Unsupervised Feature Learning Via Sparse Hierarchical Representations
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Unsupervised Feature Learning Via Sparse Hierarchical Representations
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Author : Honglak Lee
language : en
Publisher: Stanford University
Release Date : 2010
Unsupervised Feature Learning Via Sparse Hierarchical Representations written by Honglak Lee and has been published by Stanford University this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
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.
Unsupervised Feature Learning Via Sparse Hierarchical Representations
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Author : Honglak Lee
language : en
Publisher:
Release Date : 2010
Unsupervised Feature Learning Via Sparse Hierarchical Representations written by Honglak Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
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.
Deep Learning For Data Mining Unsupervised Feature Learning And Representation
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Author : Srinivas Babu Ratnam
language : en
Publisher: Xoffencerpublication
Release Date : 2023-07-03
Deep Learning For Data Mining Unsupervised Feature Learning And Representation written by Srinivas Babu Ratnam and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-03 with Computers categories.
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.
Advances In Brain Inspired Cognitive Systems
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Author : Cheng-Lin Liu
language : en
Publisher: Springer
Release Date : 2016-11-11
Advances In Brain Inspired Cognitive Systems written by Cheng-Lin Liu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-11 with Computers categories.
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.
Advances In Deep Learning Artificial Intelligence And Robotics
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Author : Luigi Troiano
language : en
Publisher: Springer Nature
Release Date : 2022-01-03
Advances In Deep Learning Artificial Intelligence And Robotics written by Luigi Troiano and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-03 with Technology & Engineering categories.
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.
Image And Graphics
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Author : Yao Zhao
language : en
Publisher: Springer
Release Date : 2017-12-29
Image And Graphics written by Yao Zhao and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-29 with Computers categories.
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.
Image Texture Analysis
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Author : Chih-Cheng Hung
language : en
Publisher: Springer
Release Date : 2019-06-05
Image Texture Analysis written by Chih-Cheng Hung and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-05 with Computers categories.
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.
Deep Learning For Natural Language Processing
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Author : Stephan Raaijmakers
language : en
Publisher: Simon and Schuster
Release Date : 2022-12-06
Deep Learning For Natural Language Processing written by Stephan Raaijmakers and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-06 with Computers categories.
Explore the most challenging issues of natural language processing, and learn how to solve them with cutting-edge deep learning! Deep learning has advanced natural language processing to exciting new levels and powerful new applications! For the first time, computer systems can achieve "human" levels of summarizing, making connections, and other tasks that require comprehension and context. Deep Learning for Natural Language Processing reveals the groundbreaking techniques that make these innovations possible. Stephan Raaijmakers distills his extensive knowledge into useful best practices, real-world applications, and the inner workings of top NLP algorithms. Deep learning has transformed the field of natural language processing. Neural networks recognize not just words and phrases, but also patterns. Models infer meaning from context, and determine emotional tone. Powerful deep learning-based NLP models open up a goldmine of potential uses. Deep Learning for Natural Language Processing teaches you how to create advanced NLP applications using Python and the Keras deep learning library. You'll learn to use state-of the-art tools and techniques including BERT and XLNET, multitask learning, and deep memory-based NLP. Fascinating examples give you hands-on experience with a variety of real world NLP applications. Plus, the detailed code discussions show you exactly how to adapt each example to your own uses!
Deep Learners And Deep Learner Descriptors For Medical Applications
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Author : Loris Nanni
language : en
Publisher: Springer Nature
Release Date : 2020-05-15
Deep Learners And Deep Learner Descriptors For Medical Applications written by Loris Nanni and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-15 with Technology & Engineering categories.
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.
Deep Learning For The Earth Sciences
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Author : Gustau Camps-Valls
language : en
Publisher: John Wiley & Sons
Release Date : 2021-08-16
Deep Learning For The Earth Sciences written by Gustau Camps-Valls and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-16 with Technology & Engineering categories.
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.