Explainable and interpretable models in computer vision and machine learning. (2018)

• Record Type: Book
• Further Information: Note: Hugo Jair Escalante, Sergio Escalera, Isabelle Guyon, Xavier Baró, Yağmur Güçlütürk, Umut Güçlü, Marcel van Gerven, editors.
• Editors: Escalante, Hugo Jair; Escalera, Sergio; Guyon, Isabelle; Baró, Xavier; Güçlütürk, Yağmur; Güçlü, Umut; Gerven, Marcel van
• Contents: Intro; Foreword; Preface; Acknowledgements; Contents; Contributors; Part I Notions and Concepts on Explainability and Interpretability; Considerations for Evaluation and Generalization in Interpretable Machine Learning; 1 Introduction; 2 Defining Interpretability; 3 Defining the Interpretability Need; 4 Evaluation; 5 Considerations for Generalization; 6 Conclusion: Recommendations for Researchers; References; Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges; 1 Introduction; 1.1 The Components of Explainability; 1.2 Users and Laws; 1.3 Explanation and DNNs 2 Users and Their Concerns2.1 Case Study: Autonomous Driving; 3 Laws and Regulations; 4 Explanation; 5 Explanation Methods; 5.1 Desirable Properties of Explainers; 5.2 A Taxonomy for Explanation Methods; 5.2.1 Rule-Extraction Methods; 5.2.2 Attribution Methods; 5.2.3 Intrinsic Methods; 6 Addressing General Concerns; 7 Discussion; References; Part II Explainability and Interpretability in Machine Learning; Learning Functional Causal Models with Generative NeuralNetworks; 1 Introduction; 2 Problem Setting; 2.1 Notations; 2.2 Assumptions and Properties; 3 State of the Art; 3.1 Learning the CPDAG 3.1.1 Constraint-Based Methods3.1.2 Score-Based Methods; 3.1.3 Hybrid Algorithms; 3.2 Exploiting Asymmetry Between Cause and Effect; 3.2.1 The Intuition; 3.2.2 Restriction on the Class of Causal Mechanisms Considered; 3.2.3 Pairwise Methods; 3.3 Discussion; 4 Causal Generative Neural Networks; 4.1 Modeling Continuous FCMs with Generative Neural Networks; 4.1.1 Generative Model and Interventions; 4.2 Model Evaluation; 4.2.1 Scoring Metric; 4.2.2 Representational Power of CGNN; 4.3 Model Optimization; 4.3.1 Parametric (Weight) Optimization; 4.3.2 Non-parametric (Structure) Optimization 4.3.3 Identifiability of CGNN up to Markov Equivalence Classes5 Experiments; 5.1 Experimental Setting; 5.2 Learning Bivariate Causal Structures; 5.2.1 Benchmarks; 5.2.2 Baseline Approaches; 5.2.3 Hyper-Parameter Selection; 5.2.4 Empirical Results; 5.3 Identifying v-structures; 5.4 Multivariate Causal Modeling Under Causal Sufficiency Assumption; 5.4.1 Results on Artificial Graphs with Additive and Multiplicative Noises; 5.4.2 Result on Biological Data; 5.4.3 Results on Biological Real-World Data; 6 Towards Predicting Confounding Effects; 6.1 Principle; 6.2 Experimental Validation 6.2.1 Benchmarks6.2.2 Baselines; 6.2.3 Results; 7 Discussion and Perspectives; Appendix; The Maximum Mean Discrepancy (MMD) Statistic; Proofs; Table of Scores for the Experiments on Cause-Effect Pairs; Table of Scores for the Experiments on Graphs; References; Learning Interpretable Rules for Multi-Label Classification; 1 Introduction; 2 Multi-Label Classification; 2.1 Problem Definition; 2.2 Dependencies in Multi-Label Classification; 2.3 Evaluation of Multi-Label Predictions; 2.3.1 Bipartition Evaluation Functions; 2.3.2 Multi-Label Evaluation Functions; 2.3.3 Aggregation and Averaging
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2018
• Extent: 1 online resource (xvii, 299 pages), illustrations (some color)
• Subjects: 006.3/1; Machine learning; Computer vision; Artificial Intelligence; Image Processing and Computer Vision; Pattern Recognition; Electronic books
• Languages: English
• ISBNs: 9783319981314; 3319981315
• Related ISBNs: 9783319981307
• Notes: Note: Online resource; title from PDF title page (SpringerLink, viewed December 11, 2018).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.