Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches, Lepore
Автор: Reyes Mauricio, Henriques Abreu Pedro, Cardoso Jaime Название: Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data: 4th Internat ISBN: 3030874435 ISBN-13(EAN): 9783030874438 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: iMIMIC 2021 Workshop.- Interpretable Deep Learning for Surgical Tool Management.- Soft Attention Improves Skin Cancer Classification Performance.- Deep Gradient based on Collective Arti cial Intelligence for AD Diagnosis and Prognosis.- This explains That: Congruent Image-Report Generation for Explainable Medical Image Analysis with Cyclic Generative Adversarial Networks.- Visual Explanation by Unifying Adversarial Generation and Feature Importance Attributions.- The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data.- Voxel-level Importance Maps for Interpretable Brain Age Estimation.- TDA4MedicalData Workshop.- Lattice Paths for Persistent Diagrams.- Neighborhood complex based machine learning (NCML) models for drug design.- Predictive modelling of highly multiplexed tumour tissue images by graph neural networks.- Statistical modeling of pulmonary vasculatures with topological priors in CT volumes.- Topological Detection of Alzheimer's Disease using Betti Curves.
Автор: Reyes Название: Interpretability of Machine Intelligence in Medical Image Computing ISBN: 3031179757 ISBN-13(EAN): 9783031179754 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed joint proceedings of the 5th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2022, held in September 2022, in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022. The 10 full papers presented at iMIMIC 2022 were carefully reviewed and selected from 24 submissions each. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention.
Автор: Nandi Anirban, Pal Aditya Kumar Название: Interpreting Machine Learning Models: Learn Model Interpretability and Explainability Methods ISBN: 1484278011 ISBN-13(EAN): 9781484278017 Издательство: Неизвестно Рейтинг: Цена: 55170.00 T Наличие на складе: Невозможна поставка. Описание: Chapter 1: Introduction to Machine Learning DomainChapter Goal: The book's opening chapter will talk about the journey of machine learning models and why model interpretability became so important in the recent times. This chapter will also cover some of the basic black box modelling algorithms in brief Sub-Topics: - Journey of machine learning domain- Journey of machine learning algorithms - Why only reporting accuracy is not enough for models Chapter 2: Introduction to Model InterpretabilityChapter Goal: This chapter will talk about the importance and need of interpretability and how businesses employ model interpretability for their decisionsSub-Topics: - Why is interpretability needed for machine learning models- Motivation behind using model interpretability- Understand social and commercial motivations for machine learning interpretability, fairness, accountability, and transparency- Get a definition of interpretability and learn about the groups leading interpretability research Chapter 3: Machine Learning Interpretability TaxonomyChapter Goal: A machine learning taxonomy is presented in this section. This will be used to characterize the interpretability of various popular machine learning techniques.Sub topics: - Understanding and trust- A scale for interpretability- Global and local interpretability- Model-agnostic and model-specific interpretability Chapter 4: Common Properties of Explanations Generated by Interpretability MethodsChapter goal: The purpose of this chapter to explain readers about evaluation metrics for various interpretability methods. This will help readers understand which methods to choose for specific use cases Sub topics: - Degree of importance - Stability- Consistency - Certainty- Novelty Chapter 5: Timeline of Model interpretability Methods DiscoveryChapter goal: This chapter will talk about the timeline and will give details about when most common methods of interpretability were discovered Chapter 6: Unified Framework for Model ExplanationsChapter goal: Each method is determined by three choices: how it handles features, what model behavior it analyzes, and how it summarizes feature influence. The chapter will focus in detail about each step and will try to map different methods to each step by giving detailed examplesSub topics1: - Removal based explanations- Summarization based explanations Chapter 7: Different Types of Removal Based ExplanationsChapter goal: This chapter will talk about the different types of removal based methods and how to implement them along with details of examples and Python packages, real life use cases etc.Sub topics: - IME(2009)- IME(2010)- QII- SHAP- KernelSHAP- TreeSHAP- LossSHAP- SAGE- Shapley- Shapley- Permutation- Conditional- Feature- Univariate- L2X- INVASE- LIME- LIME- PredDiff- Occlusion- CXPlain- RISE- MM- MIR- MP- EP- FIDO-CA Chapter 8: Different Types of Summarization Based ExplanationsChapter goal: This chapter will talk about the different types of summarization based methods and how to implement them along with details of examples and python p
Автор: Meyes Название: Transparency and Interpretability for Learned Representations of Artificial Neural Networks ISBN: 365840003X ISBN-13(EAN): 9783658400033 Издательство: Springer Рейтинг: Цена: 74530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Artificial intelligence (AI) is a concept, whose meaning and perception has changed considerably over the last decades. Starting off with individual and purely theoretical research efforts in the 1950s, AI has grown into a fully developed research field of modern times and may arguably emerge as one of the most important technological advancements of mankind. Despite these rapid technological advancements, some key questions revolving around the matter of transparency, interpretability and explainability of an AI’s decision-making remain unanswered. Thus, a young research field coined with the general term Explainable AI (XAI) has emerged from increasingly strict requirements for AI to be used in safety critical or ethically sensitive domains. An important research branch of XAI is to develop methods that help to facilitate a deeper understanding for the learned knowledge of artificial neural systems. In this book, a series of scientific studies are presented that shed light on how to adopt an empirical neuroscience inspired approach to investigate a neural network’s learned representation in the same spirit as neuroscientific studies of the brain.
Автор: Dehmer Название: Statistical and Machine Learning Approaches for Network Analysis ISBN: 0470195150 ISBN-13(EAN): 9780470195154 Издательство: Wiley Рейтинг: Цена: 116110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: * Provides a general framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for graph classification. * The proposed methods are applied to different real data sets to demonstrate their ability.
Автор: Sugiyama, Masashi Название: Statistical Reinforcement Learning ISBN: 0367575868 ISBN-13(EAN): 9780367575861 Издательство: Taylor&Francis Рейтинг: Цена: 45930.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Yixin Chen; Jia Li; James Z. Wang Название: Machine Learning and Statistical Modeling Approaches to Image Retrieval ISBN: 1475779305 ISBN-13(EAN): 9781475779301 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Machine Learning and Statistical Modeling Approaches to Image Retrieval describes several approaches of integrating machine learning and statistical modeling into an image retrieval and indexing system that demonstrates promising results.
Автор: Sugiyama Название: Statistical Reinforcement Learning ISBN: 1439856893 ISBN-13(EAN): 9781439856895 Издательство: Taylor&Francis Рейтинг: Цена: 86760.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data.
Supplying an up-to-date and accessible introduction to the field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches, including model-based and model-free approaches, policy iteration, and policy search methods.
Covers the range of reinforcement learning algorithms from a modern perspective
Lays out the associated optimization problems for each reinforcement learning scenario covered
Provides thought-provoking statistical treatment of reinforcement learning algorithms
The book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining/machine learning researchers. It presents state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques.
This book is an ideal resource for graduate-level students in computer science and applied statistics programs, as well as researchers and engineers in related fields.
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