Explainable Recommendation
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Explainable Recommendation
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Author : Yongfeng Zhang
language : en
Publisher:
Release Date : 2020-03-10
Explainable Recommendation written by Yongfeng Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-10 with Computers categories.
In recent years, a large number of explainable recommendation approaches have been proposed and applied in real-world systems. This survey provides a comprehensive review of the explainable recommendation research.
Explainable Recommendation
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Author : YONGFENG ZHANG; XU CHEN.
language : en
Publisher:
Release Date : 2020
Explainable Recommendation written by YONGFENG ZHANG; XU CHEN. and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
In recent years, a large number of explainable recommendation approaches have been proposed and applied in real-world systems. This survey provides a comprehensive review of the explainable recommendation research.
Everything Explained That Is Explainable
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Author : Denis Boyles
language : en
Publisher: Vintage
Release Date : 2016-06-07
Everything Explained That Is Explainable written by Denis Boyles and has been published by Vintage this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-07 with History categories.
Everything Explained That Is Explainable is the audacious, utterly improbable story of the publication of the Eleventh Edition of the legendary Encyclopædia Britannica. It is the tale of a young American entrepreneur who rescued a dying publication with the help of a floundering newspaper, and in so doing produced a series of books that forever changed the face of publishing. Thanks to the efforts of 1,500 contributors, among them a young staff of university graduates as well as some of the most distinguished names of the day, the Eleventh Edition combined scholarship and readability in a way no previous encyclopedia had (or ever has again). Denis Boyles’s work of cultural history pulls back the curtain on the 44-million-word testament to the age of reason that has profoundly shaped the way we see the world.
Integrate Knowledge Graph And Attribute Attention Mechanism To Achieve Explainable Recommendation System
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Author : 林怡瑄
language : en
Publisher:
Release Date : 2020
Integrate Knowledge Graph And Attribute Attention Mechanism To Achieve Explainable Recommendation System written by 林怡瑄 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Dual Learning
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Author : Tao Qin
language : en
Publisher: Springer Nature
Release Date : 2020-11-13
Dual Learning written by Tao Qin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-13 with Computers categories.
Many AI (and machine learning) tasks present in dual forms, e.g., English-to-Chinese translation vs. Chinese-to-English translation, speech recognition vs. speech synthesis,question answering vs. question generation, and image classification vs. image generation. Dual learning is a new learning framework that leverages the primal-dual structure of AI tasks to obtain effective feedback or regularization signals in order to enhance the learning/inference process. Since it was first introduced four years ago, the concept has attracted considerable attention in multiple fields, and been proven effective in numerous applications, such as machine translation, image-to-image translation, speech synthesis and recognition, (visual) question answering and generation, image captioning and generation, and code summarization and generation. Offering a systematic and comprehensive overview of dual learning, this book enables interested researchers (both established and newcomers) and practitioners to gain a better understanding of the state of the art in the field. It also provides suggestions for further reading and tools to help readers advance the area. The book is divided into five parts. The first part gives a brief introduction to machine learning and deep learning. The second part introduces the algorithms based on the dual reconstruction principle using machine translation, image translation, speech processing and other NLP/CV tasks as the demo applications. It covers algorithms, such as dual semi-supervised learning, dual unsupervised learning and multi-agent dual learning. In the context of image translation, it introduces algorithms including CycleGAN, DualGAN, DiscoGAN cdGAN and more recent techniques/applications. The third part presents various work based on the probability principle, including dual supervised learning and dual inference based on the joint-probability principle and dual semi-supervised learning based on the marginal-probability principle. The fourth part reviews various theoretical studies on dual learning and discusses its connections to other learning paradigms. The fifth part provides a summary and suggests future research directions.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Artificial intelligence categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Recommender Systems
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Author : Dongsheng Li
language : en
Publisher: Springer Nature
Release Date :
Recommender Systems written by Dongsheng Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Xxai Beyond Explainable Ai
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Author : Andreas Holzinger
language : en
Publisher: Springer Nature
Release Date : 2022
Xxai Beyond Explainable Ai written by Andreas Holzinger and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Artificial intelligence categories.
This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.
Database Systems For Advanced Applications
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Author : Arnab Bhattacharya
language : en
Publisher: Springer Nature
Release Date : 2022-04-26
Database Systems For Advanced Applications written by Arnab Bhattacharya and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-26 with Computers categories.
The three-volume set LNCS 13245, 13246 and 13247 constitutes the proceedings of the 26th International Conference on Database Systems for Advanced Applications, DASFAA 2022, held online, in April 2021. The total of 72 full papers, along with 76 short papers, are presented in this three-volume set was carefully reviewed and selected from 543 submissions. Additionally, 13 industrial papers, 9 demo papers and 2 PhD consortium papers are included. The conference was planned to take place in Hyderabad, India, but it was held virtually due to the COVID-19 pandemic.
Explainable Interpretable And Transparent Ai Systems
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Author : B. K. Tripathy
language : en
Publisher: CRC Press
Release Date : 2024-08-23
Explainable Interpretable And Transparent Ai Systems written by B. K. Tripathy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-23 with Technology & Engineering categories.
Transparent Artificial Intelligence (AI) systems facilitate understanding of the decision-making process and provide opportunities in various aspects of explaining AI models. This book provides up-to-date information on the latest advancements in the field of explainable AI, which is a critical requirement of AI, Machine Learning (ML), and Deep Learning (DL) models. It provides examples, case studies, latest techniques, and applications from domains such as healthcare, finance, and network security. It also covers open-source interpretable tool kits so that practitioners can use them in their domains. Features: Presents a clear focus on the application of explainable AI systems while tackling important issues of “interpretability” and “transparency”. Reviews adept handling with respect to existing software and evaluation issues of interpretability. Provides insights into simple interpretable models such as decision trees, decision rules, and linear regression. Focuses on interpreting black box models like feature importance and accumulated local effects. Discusses capabilities of explainability and interpretability. This book is aimed at graduate students and professionals in computer engineering and networking communications.