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Determinantal Point Processes For Machine Learning
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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 . This book was released on 2012 with total page 176 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.
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.
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.
Book Synopsis “A” Combination of Machine Learning and Determinantal Point Processes in Wireless Networks by : Gobika Balendra
Download or read book “A” Combination of Machine Learning and Determinantal Point Processes in Wireless Networks written by Gobika Balendra and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Learning with Determinantal Point Processes by : John A. Kulesza
Download or read book Learning with Determinantal Point Processes written by John A. Kulesza and published by . This book was released on 2012 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advances in the Theory of Determinantal Point Processes by : Justin K. Rising
Download or read book Advances in the Theory of Determinantal Point Processes written by Justin K. Rising and published by . This book was released on 2013 with total page 69 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Efficient Sampling for Determinantal Point Processes by : Chengtao Li (S.M.)
Download or read book Efficient Sampling for Determinantal Point Processes written by Chengtao Li (S.M.) and published by . This book was released on 2016 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Determinantal Point Processes (DPPs) are elegant probabilistic models of repulsion and diversity over discrete sets of items. It assigns higher probability to diverse subsets, making them more possible to be sampled. If we want to fully control the size of sampled subsets, the perfect choice would be k-Dpp, a practical specialization of DPP that only samples size-k subsets. In this thesis, we address efficient sampling algorithms and applications of (k-DPP). First, we propose a new method to approximately sample from k-Dpps. Our method takes advantage of the diversity property of subsets sampled from a DPP. It proceeds in two stages: first, it constructs a small subset called coreset from full dataset that approximates the k-Dpp distribution; then it samples from this coreset-approximated distribution efficiently. This approximate sampling strategy fits the original distribution better than existing methods, and is more efficient when multiple samples from k-DPP are required. Second, we consider accelerating existing Markov chain (k-)DPP under the condition that data kernel matrix is sparse. Concretely, we present a general framework for accelerating algorithms that requires computation of uT A-1u as one of computational subroutines. In our framework, we bound uT A-1u with Gauss-type quadrature efficiently. We study theoretical properties of Gauss-type quadrature and illustrate empirical consequences of our results by accelerating (k-)DPP sampling, where we observe tremendous speedups. Finally, we show how DPP can be applied to core machine learning applications. Due to its diversity/repulsiveness-promoting property, DPP is potentially useful in many applications where good sketching is needed. In our case, we apply DPP to Nyström method and kernel ridge regression. We show theoretical guarantees when using DPP in these methods, and observe superior performance in practice.
Book Synopsis Artificial Intelligence and Machine Learning by : Bart Bogaerts
Download or read book Artificial Intelligence and Machine Learning written by Bart Bogaerts and published by Springer Nature. This book was released on 2021-01-04 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains a selection of the best papers of the 31st Benelux Conference on Artificial Intelligence, BNAIC 2019, and 28th Belgian Dutch Machine Learning Conference, BENELEARN 2019, held in Brussels, Belgium in November 2019. The 11 papers presented in this volume were carefully reviewed and selected from 50 regular submissions. They address various aspects of artificial intelligence such as natural language processing, agent technology, game theory, problem solving, machine learning, human-agent interaction, AI and education, and data analysis.
Book Synopsis Gaussian Processes for Machine Learning by : Carl Edward Rasmussen
Download or read book Gaussian Processes for Machine Learning written by Carl Edward Rasmussen and published by MIT Press. This book was released on 2005-11-23 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Book Synopsis Machine Learning Paradigms by : George A. Tsihrintzis
Download or read book Machine Learning Paradigms written by George A. Tsihrintzis and published by Springer. This book was released on 2019-07-06 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary; accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation. To maximize the dissemination of research findings, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. This book is intended for professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter allows readers to probe further into those application areas that interest them most.
Book Synopsis Mathematics for Machine Learning by : Marc Peter Deisenroth
Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Book Synopsis Bayesian Optimization by : Roman Garnett
Download or read book Bayesian Optimization written by Roman Garnett and published by Cambridge University Press. This book was released on 2023-01-31 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way. This bottom-up approach illuminates unifying themes in the design of Bayesian optimization algorithms and builds a solid theoretical foundation for approaching novel situations. The core of the book is divided into three main parts, covering theoretical and practical aspects of Gaussian process modeling, the Bayesian approach to sequential decision making, and the realization and computation of practical and effective optimization policies. Following this foundational material, the book provides an overview of theoretical convergence results, a survey of notable extensions, a comprehensive history of Bayesian optimization, and an extensive annotated bibliography of applications.
Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Michele Berlingerio
Download or read book Machine Learning and Knowledge Discovery in Databases written by Michele Berlingerio and published by Springer. This book was released on 2019-01-22 with total page 866 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation. Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.
Book Synopsis Approximate Inference for Determinantal Point Processes by : Jennifer Gillenwater
Download or read book Approximate Inference for Determinantal Point Processes written by Jennifer Gillenwater and published by . This book was released on 2014 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis we explore a probabilistic model that is well-suited to a variety of subset selection tasks: the determinantal point process (DPP). DPPs were originally developed in the physics community to describe the repulsive interactions of fermions. More recently, they have been applied to machine learning problems such as search diversification and document summarization, which can be cast as subset selection tasks. A challenge, however, is scaling such DPP-based methods to the size of the datasets of interest to this community, and developing approximations for DPP inference tasks whose exact computation is prohibitively expensive. A DPP defines a probability distribution over all subsets of a ground set of items. Consider the inference tasks common to probabilistic models, which include normalizing, marginalizing, conditioning, sampling, estimating the mode, and maximizing likelihood. For DPPs, exactly computing the quantities necessary for the first four of these tasks requires time cubic in the number of items or features of the items. In this thesis, we propose a means of making these four tasks tractable even in the realm where the number of items and the number of features is large. Specifically, we analyze the impact of randomly projecting the features down to a lower-dimensional space and show that the variational distance between the resulting DPP and the original is bounded. In addition to expanding the circumstances in which these first four tasks are tractable, we also tackle the other two tasks, the first of which is known to be NP-hard (with no PTAS) and the second of which is conjectured to be NP-hard. For mode estimation, we build on submodular maximization techniques to develop an algorithm with a multiplicative approximation guarantee. For likelihood maximization, we exploit the generative process associated with DPP sampling to derive an expectation-maximization (EM) algorithm. We experimentally verify the practicality of all the techniques that we develop, testing them on applications such as news and research summarization, political candidate comparison, and product recommendation.
Book Synopsis Proceedings of the Forum "Math-for-Industry" 2018 by : Jin Cheng
Download or read book Proceedings of the Forum "Math-for-Industry" 2018 written by Jin Cheng and published by Springer Nature. This book was released on 2022-01-01 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume includes selected technical papers presented at the Forum “Math-for-Industry” 2018. The papers written by eminent researchers and academics working in the area of industrial mathematics from the viewpoint of financial mathematics, machine learning, neural networks, inverse problems, stochastic modelling, etc., discuss how the ingenuity of science, technology, engineering and mathematics are and will be expected to be utilized. This volume focuses on the role that mathematics-for-industry can play in interdisciplinary research to develop new methods. The contents are useful for researchers both in academia and industry working in interdisciplinary sectors.
Book Synopsis PRICAI 2021: Trends in Artificial Intelligence by : Duc Nghia Pham
Download or read book PRICAI 2021: Trends in Artificial Intelligence written by Duc Nghia Pham and published by Springer Nature. This book was released on 2021-11-01 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume set, LNAI 13031, LNAI 13032, and LNAI 13033 constitutes the thoroughly refereed proceedings of the 18th Pacific Rim Conference on Artificial Intelligence, PRICAI 2021, held in Hanoi, Vietnam, in November 2021.The 93 full papers and 28 short papers presented in these volumes were carefully reviewed and selected from 382 submissions. PRICAI covers a wide range of topics in the areas of social and economic importance for countries in the Pacific Rim: artificial intelligence, machine learning, natural language processing, knowledge representation and reasoning, planning and scheduling, computer vision, distributed artificial intelligence, search methodologies, etc. Part III includes two thematic blocks: Reinforcement Learning, followed by Vision and Perception.
Book Synopsis Artificial Intelligence and Machine Learning by : Toon Calders
Download or read book Artificial Intelligence and Machine Learning written by Toon Calders and published by Springer Nature. This book was released on 2023-09-04 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains a selection of the best papers of the 34th Benelux Conference on Artificial Intelligence, BNAIC/ BENELEARN 2022, held in Mechelen, Belgium, in November 2022. The 11 papers presented in this volume were carefully reviewed and selected from 134 regular submissions. They address various aspects of artificial intelligence such as natural language processing, agent technology, game theory, problem solving, machine learning, human-agent interaction, AI and education, and data analysis.
