Dell'incertezza : tre meditazioni filosofiche / Salvatore Veca

• Record Nr.: UNISALENTO991001051709707536
• Autore: Veca, Salvatore
• Pubbl/distr/stampa: Milano : Feltrinelli, 1997
• ISBN: 8807102145
• Descrizione fisica: 415 p. ; 22 cm.
• Collana: Campi del sapere [Feltrinelli]
• Soggetti: Filosofia morale; Filosofia politica
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Machine Learning and Knowledge Discovery in Databases : Research Track / / edited by Danai Koutra [and four others]

• Record Nr.: UNISA996550555203316
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023]; ©2023
• ISBN: 3-031-43415-3
• Edizione: [First edition.]
• Descrizione fisica: 1 online resource (758 pages)
• Collana: Lecture Notes in Computer Science Series ; ; Volume 14170
• Disciplina: 006.31
• Soggetti: Data mining; Databases; Machine learning
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Invited Talks Abstracts -- Neural ; Wave Representations -- Physics-Inspired Graph Neural Networks -- Mapping Generative AI -- Contents - Part II -- Computer Vision -- Sample Prior Guided Robust Model Learning to Suppress Noisy Labels -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Prior Guided Sample Dividing -- 3.2 Denoising with the Divided Sets -- 4 Experiment -- 4.1 Datasets and Implementation Details -- 4.2 Comparison with State-of-the-Art Methods -- 4.3 Ablation Study -- 4.4 Generalization to Instance-Dependent Label Noise -- 4.5 Hyper-parameters Analysis -- 4.6 Discussion for Prior Generation Module -- 5 Limitations -- 6 Conclusions -- References -- DCID: Deep Canonical Information Decomposition -- 1 Introduction -- 2 Related Work -- 2.1 Canonical Correlation Analysis (CCA) -- 2.2 Multi-Task Learning (MTL) -- 3 Univariate Shared Information Retrieval -- 3.1 Problem Setting -- 3.2 Evaluating the Shared Representations -- 4 Method: Deep Canonical Information Decomposition -- 4.1 Limitations of the CCA Setting -- 4.2 Deep Canonical Information Decomposition (DCID) -- 5 Experiments -- 5.1 Baselines -- 5.2 Experimental Settings -- 5.3 Learning the Shared Features Z -- 5.4 Variance Explained by Z and Model Performance -- 5.5 Obesity and the Volume of Brain Regions of Interest (ROIs) -- 6 Discussion -- 6.1 Results Summary -- 6.2 Limitations -- References -- Negative Prototypes Guided Contrastive Learning for Weakly Supervised Object Detection -- 1 Introduction -- 2 Related Work -- 2.1 Weakly Supervised Object Detection -- 2.2 Contrastive Learning -- 3 Proposed Method -- 3.1 Preliminaries -- 3.2 Feature Extractor -- 3.3 Contrastive Branch -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Comparison with State-of-the-Arts -- 4.4 Qualitative Results -- 4.5 Ablation Study.; 5 Conclusion -- References -- Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks -- 1 Introduction -- 2 Related Works -- 3 Empirical Study: Pruning a Pre-trained Model for Different Tasks -- 3.1 A Dataset of Pruned Models -- 3.2 Do Similar Tasks Share More Nodes on Their Pruned Models? -- 4 Meta-Vote Pruning (MVP) -- 5 Experiments -- 5.1 Implementation Details -- 5.2 Baseline Methods -- 5.3 Main Results -- 5.4 Performance on Unseen Dataset -- 5.5 Results of MVP on Sub-tasks of Different Sizes -- 5.6 Ablation Study -- 6 Conclusion -- References -- Make a Long Image Short: Adaptive Token Length for Vision Transformers -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Token-Length Assigner -- 3.2 Resizable-ViT -- 3.3 Training Strategy -- 4 Experiments -- 4.1 Experimental Results -- 4.2 Ablation Study -- 5 Conclusions -- References -- Graph Rebasing and Joint Similarity Reconstruction for Cross-Modal Hash Retrieval -- 1 Introduction -- 2 Related Work -- 2.1 Supervised Cross-Modal Hashing Methods -- 2.2 Unsupervised Cross-Modal Hashing Methods -- 3 Methodology -- 3.1 Local Relation Graph Building -- 3.2 Graph Rebasing -- 3.3 Global Relation Graph Construction -- 3.4 Joint Similarity Reconstruction -- 3.5 Training Objectives -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Performance Comparison -- 4.4 Parameter Sensitivity Experiments -- 4.5 Ablation Experiments -- 5 Conclusion -- References -- ARConvL: Adaptive Region-Based Convolutional Learning for Multi-class Imbalance Classification -- 1 Introduction -- 2 Related Work -- 2.1 Multi-class Imbalance Learning -- 2.2 Loss Modification Based Methods -- 2.3 Convolutional Prototype Learning -- 3 ARConvL -- 3.1 Overview of ARConvL -- 3.2 Region Learning Module -- 3.3 Optimizing Class-Wise Latent Feature Distribution.; 3.4 Enlarging Margin Between Classes -- 4 Experimental Studies -- 4.1 Experimental Setup -- 4.2 Performance Comparison -- 4.3 ; Performance Deterioration with Increasing Imbalance Levels -- 4.4 Effect of Each Adaptive Component of ARConvL -- 4.5 Utility in Large-Scale Datasets -- 5 Conclusion -- References -- Deep Learning -- Binary Domain Generalization for Sparsifying Binary Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Preliminaries -- 3.2 Sparse Binary Neural Network (SBNN) Formulation -- 3.3 Weight Optimization -- 3.4 Network Training -- 3.5 Implementation Gains -- 4 Experiments and Results -- 4.1 Ablation Studies -- 4.2 Benchmark -- 5 Conclusions -- References -- Efficient Hyperdimensional Computing -- 1 Introduction -- 2 Background -- 3 High Dimensions Are Not Necessary -- 3.1 Dimension-Accuracy Analysis -- 3.2 Low-Dimension Hypervector Training -- 4 Results -- 4.1 A Case Study of Our Technologies -- 4.2 Experimental Results -- 5 Discussion -- 5.1 Limitation of HDCs -- 5.2 Further Discussion of the Low Accuracy When d Is Low -- 6 Conclusion -- References -- Rényi Divergence Deep Mutual Learning -- 1 Introduction -- 2 Deep Mutual Learning -- 2.1 Rényi Divergence Deep Mutual Learning -- 3 Properties of RDML -- 3.1 Convergence Guarantee -- 3.2 Computational Complexity of RDML -- 4 Empirical Study -- 4.1 Experimental Setup -- 4.2 Convergence Trace Analysis -- 4.3 Evaluation Results -- 4.4 Generalization Results -- 5 Related Work -- 6 Conclusion -- References -- Is My Neural Net Driven by the MDL Principle? -- 1 Introduction -- 2 Related Work -- 3 MDL Principle, Signal, and Noise -- 3.1 Information Theory Primer -- 3.2 Signal and Noise -- 4 Learning with the MDL Principle -- 4.1 MDL Objective -- 4.2 Local Formulation -- 4.3 Combining Local Objectives to Obtain a Spectral Distribution -- 4.4 The MDL Spectral Distributions.; 5 Experimental Results -- 5.1 Experimental Noise -- 5.2 Discussion -- 6 Conclusion and Future Work -- References -- Scoring Rule Nets: Beyond Mean Target Prediction in Multivariate Regression -- 1 Introduction -- 2 Distributional Regression -- 2.1 Proper Scoring Rules -- 2.2 Conditional CRPS -- 2.3 CCRPS as ANN Loss Function for Multivariate Gaussian Mixtures -- 2.4 Energy Score Ensemble Models -- 3 Experiments -- 3.1 Evaluation Metrics -- 3.2 Synthetic Experiments -- 3.3 Real World Experiments -- 4 Conclusion -- References -- Learning Distinct Features Helps, Provably -- 1 Introduction -- 2 Preliminaries -- 3 Learning Distinct Features Helps -- 4 Extensions -- 4.1 Binary Classification -- 4.2 Multi-layer Networks -- 4.3 Multiple Outputs -- 5 Discussion and Open Problems -- References -- Continuous Depth Recurrent Neural Differential Equations -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Problem Definition -- 3.2 Gated Recurrent Unit -- 3.3 Recurrent Neural Ordinary Differential Equations -- 4 Continuous Depth Recurrent Neural Differential Equations -- 4.1 CDR-NDE Based on Heat Equation -- 5 Experiments -- 5.1 Baselines -- 5.2 Person Activity Recognition with Irregularly Sampled Time-Series -- 5.3 Walker2d Kinematic Simulation -- 5.4 Stance Classification -- 6 Conclusion and Future Work -- References -- Fairness -- Mitigating Algorithmic Bias with Limited Annotations -- 1 Introduction -- 2 Preliminaries -- 2.1 Notation and Problem Definition -- 2.2 Fairness Evaluation Metrics -- 3 Active Penalization Of Discrimination -- 3.1 Penalization Of Discrimination (POD) -- 3.2 Active Instance Selection (AIS) -- 3.3 The APOD Algorithm -- 3.4 Theoretical Analysis -- 4 Experiment -- 4.1 Bias Mitigation Performance Analysis (RQ1) -- 4.2 Annotation Effectiveness Analysis (RQ2) -- 4.3 Annotation Ratio Analysis (RQ3) -- 4.4 Ablation Study (RQ4).; 4.5 Visualization of Annotated Instances -- 5 Conclusion -- References -- FG2AN: Fairness-Aware Graph Generative Adversarial Networks -- 1 Introduction -- 2 Related Work -- 2.1 Graph Generative Model -- 2.2 ; Fairness on Graphs -- 3 Notation and Background -- 3.1 Notation -- 3.2 Root Causes of Representational Discrepancies -- 4 Methodology -- 4.1 Mitigating Degree-Related Bias -- 4.2 Mitigating Connectivity-Related Bias -- 4.3 FG2AN Assembling -- 4.4 Fairness Definitions for Graph Generation -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Experimental Results -- 6 Conclusion -- References -- Targeting the Source: Selective Data Curation for Debiasing NLP Models -- 1 Introduction -- 2 Three Sources of Bias in Text Encoders -- 2.1 Bias in Likelihoods -- 2.2 Bias in Attentions -- 2.3 Bias in Representations -- 3 Pipeline for Measuring Bias in Text -- 3.1 Masking -- 3.2 Probing -- 3.3 Aggregation and Normalization -- 3.4 Bias Computation -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Question Answering -- 4.3 Sentence Inference -- 4.4 Sentiment Analysis -- 5 Related Work -- 5.1 Bias Quantification -- 5.2 Bias Reduction -- 6 Conclusion -- 7 Ethical Considerations -- References -- Fairness in Multi-Task Learning via Wasserstein Barycenters -- 1 Introduction -- 2 Problem Statement -- 2.1 Multi-task Learning -- 2.2 Demographic Parity -- 3 Wasserstein Fair Multi-task Predictor -- 4 Plug-In Estimator -- 4.1 Data-Driven Approach -- 4.2 Empirical Multi-task -- 5 Numerical Evaluation -- 5.1 Datasets -- 5.2 Methods -- 5.3 Results -- 6 Conclusion -- References -- REST: Enhancing Group Robustness in DNNs Through Reweighted Sparse Training -- 1 Introduction -- 2 Related Work -- 2.1 Sparse Neural Network Training -- 2.2 Debiasing Frameworks -- 3 Methodology -- 3.1 Sparse Training -- 4 Experiments -- 4.1 Baselines -- 4.2 Datasets -- 4.3 Setup.; 4.4 Computational Costs.

Advancing the science of climate change / / National Research Council of the National Academies

• Record Nr.: UNINA9910955389903321
• Pubbl/distr/stampa: Washington, D.C., : National Academies Press, 2010
• ISBN: 9786612948497; 9780309155922; 0309155924; 9781282948495; 1282948490; 9780309145893; 0309145899
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (525 p.)
• Collana: America's climate choices Advancing the science of climate change
• Disciplina: 363.73874
• Soggetti: Climatic changes - Research - United States; Climatic changes - United States
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "America's Climate Choices: Panel on Advancing the Science of Climate Change, Board on Atmospheric Sciences and Climate, Division on Earth and Life Studies."
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: ""Front Matter""; ""Foreword: About America's Climate Choices""; ""Preface""; ""Acknowledgments""; ""Contents""; ""Summary""; ""Part I""; ""1 Introduction: Science for Understanding and Responding to Climate Change""; ""2 What We Know About Climate Change and Its Interactions with People and Ecosystems""; ""3 A New Era of Climate Change Research""; ""4 Integrative Themes for Climate Change Research""; ""5 Recommendations for Meeting the Challenge of Climate Change Research""; ""Part II: Technical Chapters""; ""6 Changes in the Climate System""; ""7 Sea Level Rise and the Coastal Environment""; ""8 Freshwater Resources""""9 Ecosystems, Ecosystem Services, and Biodiversity""; ""10 Agriculture, Fisheries, and Food Production""; ""11 Public Health""; ""12 Cities and the Built Environment""; ""13 Transportation""; ""14 Energy Supply and Use""; ""15 Solar Radiation Management""; ""16 National and Human Security""; ""17 Designing, ; Implementing, and Evaluating Climate Policies""; ""References""; ""Appendix A: America's Climate Choices: Membership Lists""; ""Appendix B: Panel on Advancing the Science of Climate Change: Statement of Task""; ""Appendix C: Panel on Advancing the Science of Climate Change: Biographical Sketches""""Appendix D: Uncertainty Terminology""; ""Appendix E: The United States Global Change Research Program""; ""Appendix F: Geoengineering Options to Respond to Climate Change: Steps to Establish a Research Agenda""; ""Appendix G: Acronyms and Initialisms""
• Sommario/riassunto: "Climate change is occurring, is caused largely by human activities, and poses significant risks for--and in many cases is already affecting--a broad range of human and natural systems. The compelling case for these conclusions is provided in Advancing the Science of Climate Change, part of a congressionally requested suite of studies known as America's Climate Choices. While noting that there is always more to learn and that the scientific process is never closed, the book shows that hypotheses about climate change are supported by multiple lines of evidence and have stood firm in the face of serious debate and careful evaluation of alternative explanations. As decision makers respond to these risks, the nation's scientific enterprise can contribute through research that improves understanding of the causes and consequences of climate change and also is useful to decision makers at the local, regional, national, and international levels. The book identifies decisions being made in 12 sectors, ranging from agriculture to transportation, to identify decisions being made in response to climate change. Advancing the Science of Climate Change calls for a single federal entity or program to coordinate a national, multidisciplinary research effort aimed at improving both understanding and responses to climate change. Seven cross-cutting research themes are identified to support this scientific enterprise. In addition, leaders of federal climate research should redouble efforts to deploy a comprehensive climate observing system, improve climate models and other analytical tools, invest in human capital, and improve linkages between research and decisions by forming partnerships with action-oriented programs"--Publisher's description.