Experimental Results -- 4.1 Implementation Details -- 4.2 Comparison with Baseline -- 4.3 Demand Forecasting for Multi-seller Products and Cold Start Offers -- 5 Conclusion -- References -- The Limit Order Book Recreation Model (LOBRM): An Extended Analysis -- 1 Introduction -- 2 Background and Related Work -- 2.1 The Limit Order Book (LOB) -- 2.2 Generating Synthetic LOB Data -- 3 Model Formulation -- 3.1 Motivation -- 3.2 Problem Description -- 3.3 Formalized Workflow of LOBRM -- 4 Experiment and Empirical Analysis -- 4.1 Data Preprocessing -- 4.2 Model Comparison -- 4.3 Ablation Study -- 4.4 Superiority of Sparse Encoding for TAQ -- 4.5 Is the Model Well-Trained? -- 5 Conclusion -- References -- Taking over the Stock Market: Adversarial Perturbations Against Algorithmic Traders -- 1 Introduction -- 2 Background -- 2.1 Algorithmic Trading -- 2.2 Adversarial Learning -- 3 Problem Description -- 3.1 Trading Setup -- 3.2 Threat Model -- 4 Proposed Attack -- 5 Evaluation Setup -- 5.1 Dataset -- 5.2 Feature Extraction -- 5.3 Models -- 5.4 Evaluation -- 6 White-Box Attack -- 7 Black-Box Attack -- 8 Mitigation -- 9 Conclusions -- References -- Continuous-Action Reinforcement Learning for Portfolio Allocation of a Life Insurance Company -- 1 Introduction -- 2 Problem Definition -- 2.1 Formalization -- 2.2 Implementation Details -- 2.3 Optimization Problem -- 3 Solution -- 3.1 Structural and Parametric Constraints -- 4 Experimental Evaluation -- 4.1 Three Assets Scenario. -- 4.2 Six Assets Scenario -- 5 Related Work -- 6 Conclusions -- References -- XRR: Explainable Risk Ranking for Financial Reports -- 1 Introduction -- 2 Methodology -- 2.1 Definitions and Problem Formulation -- 2.2 Post-event Return Volatility -- 2.3 Multilevel Explanation Structure. 2.4 Pairwise Deep Ranking -- 3 Experiments -- 3.1 Data Description -- 3.2 Experimental Settings -- 3.3 Pre-trained Word Embedding -- 3.4 Compared Methods -- 3.5 Experimental Results -- 3.6 Fine-Grained Analysis -- 3.7 Different Risk Measure Analysis -- 4 Discussions on Explainability -- 4.1 Financial Sentiment Terms Analysis -- 4.2 Financial Sentiment Sentences Analysis -- 5 Conclusion -- References -- Healthcare and Medical Applications (including Covid) -- Self-disclosure on Twitter During the COVID-19 Pandemic: A Network Perspective -- 1 Introduction -- 2 Dataset -- 3 Self-disclosure Measurements -- 3.1 Measurement Scale -- 3.2 Manual Annotations -- 3.3 Label Generation -- 4 Analysis -- 4.1 Self-disclosure Assortativity in Twitter Reply Networks -- 4.2 Persistent Groups and Self-disclosure -- 4.3 Characterizing Sensitive Disclosures in Temporally Persistent Social Connections -- 5 Discussion -- 6 Related Work -- 7 Conclusion -- References -- COVID Edge-Net: Automated COVID-19 Lung Lesion Edge Detection in Chest CT Images -- 1 Introduction -- 2 Related Works -- 2.1 COVID-19 Segmentation -- 2.2 Edge Detection -- 3 Methodology -- 3.1 Task Definition -- 3.2 Overview of COVID Edge-Net -- 3.3 The Edge Detection Backbone -- 3.4 Multi-scale Residual Dual Attention (MSRDA) Module -- 3.5 Canny Operator Module -- 3.6 Global Loss Function -- 4 Experiments and Discussions -- 4.1 Experimental Settings -- 4.2 Comparison with State-of-the-Arts -- 4.3 Ablation Study -- 4.4 Additional Experiments -- 5 Conclusions -- References -- Improving Ambulance Dispatching with Machine Learning and Simulation -- 1 Introduction -- 2 Related Work -- 3 The Data Set: Historic Dispatch Decisions -- 3.1 Feature Engineering -- 4 Capturing the Dispatch Policy with a Decision Tree -- 4.1 Performance Analysis of the Learned Decision Tree and Policy. 4.2 The Penalty-Based Closest-Idle Policy. |
| Record Nr. | UNISA-996464516103316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track [[electronic resource] ] : European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V / / edited by Yuxiao Dong, Georgiana Ifrim, Dunja Mladenić, Craig Saunders, Sofie Van Hoecke
| Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track [[electronic resource] ] : European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V / / edited by Yuxiao Dong, Georgiana Ifrim, Dunja Mladenić, Craig Saunders, Sofie Van Hoecke |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (XLII, 577 p. 205 illus., 181 illus. in color.) |
| Disciplina | 006.312 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Machine learning Education—Data processing Social sciences—Data processing Computer engineering Computer networks Data Mining and Knowledge Discovery Machine Learning Computers and Education Computer Application in Social and Behavioral Sciences Computer Engineering and Networks |
| ISBN | 3-030-67670-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Applied data science: recommendation -- applied data science: anomaly detection -- applied data science: Web mining -- applied data science: transportation -- applied data science: activity recognition -- applied data science: hardware and manufacturing -- applied data science: spatiotemporal data. |
| Record Nr. | UNISA-996464444203316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track : European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V / / edited by Yuxiao Dong, Georgiana Ifrim, Dunja Mladenić, Craig Saunders, Sofie Van Hoecke
| Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track : European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V / / edited by Yuxiao Dong, Georgiana Ifrim, Dunja Mladenić, Craig Saunders, Sofie Van Hoecke |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (XLII, 577 p. 205 illus., 181 illus. in color.) |
| Disciplina | 006.312 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Machine learning Education—Data processing Social sciences—Data processing Computer engineering Computer networks Data Mining and Knowledge Discovery Machine Learning Computers and Education Computer Application in Social and Behavioral Sciences Computer Engineering and Networks |
| ISBN | 3-030-67670-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Applied data science: recommendation -- applied data science: anomaly detection -- applied data science: Web mining -- applied data science: transportation -- applied data science: activity recognition -- applied data science: hardware and manufacturing -- applied data science: spatiotemporal data. |
| Record Nr. | UNINA-9910484810503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part IV / / edited by Yuxiao Dong, Nicolas Kourtellis, Barbara Hammer, Jose A. Lozano
| Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part IV / / edited by Yuxiao Dong, Nicolas Kourtellis, Barbara Hammer, Jose A. Lozano |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (579 pages) |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Computer networks Social sciences—Data processing Education—Data processing Application software Artificial Intelligence Computer Communication Networks Computer Application in Social and Behavioral Sciences Computers and Education Computer and Information Systems Applications |
| ISBN | 3-030-86514-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part IV -- Anomaly Detection and Malware -- Anomaly Detection: How to Artificially Increase Your F1-Score with a Biased Evaluation Protocol -- 1 Introduction -- 2 Related Work -- 3 Issues When Using F1-Score and AVPR Metrics -- 3.1 Formalism and Problem Statement -- 3.2 Definition of the Metrics -- 3.3 Evaluation Protocols: Theory vs Practice -- 3.4 Metrics Sensitivity to the Contamination Rate of the Test Set -- 3.5 How to Artificially Increase Your F1-Score and AVPR -- 3.6 F1-Score Cannot Compare Datasets Difficulty -- 4 Call for Action -- 4.1 Use AUC -- 4.2 Do Not Waste Anomalous Samples -- 5 Conclusion -- References -- Mining Anomalies in Subspaces of High-Dimensional Time Series for Financial Transactional Data -- 1 Introduction -- 2 Related Work -- 3 Definitions and Notation -- 4 System Architecture -- 4.1 Subspace Searching Module -- 4.2 Discord Mining Module -- 4.3 Discussion -- 5 Evaluation -- 5.1 Alternative Approaches -- 5.2 Synthetic Data -- 5.3 Real-World Transactional Data -- 6 Conclusion -- References -- AIMED-RL: Exploring Adversarial Malware Examples with Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 2.1 Reinforcement Learning -- 2.2 Further Approaches -- 3 AIMED-RL -- 3.1 Framework and Notation -- 3.2 Experimental Setting -- 3.3 Environment -- 4 Experimental Results -- 4.1 Diversity of Perturbations -- 4.2 Evasion Rate -- 5 Availability -- 6 Conclusion -- References -- Learning Explainable Representations of Malware Behavior -- 1 Introduction -- 2 Related Work -- 3 Problem Setting and Operating Environment -- 3.1 Network Events -- 3.2 Identification of Threats -- 3.3 Data Collection and Quantitative Analysis -- 4 Models -- 4.1 Architectures -- 4.2 Unsupervised Pre-training -- 5 Experiments -- 5.1 Hyperparameter Optimization -- 5.2 Malware-Classification Performance.
5.3 Indicators of Compromise -- 6 Conclusion -- References -- Strategic Mitigation Against Wireless Attacks on Autonomous Platoons -- 1 Introduction -- 1.1 Related Work -- 2 Message Falsification Attacks Against Platoons -- 2.1 Vehicular Platoon Control Policy -- 2.2 Attack Model -- 2.3 Attack Detection Algorithm -- 3 Security Game-Based Mitigation Framework -- 3.1 Numerical Example -- 4 Simulation Setup -- 5 Simulation Results and Discussion -- 5.1 Realistic Driving Scenario -- 6 Conclusion -- References -- DeFraudNet: An End-to-End Weak Supervision Framework to Detect Fraud in Online Food Delivery -- 1 Introduction -- 2 Related Work -- 3 The Framework: DeFraudNet -- 3.1 Problem Definition -- 3.2 Fraud Detection Pipeline -- 4 Data and Feature Processing -- 4.1 Dataset -- 4.2 Feature Engineering -- 5 Label Generation -- 5.1 Generating Noisy Labels Using LFs -- 5.2 Snorkel Generative Model -- 5.3 Class-Specific Autoencoders for Denoising -- 6 Discriminator Models -- 6.1 Multi Layer Perceptron -- 6.2 LSTM Sequence Model -- 7 Deployment and Serving Infrastructure -- 8 Ablation Experiments -- 8.1 Setup and Baseline -- 8.2 Experiments -- 9 Conclusion -- References -- Spatio-Temporal Data -- Time Series Forecasting with Gaussian Processes Needs Priors -- 1 Introduction -- 2 Gaussian Processes -- 2.1 Kernel Compositions -- 2.2 The Composition -- 2.3 Training Strategy -- 2.4 MAP Estimation -- 2.5 Forecasting -- 3 Experiments -- 4 Dealing with Multiple Seasonalities -- 5 Code and Replicability -- 6 Conclusions -- References -- Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Definition of MTL, TL, and Zero-Shot Learning -- 3.2 Proposed Method -- 4 Experimental Evaluation of the Task-Temporal Convolution Network. 4.1 GemanSolarFarm and EuropeWindFarm Dataset -- 4.2 Evaluation Measures -- 4.3 MTL Experiment -- 4.4 Zero-Shot Learning Experiment -- 4.5 Inductive TL Experiment -- 5 Conclusion and Future Work -- References -- Generating Multi-type Temporal Sequences to Mitigate Class-Imbalanced Problem -- 1 Introduction -- 2 Related Work -- 2.1 GAN for Sequence Data -- 2.2 RL for GANs with Sequences of Discrete Tokens -- 2.3 Gumbel-Softmax Distribution for GANs with Sequences of Discrete Tokens -- 3 Methodology -- 3.1 Definitions -- 3.2 RL and Policy Improvement to Train GAN -- 3.3 An Approximation with Gumbel-Softmax Distribution -- 4 Data Experiments -- 4.1 Synthetic Dataset -- 4.2 Evaluation Metric -- 4.3 Experiment Setup -- 4.4 Experiment Results -- 5 Conclusions -- References -- Recognizing Skeleton-Based Hand Gestures by a Spatio-Temporal Network -- 1 Introduction -- 2 Related Work -- 2.1 Hand Pose and Gesture Representation -- 2.2 Hand Gesture Recognition -- 3 Problem Formulation -- 3.1 Definition -- 3.2 Embedding Representation for Skeletal Data -- 4 Our Model -- 4.1 Spatio-Temporal Feature Encoder -- 4.2 Attention Scorer -- 4.3 Network-Based Classifier -- 5 Experiments -- 5.1 Datasets and Preprocessing -- 5.2 Experimental Set-Ups and Baselines -- 5.3 Comparison Results on Publicly-Available Datasets -- 5.4 Comparisons Results on TaiChi2021 -- 5.5 Ablation Study -- 6 Conclusion -- References -- E-commerce and Finance -- Smurf-Based Anti-money Laundering in Time-Evolving Transaction Networks -- 1 Introduction -- 2 Related Work -- 3 Dataset Description -- 4 Extraction of Smurf-Like Motifs from Transaction Graph -- 4.1 Proposed Pipeline -- 4.2 Results -- 5 Conclusion -- References -- Spatio-Temporal Multi-graph Networks for Demand Forecasting in Online Marketplaces -- 1 Introduction -- 2 Prior Work -- 3 Proposed Method -- 3.1 Problem Formulation. 3.2 Graph Construction -- 3.3 Graph Neural Networks -- 3.4 Sequential Model -- 4 Experimental Results -- 4.1 Implementation Details -- 4.2 Comparison with Baseline -- 4.3 Demand Forecasting for Multi-seller Products and Cold Start Offers -- 5 Conclusion -- References -- The Limit Order Book Recreation Model (LOBRM): An Extended Analysis -- 1 Introduction -- 2 Background and Related Work -- 2.1 The Limit Order Book (LOB) -- 2.2 Generating Synthetic LOB Data -- 3 Model Formulation -- 3.1 Motivation -- 3.2 Problem Description -- 3.3 Formalized Workflow of LOBRM -- 4 Experiment and Empirical Analysis -- 4.1 Data Preprocessing -- 4.2 Model Comparison -- 4.3 Ablation Study -- 4.4 Superiority of Sparse Encoding for TAQ -- 4.5 Is the Model Well-Trained? -- 5 Conclusion -- References -- Taking over the Stock Market: Adversarial Perturbations Against Algorithmic Traders -- 1 Introduction -- 2 Background -- 2.1 Algorithmic Trading -- 2.2 Adversarial Learning -- 3 Problem Description -- 3.1 Trading Setup -- 3.2 Threat Model -- 4 Proposed Attack -- 5 Evaluation Setup -- 5.1 Dataset -- 5.2 Feature Extraction -- 5.3 Models -- 5.4 Evaluation -- 6 White-Box Attack -- 7 Black-Box Attack -- 8 Mitigation -- 9 Conclusions -- References -- Continuous-Action Reinforcement Learning for Portfolio Allocation of a Life Insurance Company -- 1 Introduction -- 2 Problem Definition -- 2.1 Formalization -- 2.2 Implementation Details -- 2.3 Optimization Problem -- 3 Solution -- 3.1 Structural and Parametric Constraints -- 4 Experimental Evaluation -- 4.1 Three Assets Scenario. -- 4.2 Six Assets Scenario -- 5 Related Work -- 6 Conclusions -- References -- XRR: Explainable Risk Ranking for Financial Reports -- 1 Introduction -- 2 Methodology -- 2.1 Definitions and Problem Formulation -- 2.2 Post-event Return Volatility -- 2.3 Multilevel Explanation Structure. 2.4 Pairwise Deep Ranking -- 3 Experiments -- 3.1 Data Description -- 3.2 Experimental Settings -- 3.3 Pre-trained Word Embedding -- 3.4 Compared Methods -- 3.5 Experimental Results -- 3.6 Fine-Grained Analysis -- 3.7 Different Risk Measure Analysis -- 4 Discussions on Explainability -- 4.1 Financial Sentiment Terms Analysis -- 4.2 Financial Sentiment Sentences Analysis -- 5 Conclusion -- References -- Healthcare and Medical Applications (including Covid) -- Self-disclosure on Twitter During the COVID-19 Pandemic: A Network Perspective -- 1 Introduction -- 2 Dataset -- 3 Self-disclosure Measurements -- 3.1 Measurement Scale -- 3.2 Manual Annotations -- 3.3 Label Generation -- 4 Analysis -- 4.1 Self-disclosure Assortativity in Twitter Reply Networks -- 4.2 Persistent Groups and Self-disclosure -- 4.3 Characterizing Sensitive Disclosures in Temporally Persistent Social Connections -- 5 Discussion -- 6 Related Work -- 7 Conclusion -- References -- COVID Edge-Net: Automated COVID-19 Lung Lesion Edge Detection in Chest CT Images -- 1 Introduction -- 2 Related Works -- 2.1 COVID-19 Segmentation -- 2.2 Edge Detection -- 3 Methodology -- 3.1 Task Definition -- 3.2 Overview of COVID Edge-Net -- 3.3 The Edge Detection Backbone -- 3.4 Multi-scale Residual Dual Attention (MSRDA) Module -- 3.5 Canny Operator Module -- 3.6 Global Loss Function -- 4 Experiments and Discussions -- 4.1 Experimental Settings -- 4.2 Comparison with State-of-the-Arts -- 4.3 Ablation Study -- 4.4 Additional Experiments -- 5 Conclusions -- References -- Improving Ambulance Dispatching with Machine Learning and Simulation -- 1 Introduction -- 2 Related Work -- 3 The Data Set: Historic Dispatch Decisions -- 3.1 Feature Engineering -- 4 Capturing the Dispatch Policy with a Decision Tree -- 4.1 Performance Analysis of the Learned Decision Tree and Policy. 4.2 The Penalty-Based Closest-Idle Policy. |
| Record Nr. | UNINA-9910502999303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part V / / edited by Yuxiao Dong, Nicolas Kourtellis, Barbara Hammer, Jose A. Lozano
| Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part V / / edited by Yuxiao Dong, Nicolas Kourtellis, Barbara Hammer, Jose A. Lozano |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (542 pages) |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Social sciences—Data processing Computer networks Data mining Artificial Intelligence Computer Application in Social and Behavioral Sciences Computer Communication Networks Data Mining and Knowledge Discovery |
| ISBN | 3-030-86517-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part V -- Automating Machine Learning, Optimization, and Feature Engineering -- PuzzleShuffle: Undesirable Feature Learning for Semantic Shift Detection -- 1 Introduction -- 2 Related Work -- 2.1 Out-of-Distribution Detection -- 2.2 Data Augmentation -- 2.3 Uncertainty Calibration -- 3 Preliminaries -- 3.1 The Effects by Perturbation -- 3.2 Adversarial Undesirable Feature Learning -- 4 Proposed Method -- 4.1 PuzzleShuffle Augmentation -- 4.2 Adaptive Label Smoothing -- 4.3 Motivation -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Compared Methods -- 5.3 Results -- 5.4 Analysis -- 6 Conclusion -- References -- Enabling Machine Learning on the Edge Using SRAM Conserving Efficient Neural Networks Execution Approach -- 1 Introduction -- 2 Background and Related Work -- 2.1 Deep Model Compression -- 2.2 Executing Neural Networks on Microcontrollers -- 3 Efficient Neural Network Execution Approach Design -- 3.1 Tensor Memory Mapping (TMM) Method Design -- 3.2 Loading Fewer Tensors and Tensors Re-usage -- 3.3 Finding the Cheapest NN Graph Execution Sequence -- 3.4 Core Algorithm -- 4 Experimental Evaluation -- 4.1 SRAM Usage -- 4.2 Model Performance -- 4.3 Inference Time and Energy Consumption -- 5 Conclusion -- References -- AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge -- 1 Introduction -- 2 Challenge Setting -- 2.1 Phases -- 2.2 Protocol -- 2.3 Datasets -- 2.4 Metrics -- 2.5 Platform, Hardware and Limitations -- 2.6 Baseline -- 2.7 Results -- 3 Post Challenge Experiments -- 3.1 Reproducibility -- 3.2 Overfitting and Generalisation -- 3.3 Comparison to Open Source AutoML Solutions -- 3.4 Impact of Time Budget -- 3.5 Dataset Difficulty -- 4 Conclusion and Future Work -- References -- Methods for Automatic Machine-Learning Workflow Analysis -- 1 Introduction.
2 Problem Definition -- 3 Related Work -- 4 Residual Graph-Level Graph Convolutional Networks -- 5 Datasets -- 6 Workflow Similarity -- 7 Structural Performance Prediction -- 8 Component Refinement and Suggestion -- 9 Conclusion -- References -- ConCAD: Contrastive Learning-Based Cross Attention for Sleep Apnea Detection -- 1 Introduction -- 2 Related Work -- 2.1 Sleep Apnea Detection -- 2.2 Attention-Based Feature Fusion -- 2.3 Contrastive Learning -- 3 Methodology -- 3.1 Expert Feature Extraction and Data Augmentation -- 3.2 Feature Extractor -- 3.3 Cross Attention -- 3.4 Contrastive Learning. -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Compared Methods -- 4.3 Experiment Setup -- 4.4 Results and Discussions -- 5 Conclusions and Future Work -- References -- Machine Learning Based Simulations and Knowledge Discovery -- DeepPE: Emulating Parameterization in Numerical Weather Forecast Model Through Bidirectional Network -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Problem Definition -- 3.2 Deep Parameterization Emulator -- 3.3 Transfer Scheme -- 3.4 Training -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Setup -- 5 Results -- 5.1 DeepPE Performance Analysis -- 5.2 Transfer Analysis -- 6 Conclusion -- References -- Effects of Boundary Conditions in Fully Convolutional Networks for Learning Spatio-Temporal Dynamics -- 1 Introduction -- 2 Method -- 2.1 Learning an Auto-Regressive Model -- 2.2 Neural Network Convolutional Architecture -- 2.3 Boundary Condition Treatment -- 2.4 Loss Function -- 3 Applications: Time-Evolving PDEs -- 3.1 Acoustic Propagation of Gaussian Pulses -- 3.2 Diffusion of Temperature Spots -- 3.3 Datasets Generation and Parameters -- 4 Results -- 5 Conclusion -- References -- Physics Knowledge Discovery via Neural Differential Equation Embedding -- 1 Introduction -- 2 Phase-Field Model -- 3 Problem Statement. 4 Neural Differential Equation Embedding -- 5 Related Work -- 6 Experiments -- 7 Conclusion -- References -- A Bayesian Convolutional Neural Network for Robust Galaxy Ellipticity Regression -- 1 Introduction -- 2 Estimating Galaxy Ellipticity from Images -- 3 A Method to Assess Uncertainty in Ellipticity Estimation -- 3.1 Estimation of Noise Related Uncertainty -- 3.2 Estimation of Blend Related Uncertainty -- 3.3 Training Protocol -- 4 Experiments -- 4.1 Estimation of Uncertainty Related to Noise -- 4.2 Estimation of Uncertainty Related to Blending -- 5 Conclusion -- References -- Precise Weather Parameter Predictions for Target Regions via Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Pertinent Background -- 4 Learning-Based Modelets for Weather Forecasting -- 4.1 Micro Model -- 4.2 Micro-Macro Model -- 5 Experiment -- 5.1 Setting -- 5.2 Overall Performance -- 5.3 Comparing to Other Methods -- 5.4 Ablation Study -- 5.5 Abnormal Weather Forecasting -- 6 Conclusion -- References -- Action Set Based Policy Optimization for Safe Power Grid Management -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 3.1 Power Grid Management -- 3.2 Search-Based Planning -- 4 Methodology -- 4.1 Search with the Action Set -- 4.2 Policy Optimization -- 4.3 Discussion on Action Set Size -- 4.4 Algorithm Summary -- 5 Experiments -- 5.1 Experiment Setup -- 5.2 Implementation -- 5.3 Competition -- 6 Conclusion -- A Grid2Op Environment -- References -- Conditional Neural Relational Inference for Interacting Systems -- 1 Introduction -- 2 Related Work -- 3 The Conditional Neural Inference Model -- 3.1 Encoding, Establishing the Body-Part Interactions -- 3.2 Decoding, Establishing the Dynamics -- 3.3 Conditional Generation -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Results -- 5 Conclusion -- References -- Recommender Systems and Behavior Modeling. MMNet: Multi-granularity Multi-mode Network for Item-Level Share Rate Prediction -- 1 Introduction -- 2 Related Works -- 3 Preliminary -- 4 Methodology -- 4.1 Overall Framework -- 4.2 Fine-Granularity Module -- 4.3 Coarse-Granularity Module -- 4.4 Meta-info Modeling Module -- 4.5 Optimization Objectives -- 5 Online Deployment -- 6 Experiments -- 6.1 Datasets -- 6.2 Baselines and Experimental Settings -- 6.3 Offline Item-Level Share Rate Prediction -- 6.4 Online A/B Tests -- 6.5 Ablation Studies -- 6.6 Parameter Analyses -- 7 Conclusion and Future Work -- References -- The Joy of Dressing Is an Art: Outfit Generation Using Self-attention Bi-LSTM -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Bayesian Personalized Ranking (MF) Embedding -- 3.2 Training Dataset Generation -- 3.3 Bi-LSTM -- 3.4 Self-attention Bi-LSTM -- 3.5 Generation of New Outfits -- 4 Results -- 5 Conclusion -- References -- On Inferring a Meaningful Similarity Metric for Customer Behaviour -- 1 Introduction -- 2 Problem Definition -- 3 SIMPRIM Framework -- 3.1 Journey Log to Journey Profiles -- 3.2 Measuring Similarity -- 3.3 Dimensionality Reduction -- 3.4 Co-learning of Metric Weights and Journey Clustering -- 3.5 Evaluation -- 4 Experimental Evaluation -- 4.1 Customer Service Process at Anonycomm -- 4.2 BPIC 2012 Real Dataset -- 5 Related Work -- 6 Conclusion -- References -- Quantifying Explanations of Neural Networks in E-Commerce Based on LRP -- 1 Introduction -- 2 Preliminaries -- 3 Formal Model of an Online Shop -- 4 Explanation Approach -- 4.1 Explanation via Layer-Wise Relevance Propagation -- 4.2 Input Analysis with Leave-One-Out Method -- 4.3 Explanation Quantity Measures -- 5 Evaluation -- 5.1 Evaluation Setting -- 5.2 Evaluation Data Set -- 5.3 Evaluation Results -- 6 Conclusion -- References -- Natural Language Processing. Balancing Speed and Accuracy in Neural-Enhanced Phonetic Name Matching -- 1 Introduction -- 1.1 Challenges -- 2 Related Work -- 3 Phonetic Name Matching Systems -- 3.1 Neural Name Transliteration -- 3.2 Neural Name Matching -- 4 Experimental Results -- 4.1 Training and Hyperparameters -- 4.2 Results -- 5 Conclusion and Future Work -- References -- Robust Learning for Text Classification with Multi-source Noise Simulation and Hard Example Mining -- 1 Introduction -- 2 Related Work -- 2.1 Noise Reduction -- 2.2 Adversarial Training -- 2.3 Training with Noisy Data -- 3 Problem -- 3.1 Notation -- 3.2 Text Classification -- 3.3 A Practical Scenario -- 3.4 OCR Noise Simulation -- 3.5 Robust Training -- 4 Approach -- 4.1 OCR Noise Simulation -- 4.2 Noise Invariance Representation -- 4.3 Hard Example Mining -- 4.4 The Overall Framework -- 5 Experiment -- 5.1 Dataset -- 5.2 Implementation -- 5.3 Results -- 6 Analysis -- 6.1 Naive Training with a Single Noise Simulation Method -- 6.2 The Impact of Different Noise Level -- 6.3 The Impact of Hard Example Mining -- 6.4 The Impact of Stability Loss -- 7 Conclusion -- References -- Topic-to-Essay Generation with Comprehensive Knowledge Enhancement -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Task Formulation -- 3.2 Model Description -- 3.3 Training and Inference -- 4 Experiments -- 4.1 Datasets -- 4.2 Settings -- 4.3 Baselines -- 4.4 Evaluation Metrics -- 4.5 Experimental Results -- 4.6 Validity of Knowledge Transfer -- 4.7 Case Study -- 5 Conclusion -- References -- Analyzing Research Trends in Inorganic Materials Literature Using NLP -- 1 Introduction -- 2 Related Work -- 3 Corpus Preparation -- 3.1 Definition of Types -- 3.2 Collecting Literature -- 3.3 Annotation -- 4 Approach -- 4.1 Sequence Labeling Architecture -- 4.2 Numeric Normalization -- 5 Results -- 5.1 Inter-Annotator Agreement. 5.2 Comparing Language Models. |
| Record Nr. | UNINA-9910502985703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||