Download or read book Unsupervised Feature Learning Via Sparse Hierarchical Representations written by Honglak Lee and published by Stanford University. This book was released on 2010 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has proved a powerful tool for artificial intelligence and data mining problems. However, its success has usually relied on having a good feature representation of the data, and having a poor representation can severely limit the performance of learning algorithms. These feature representations are often hand-designed, require significant amounts of domain knowledge and human labor, and do not generalize well to new domains. To address these issues, I will present machine learning algorithms that can automatically learn good feature representations from unlabeled data in various domains, such as images, audio, text, and robotic sensors. Specifically, I will first describe how efficient sparse coding algorithms --- which represent each input example using a small number of basis vectors --- can be used to learn good low-level representations from unlabeled data. I also show that this gives feature representations that yield improved performance in many machine learning tasks. In addition, building on the deep learning framework, I will present two new algorithms, sparse deep belief networks and convolutional deep belief networks, for building more complex, hierarchical representations, in which more complex features are automatically learned as a composition of simpler ones. When applied to images, this method automatically learns features that correspond to objects and decompositions of objects into object-parts. These features often lead to performance competitive with or better than highly hand-engineered computer vision algorithms in object recognition and segmentation tasks. Further, the same algorithm can be used to learn feature representations from audio data. In particular, the learned features yield improved performance over state-of-the-art methods in several speech recognition tasks.
Download or read book DEEP LEARNING FOR DATA MINING UNSUPERVISED FEATURE LEARNING AND REPRESENTATION written by Srinivas Babu Ratnam and published by Xoffencerpublication. This book was released on 2023-07-03 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: Several empirical research have come to the conclusion that the representation of data plays a vital role in the efficiency with which machine learning algorithms complete their tasks. This indicates that the design of feature extraction, preprocessing, and data transformations requires a disproportionate amount of time and resources when actually executing machine learning algorithms. These steps include preparing the data for analysis, extracting features from the data, and processing the data. This is because each of these components is essential to the algorithm as a whole in order for it to function properly. In spite of the fact that it is of the utmost significance, feature engineering calls for a significant amount of human effort. It also shows a shortcoming of the learning algorithms that are now in use, which is their inability to extract all of the pertinent characteristics from the data that is currently accessible. This is a difficulty with the approaches that are currently utilized in the process of learning. An approach that may be utilized to make up for such a shortfall is called feature engineering, and it involves making use of human intelligence in conjunction with prior information. It would be extremely desired to make learning algorithms less dependent on feature engineering in order to expedite the production of innovative applications and, more crucially, to realize advancements in artificial intelligence (AI). This would be done in order to achieve developments in AI. There are two possible consequences resulting from this. This would make it possible to use machine learning in a larger variety of applications that are simpler to put into action, which would increase the value of machine learning. An artificial intelligence has to have at least a fundamental comprehension of the environment in which humans live, and this may be accomplished if a learner is able to interpret the concealed explanatory factors that are embedded within the visible milieu of low-level sensory input. It is conceivable to combine feature engineering with feature learning in order to obtain state-of-the-art solutions that can be applied to actual circumstances in the real world.
Download or read book Experimental Robotics written by Jaydev P. Desai and published by Springer. This book was released on 2013-07-09 with total page 966 pages. Available in PDF, EPUB and Kindle. Book excerpt: The International Symposium on Experimental Robotics (ISER) is a series of bi-annual meetings, which are organized, in a rotating fashion around North America, Europe and Asia/Oceania. The goal of ISER is to provide a forum for research in robotics that focuses on novelty of theoretical contributions validated by experimental results. The meetings are conceived to bring together, in a small group setting, researchers from around the world who are in the forefront of experimental robotics research. This unique reference presents the latest advances across the various fields of robotics, with ideas that are not only conceived conceptually but also explored experimentally. It collects robotics contributions on the current developments and new directions in the field of experimental robotics, which are based on the papers presented at the 13the ISER held in Québec City, Canada, at the Fairmont Le Château Frontenac, on June 18-21, 2012. This present thirteenth edition of Experimental Robotics edited by Jaydev P. Desai, Gregory Dudek, Oussama Khatib, and Vijay Kumar offers a collection of a broad range of topics in field and human-centered robotics.
Download or read book Advances in Brain Inspired Cognitive Systems written by Cheng-Lin Liu and published by Springer. This book was released on 2016-11-11 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th International Conference on Brain Inspired Cognitive Systems, BICS 2016, held in Beijing, China, in November 2016. The 32 full papers presented were carefully reviewed and selected from 43 submissions. They discuss the emerging areas and challenges, present the state of the art of brain-inspired cognitive systems research and applications in diverse fields by covering many topics in brain inspired cognitive systems related research including biologically inspired systems, cognitive neuroscience, models consciousness, and neural computation.
Download or read book Deep Learning for Natural Language Processing written by Stephan Raaijmakers and published by Simon and Schuster. This book was released on 2022-11-29 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Humans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context. Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively. In this insightful book, NLP expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Download or read book Advances in Deep Learning Artificial Intelligence and Robotics written by Luigi Troiano and published by Springer Nature. This book was released on 2022-01-03 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book of Advances in Deep Learning, Artificial Intelligence and Robotics (proceedings of ICDLAIR 2020) is intended to be used as a reference by students and researchers who collect scientific and technical contributions with respect to models, tools, technologies and applications in the field of modern artificial intelligence and robotics. Deep Learning, AI and robotics represent key ingredients for the 4th Industrial Revolution. Their extensive application is dramatically changing products and services, with a large impact on labour, economy and society at all. The research and reports of new technologies and applications in DL, AI and robotics like biometric recognition systems, medical diagnosis, industries, telecommunications, AI petri nets model-based diagnosis, gaming, stock trading, intelligent aerospace systems, robot control and web intelligence aim to bridge the gap between these non-coherent disciplines of knowledge and fosters unified development in next-generation computational models for machine intelligence.
Download or read book Image and Graphics written by Yao Zhao and published by Springer. This book was released on 2017-12-29 with total page 733 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume set LNCS 10666, 10667, and 10668 constitutes the refereed conference proceedings of the 9th International Conference on Image and Graphics, ICIG 2017, held in Shanghai, China, in September 2017. The 172 full papers were selected from 370 submissions and focus on advances of theory, techniques and algorithms as well as innovative technologies of image, video and graphics processing and fostering innovation, entrepreneurship, and networking.
Download or read book Deep Learning for the Earth Sciences written by Gustau Camps-Valls and published by John Wiley & Sons. This book was released on 2021-08-18 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.
Download or read book Image Texture Analysis written by Chih-Cheng Hung and published by Springer. This book was released on 2019-06-05 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This useful textbook/reference presents an accessible primer on the fundamentals of image texture analysis, as well as an introduction to the K-views model for extracting and classifying image textures. Divided into three parts, the book opens with a review of existing models and algorithms for image texture analysis, before delving into the details of the K-views model. The work then concludes with a discussion of popular deep learning methods for image texture analysis. Topics and features: provides self-test exercises in every chapter; describes the basics of image texture, texture features, and image texture classification and segmentation; examines a selection of widely-used methods for measuring and extracting texture features, and various algorithms for texture classification; explains the concepts of dimensionality reduction and sparse representation; discusses view-based approaches to classifying images; introduces the template for the K-views algorithm, as well as a range of variants of this algorithm; reviews several neural network models for deep machine learning, and presents a specific focus on convolutional neural networks. This introductory text on image texture analysis is ideally suitable for senior undergraduate and first-year graduate students of computer science, who will benefit from the numerous clarifying examples provided throughout the work.
Download or read book Deep Learners and Deep Learner Descriptors for Medical Applications written by Loris Nanni and published by Springer Nature. This book was released on 2020-05-15 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to the current trends in using deep learners and deep learner descriptors for medical applications. It reviews the recent literature and presents a variety of medical image and sound applications to illustrate the five major ways deep learners can be utilized: 1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data); 2) by implementing transfer learning from a pre-trained deep learner and extracting deep features for different CNN layers that can be fed into simpler classifiers, such as the support vector machine; 3) by fine-tuning one or more pre-trained deep learners on an unrelated dataset so that they are able to identify novel medical datasets; 4) by fusing different deep learner architectures; and 5) by combining the above methods to generate a variety of more elaborate ensembles. This book is a value resource for anyone involved in engineering deep learners for medical applications as well as to those interested in learning more about the current techniques in this exciting field. A number of chapters provide source code that can be used to investigate topics further or to kick-start new projects.
Download or read book Deep Learning for the Earth Sciences written by Gustau Camps-Valls and published by John Wiley & Sons. This book was released on 2021-08-16 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.
Download or read book Deep Learning in Healthcare written by Yen-Wei Chen and published by Springer Nature. This book was released on 2019-11-18 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems. Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data. Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.
Download or read book Deep Learning through Sparse and Low Rank Modeling written by Zhangyang Wang and published by Academic Press. This book was released on 2019-04-12 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics.
Download or read book Feature and Dimensionality Reduction for Clustering with Deep Learning written by Frederic Ros and published by Springer Nature. This book was released on 2024-01-22 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by “family” to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.
Download or read book Machine Learning in Medical Imaging written by Guorong Wu and published by Springer. This book was released on 2013-09-18 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, in Nagoya, Japan, in September 2013. The 32 contributions included in this volume were carefully reviewed and selected from 57 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.
Download or read book Neural Information Processing written by Tingwen Huang and published by Springer. This book was released on 2012-11-05 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: The five volume set LNCS 7663, LNCS 7664, LNCS 7665, LNCS 7666 and LNCS 7667 constitutes the proceedings of the 19th International Conference on Neural Information Processing, ICONIP 2012, held in Doha, Qatar, in November 2012. The 423 regular session papers presented were carefully reviewed and selected from numerous submissions. These papers cover all major topics of theoretical research, empirical study and applications of neural information processing research. The 5 volumes represent 5 topical sections containing articles on theoretical analysis, neural modeling, algorithms, applications, as well as simulation and synthesis.
Download or read book Computer Vision ACCV 2014 written by Daniel Cremers and published by Springer. This book was released on 2015-04-16 with total page 743 pages. Available in PDF, EPUB and Kindle. Book excerpt: The five-volume set LNCS 9003--9007 constitutes the thoroughly refereed post-conference proceedings of the 12th Asian Conference on Computer Vision, ACCV 2014, held in Singapore, Singapore, in November 2014. The total of 227 contributions presented in these volumes was carefully reviewed and selected from 814 submissions. The papers are organized in topical sections on recognition; 3D vision; low-level vision and features; segmentation; face and gesture, tracking; stereo, physics, video and events; and poster sessions 1-3.
