Learning And Enforcing Diversity With Determinantal Point Processes

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Learning and Enforcing Diversity with Determinantal Point Processes

Author : Zelda Elaine Mariet
Publisher :
ISBN 13 :
Total Pages : 66 pages
Book Rating : 4.:/5 (953 download)

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Book Synopsis Learning and Enforcing Diversity with Determinantal Point Processes by : Zelda Elaine Mariet

Download or read book Learning and Enforcing Diversity with Determinantal Point Processes written by Zelda Elaine Mariet and published by . This book was released on 2016 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: As machine-learning techniques continue to require more data and become increasingly memory-heavy, being able to choose a subset of relevant, high-quality and diverse elements among large amounts of redundant or noisy data and parameters has become an important concern. Here, we approach this problem using Determinantal Point Processes (DPPs), probabilistic models that provide an intuitive and powerful way of balancing quality and diversity in sets of items. We introduce a novel, fixed-point algorithm for estimating the maximum likelihood parameters of a DPP, provide proof of convergence and discuss generalizations of this technique. We then apply DPPs to the difficult problem of detecting and eliminating redundancy in fully-connected layers of neural networks. By placing a DPP over a layer, we are able to sample a subset of neurons that perform non-overlapping computations and merge all other neurons of the layer into the previous diverse subset. This allows us to significantly reduce the size of the neural network while simultaneously maintaining a good performance.

Computer Vision – ECCV 2016

Author : Bastian Leibe
Publisher : Springer
ISBN 13 : 3319464876
Total Pages : 909 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Computer Vision – ECCV 2016 by : Bastian Leibe

Download or read book Computer Vision – ECCV 2016 written by Bastian Leibe and published by Springer. This book was released on 2016-09-16 with total page 909 pages. Available in PDF, EPUB and Kindle. Book excerpt: The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physics-based vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action activity and tracking; 3D; and 9 poster sessions.

Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning

Author : Igor V. Tetko
Publisher : Springer Nature
ISBN 13 : 3030304841
Total Pages : 807 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning by : Igor V. Tetko

Download or read book Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning written by Igor V. Tetko and published by Springer Nature. This book was released on 2019-09-09 with total page 807 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.

Computer Vision – ECCV 2018

Author : Vittorio Ferrari
Publisher : Springer
ISBN 13 : 3030012379
Total Pages : 880 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Computer Vision – ECCV 2018 by : Vittorio Ferrari

Download or read book Computer Vision – ECCV 2018 written by Vittorio Ferrari and published by Springer. This book was released on 2018-10-06 with total page 880 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.

Cognitive Internet of Things: Frameworks, Tools and Applications

Author : Huimin Lu
Publisher : Springer
ISBN 13 : 3030049469
Total Pages : 522 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Cognitive Internet of Things: Frameworks, Tools and Applications by : Huimin Lu

Download or read book Cognitive Internet of Things: Frameworks, Tools and Applications written by Huimin Lu and published by Springer. This book was released on 2019-02-18 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides insights into the research in the fields of artificial intelligence in combination with Internet of Things (IoT) technologies. Today, the integration of artificial intelligence and IoT technologies is attracting considerable interest from both researchers and developers from academic fields and industries around the globe. It is foreseeable that the next generation of IoT research will focus on artificial intelligence/beyond artificial intelligence approaches. The rapidly growing numbers of artificial intelligence algorithms and big data solutions have significantly increased the number of potential applications for IoT technologies, but they have also created new challenges for the artificial intelligence community. This book shares the latest scientific advances in this area.

Determinantal Point Processes for Machine Learning

Author : Alex Kulesza
Publisher : Now Pub
ISBN 13 : 9781601986283
Total Pages : 178 pages
Book Rating : 4.9/5 (862 download)

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Book Synopsis Determinantal Point Processes for Machine Learning by : Alex Kulesza

Download or read book Determinantal Point Processes for Machine Learning written by Alex Kulesza and published by Now Pub. This book was released on 2012-11-29 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph provides a comprehensible introduction to DPPs, focusing on the intuitions, algorithms, and extensions that are most relevant to the machine learning community.

Computer Vision – ACCV 2020

Author : Hiroshi Ishikawa
Publisher : Springer Nature
ISBN 13 : 3030695417
Total Pages : 718 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Computer Vision – ACCV 2020 by : Hiroshi Ishikawa

Download or read book Computer Vision – ACCV 2020 written by Hiroshi Ishikawa and published by Springer Nature. This book was released on 2021-02-25 with total page 718 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.* The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; segmentation and grouping Part II: low-level vision, image processing; motion and tracking Part III: recognition and detection; optimization, statistical methods, and learning; robot vision Part IV: deep learning for computer vision, generative models for computer vision Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis Part VI: applications of computer vision; vision for X; datasets and performance analysis *The conference was held virtually.

Search Result Diversification

Author : Santos Rodrygo L T
Publisher : Now Publishers
ISBN 13 : 9781680830002
Total Pages : 108 pages
Book Rating : 4.8/5 (3 download)

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Book Synopsis Search Result Diversification by : Santos Rodrygo L T

Download or read book Search Result Diversification written by Santos Rodrygo L T and published by Now Publishers. This book was released on 2015-02-27 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: This primer reviews the published literature on search result diversification. In particular, it discusses the motivations for diversifying the search results for an ambiguous query and provides a formal definition of the search result diversification problem. In addition, it describes the most successful approaches in the literature for producing and evaluating diversity in multiple search domains.

Mathematics and Computation

Author : Avi Wigderson
Publisher : Princeton University Press
ISBN 13 : 0691189137
Total Pages : 434 pages
Book Rating : 4.6/5 (911 download)

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Book Synopsis Mathematics and Computation by : Avi Wigderson

Download or read book Mathematics and Computation written by Avi Wigderson and published by Princeton University Press. This book was released on 2019-10-29 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to computational complexity theory, its connections and interactions with mathematics, and its central role in the natural and social sciences, technology, and philosophy Mathematics and Computation provides a broad, conceptual overview of computational complexity theory—the mathematical study of efficient computation. With important practical applications to computer science and industry, computational complexity theory has evolved into a highly interdisciplinary field, with strong links to most mathematical areas and to a growing number of scientific endeavors. Avi Wigderson takes a sweeping survey of complexity theory, emphasizing the field’s insights and challenges. He explains the ideas and motivations leading to key models, notions, and results. In particular, he looks at algorithms and complexity, computations and proofs, randomness and interaction, quantum and arithmetic computation, and cryptography and learning, all as parts of a cohesive whole with numerous cross-influences. Wigderson illustrates the immense breadth of the field, its beauty and richness, and its diverse and growing interactions with other areas of mathematics. He ends with a comprehensive look at the theory of computation, its methodology and aspirations, and the unique and fundamental ways in which it has shaped and will further shape science, technology, and society. For further reading, an extensive bibliography is provided for all topics covered. Mathematics and Computation is useful for undergraduate and graduate students in mathematics, computer science, and related fields, as well as researchers and teachers in these fields. Many parts require little background, and serve as an invitation to newcomers seeking an introduction to the theory of computation. Comprehensive coverage of computational complexity theory, and beyond High-level, intuitive exposition, which brings conceptual clarity to this central and dynamic scientific discipline Historical accounts of the evolution and motivations of central concepts and models A broad view of the theory of computation's influence on science, technology, and society Extensive bibliography

Case-Based Reasoning Research and Development

Author : David W. Aha
Publisher : Springer Science & Business Media
ISBN 13 : 3540423583
Total Pages : 769 pages
Book Rating : 4.5/5 (44 download)

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Book Synopsis Case-Based Reasoning Research and Development by : David W. Aha

Download or read book Case-Based Reasoning Research and Development written by David W. Aha and published by Springer Science & Business Media. This book was released on 2001-07-18 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th International Conference on Case-Based Reasoning, ICCBR 2001, held in Vancouver, BC, Canada, in July/August 2001. The 36 revised full research papers and 14 revised full application papers presented together with 3 invited papers were carefully reviewed and selected from 81 submissions. The papers address all current foundational and theoretical aspects of case-based reasoning as well as advanced applications in a variety of fields.

Interpretable Machine Learning

Author : Christoph Molnar
Publisher : Lulu.com
ISBN 13 : 0244768528
Total Pages : 320 pages
Book Rating : 4.2/5 (447 download)

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Book Synopsis Interpretable Machine Learning by : Christoph Molnar

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Federated Learning

Author : Qiang Yang
Publisher : Springer Nature
ISBN 13 : 3030630765
Total Pages : 291 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Federated Learning by : Qiang Yang

Download or read book Federated Learning written by Qiang Yang and published by Springer Nature. This book was released on 2020-11-25 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”

Algebraic Statistics for Computational Biology

Author : L. Pachter
Publisher : Cambridge University Press
ISBN 13 : 9780521857000
Total Pages : 440 pages
Book Rating : 4.8/5 (57 download)

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Book Synopsis Algebraic Statistics for Computational Biology by : L. Pachter

Download or read book Algebraic Statistics for Computational Biology written by L. Pachter and published by Cambridge University Press. This book was released on 2005-08-22 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, first published in 2005, offers an introduction to the application of algebraic statistics to computational biology.

Sampling Techniques for Supervised or Unsupervised Tasks

Author : Frédéric Ros
Publisher : Springer Nature
ISBN 13 : 3030293491
Total Pages : 232 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Sampling Techniques for Supervised or Unsupervised Tasks by : Frédéric Ros

Download or read book Sampling Techniques for Supervised or Unsupervised Tasks written by Frédéric Ros and published by Springer Nature. This book was released on 2019-10-26 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling techniques for machine learning algorithms. It covers theory and models of sampling methods for managing scalability and the “curse of dimensionality”, their implementations, evaluations, and applications. A large part of the book is dedicated to database comprising standard feature vectors, and a special section is reserved to the handling of more complex objects and dynamic scenarios. The book is ideal for anyone teaching or learning pattern recognition and interesting teaching or learning pattern recognition and is interested in the big data challenge. It provides an accessible introduction to the field and discusses the state of the art concerning sampling techniques for supervised and unsupervised task. Provides a comprehensive description of sampling techniques for unsupervised and supervised tasks; Describe implementation and evaluation of algorithms that simultaneously manage scalable problems and curse of dimensionality; Addresses the role of sampling in dynamic scenarios, sampling when dealing with complex objects, and new challenges arising from big data. "This book represents a timely collection of state-of-the art research of sampling techniques, suitable for anyone who wants to become more familiar with these helpful techniques for tackling the big data challenge." M. Emre Celebi, Ph.D., Professor and Chair, Department of Computer Science, University of Central Arkansas "In science the difficulty is not to have ideas, but it is to make them work" From Carlo Rovelli

Perturbations, Optimization, and Statistics

Author : Tamir Hazan
Publisher : MIT Press
ISBN 13 : 0262337940
Total Pages : 412 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Perturbations, Optimization, and Statistics by : Tamir Hazan

Download or read book Perturbations, Optimization, and Statistics written by Tamir Hazan and published by MIT Press. This book was released on 2017-09-22 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules with improved theoretical guarantees. This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview. Chapters address recent modeling ideas that have arisen within the perturbations framework, including Perturb & MAP, herding, and the use of neural networks to map generic noise to distribution over highly structured data. They describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments of model samples to moments of data. They discuss understanding the relation of perturbation models to their traditional counterparts, with one chapter showing that the perturbations viewpoint can lead to new algorithms in the traditional setting. And they consider perturbation-based regularization in neural networks, offering a more complete understanding of dropout and studying perturbations in the context of deep neural networks.

Essentials of Computational Chemistry

Author : Christopher J. Cramer
Publisher : John Wiley & Sons
ISBN 13 : 1118712277
Total Pages : 624 pages
Book Rating : 4.1/5 (187 download)

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Book Synopsis Essentials of Computational Chemistry by : Christopher J. Cramer

Download or read book Essentials of Computational Chemistry written by Christopher J. Cramer and published by John Wiley & Sons. This book was released on 2013-04-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essentials of Computational Chemistry provides a balanced introduction to this dynamic subject. Suitable for both experimentalists and theorists, a wide range of samples and applications are included drawn from all key areas. The book carefully leads the reader thorough the necessary equations providing information explanations and reasoning where necessary and firmly placing each equation in context.

Uncertainty in Artificial Intelligence

Author : Laveen N. Kanal
Publisher : North Holland
ISBN 13 : 9780444700582
Total Pages : 509 pages
Book Rating : 4.7/5 (5 download)

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Book Synopsis Uncertainty in Artificial Intelligence by : Laveen N. Kanal

Download or read book Uncertainty in Artificial Intelligence written by Laveen N. Kanal and published by North Holland. This book was released on 1986 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hardbound. How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.