Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Computational Label And Data Efficiency In Deep Learning For Sparse 3d Data
Download Computational Label And Data Efficiency In Deep Learning For Sparse 3d Data full books in PDF, epub, and Kindle. Read online Computational Label And Data Efficiency In Deep Learning For Sparse 3d Data ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Computational, label, and data efficiency in deep learning for sparse 3D data by : Li, Lanxiao
Download or read book Computational, label, and data efficiency in deep learning for sparse 3D data written by Li, Lanxiao and published by KIT Scientific Publishing. This book was released on 2024-05-13 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is widely applied to sparse 3D data to perform challenging tasks, e.g., 3D object detection and semantic segmentation. However, the high performance of deep learning comes with high costs, including computational costs and the effort to capture and label data. This work investigates and improves the efficiency of deep learning for sparse 3D data to overcome the obstacles to the further development of this technology.
Book Synopsis Computational, Label, and Data Efficiency in Deep Learning for Sparse 3D Data by : Lanxiao Li
Download or read book Computational, Label, and Data Efficiency in Deep Learning for Sparse 3D Data written by Lanxiao Li and published by . This book was released on 2023* with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Deep Learning through Sparse and Low-Rank Modeling by : Zhangyang Wang
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 2019-04-11 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. Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models Provides tactics on how to build and apply customized deep learning models for various applications
Book Synopsis Resource and Data Efficient Deep Learning by : Cody Austun Coleman
Download or read book Resource and Data Efficient Deep Learning written by Cody Austun Coleman and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Using massive computation, deep learning allows machines to translate large amounts of data into models that accurately predict the real world, enabling powerful applications like virtual assistants and autonomous vehicles. As datasets and computer systems have continued to grow in scale, so has the quality of machine learning models, creating an expensive appetite in practitioners and researchers for data and computation. To address this demand, this dissertation discusses ways to measure and improve both the computational and data efficiency of deep learning. First, we introduce DAWNBench and MLPerf as a systematic way to measure end-to-end machine learning system performance. Researchers have proposed numerous hardware, software, and algorithmic optimizations to improve the computational efficiency of deep learning. While some of these optimizations perform the same operations faster (e.g., increasing GPU clock speed), many others modify the semantics of the training procedure (e.g., reduced precision) and can even impact the final model's accuracy on unseen data. Because of these trade-offs between accuracy and computational efficiency, it has been difficult to compare and understand the impact of these optimizations. We propose and evaluate a new metric, time-to-accuracy, that can be used to compare different system designs and use it to evaluate high performing systems by organizing two public benchmark competitions, DAWNBench and MLPerf. MLPerf has now grown into an industry standard benchmark co-organized by over 70 organizations. Second, we present ways to perform data selection on large-scale datasets efficiently. Data selection methods, such as active learning and core-set selection, improve the data efficiency of machine learning by identifying the most informative data points to label or train on. Across the data selection literature, there are many ways to identify these training examples. However, classical data selection methods are prohibitively expensive to apply in deep learning because of the larger datasets and models. To make these methods tractable, we propose (1) "selection via proxy" (SVP) to avoid expensive training and reduce the computation per example and (2) "similarity search for efficient active learning and search" (SEALS) to reduce the number of examples processed. Both methods lead to order of magnitude performance improvements, making techniques like active learning on billions of unlabeled images practical for the first time.
Book Synopsis Deep Learning for Medical Image Analysis by : S. Kevin Zhou
Download or read book Deep Learning for Medical Image Analysis written by S. Kevin Zhou and published by Academic Press. This book was released on 2023-12-01 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis. · Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache
Book Synopsis Deep Learning in Data Analytics by : Debi Prasanna Acharjya
Download or read book Deep Learning in Data Analytics written by Debi Prasanna Acharjya and published by Springer Nature. This book was released on 2021-08-11 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society. Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.
Book Synopsis Deep Learning: Convergence to Big Data Analytics by : Murad Khan
Download or read book Deep Learning: Convergence to Big Data Analytics written by Murad Khan and published by Springer. This book was released on 2018-12-30 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.
Book Synopsis Resource-efficient Deep Learning by : Dongkuan Xu
Download or read book Resource-efficient Deep Learning written by Dongkuan Xu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The phenomenal success of deep learning in the past decade has been mostly driven by the construction of increasingly large deep neural network models. These models usually impose an ideal assumption that there are sufficient resources, including large-scale parameters, sufficient data, and massive computation, for the optimization. However, this assumption usually fails in real-world scenarios. For example, computer memory may be limited as in edge devices, large-scale data are difficult to obtain due to expensive costs and privacy constraints, and computational power is constrained as in most university labs. As a result, these resource discrepancy issues have hindered the democratization of deep learning techniques in many AI applications, and the development of efficient deep learning methods that can adapt to different resource constraints is of great importance. In this dissertation, I will present my Ph.D. research concerned with the aforementioned resource discrepancy issues to free AI from the parameter-data-computation hungry beast in three threads. The first thread focuses on data efficiency in deep learning technologies. This thread extends advances in deep learning to scenarios with small, sensitive, or unlabeled data, accelerating the acceptance and adoption of AI in real-world applications. In particular, I study self-supervised learning to remove the dependency on labels, few-shot learning to free model from a large number of samples, and and attentive learning to take full advantage of heterogeneous information sources. The second thread of my work focuses on advances of parameter efficiency in deep learning technologies, which enable us to democratize powerful deep learning models at scale to bridge computer memory divide and improve the adaptability of models in dynamic environments. I study network sparsity, i.e., the technology to prune networks, and network modularity, i.e., the technology to modularize neural networks into multiple modules, each of which is a function with its own parameters. The third thread focuses on computation efficiency of deep learning models, from inference to training, reducing the energy consumption of models, promoting environmental sustainability, and complementing data efficiency. More specifically, I study task-agnostic model compression, the task of generating efficient compressed models without utilizing the downstream task label information, avoiding the repetitive compression process, which saves much training cost.
Book Synopsis Multi-Label Dimensionality Reduction by : Liang Sun
Download or read book Multi-Label Dimensionality Reduction written by Liang Sun and published by CRC Press. This book was released on 2016-04-19 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks
Book Synopsis Handbook of Medical Image Computing and Computer Assisted Intervention by : S. Kevin Zhou
Download or read book Handbook of Medical Image Computing and Computer Assisted Intervention written by S. Kevin Zhou and published by Academic Press. This book was released on 2019-10-18 with total page 1074 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention. Presents the key research challenges in medical image computing and computer-assisted intervention Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society Contains state-of-the-art technical approaches to key challenges Demonstrates proven algorithms for a whole range of essential medical imaging applications Includes source codes for use in a plug-and-play manner Embraces future directions in the fields of medical image computing and computer-assisted intervention
Book Synopsis Computational Methods and Clinical Applications in Musculoskeletal Imaging by : Ben Glocker
Download or read book Computational Methods and Clinical Applications in Musculoskeletal Imaging written by Ben Glocker and published by Springer. This book was released on 2018-01-26 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Workshop and Challenge on Computational Methods and Clinical Applications for Musculoskeletal Imaging, MSKI 2017, held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 13 workshop papers were carefully reviewed and selected for inclusion in this volume. Topics of interest include all major aspects of musculoskeletal imaging, for example: clinical applications of musculoskeletal computational imaging; computer-aided detection and diagnosis of conditions of the bones, muscles and joints; image-guided musculoskeletal surgery and interventions; image-based assessment and monitoring of surgical and pharmacological treatment; segmentation, registration, detection, localization and visualization of the musculoskeletal anatomy; statistical and geometrical modeling of the musculoskeletal shape and appearance; image-based microstructural characterization of musculoskeletal tissue; novel techniques for musculoskeletal imaging.
Book Synopsis Sparsity Prior in Efficient Deep Learning Based Solvers and Models by : Xiaohan Chen
Download or read book Sparsity Prior in Efficient Deep Learning Based Solvers and Models written by Xiaohan Chen and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has been empirically successful in recent years thanks to the extremely over-parameterized deep models and the data-driven learning with enormous amounts of data. However, deep learning models are especially limited in terms of efficiency, which has two-fold meanings. Firstly, many deep models are designed in a black-box manner, which means these black-box models are unaware of the prior knowledge about the structure of the problems of interest and hence cannot efficiently utilize it. Such unawareness can cause redundancy in parameterization and inferior performance compared to more dedicated methods. Secondly, the extreme over-parameterization itself is inefficient in terms of model storage, memory requirements and computational complexity. This strictly constrains the realistic applications of deep learning on mobile devices with budget resources. Moreover, the financial and environmental costs of training such enormous deep models are unreasonably high, which is exactly the opposite of the call of green AI. In this work, we strive to address the inefficiency of deep models by introducing sparsity as an important prior knowledge to deep learning. Our efforts will be in three sub-directions. In the first direction, we aim at accelerating the solving process for a specific type of optimization problems with sparsity constraints. Instead of designing black-box deep learning models, we derive new parameterizations by absorbing insights from the sparse optimization field, which result in compact deep-learning-based solvers with significantly reduced training costs but superior empirical performance. In the second direction, we introduce sparsity to deep neural networks via weight pruning. Pruning reduces redundancy in over-parameterized deep networks by removing superfluous weights, thus naturally compressing the model storage and computational costs. We aim at pushing pruning to the limit by combining it with other compression techniques for extremely efficient deep models that can be deployed and fine-tuned on edge devices. In the third direction, we investigate what sparsity brings to deep networks. Creating sparsity in deep networks significantly changes the landscape of its loss function and thus behaviors during training. We aim at understanding what these changes are and how we can utilize them to train better sparse neural networks. The main content of this work can be summarized as below. Sparsity Prior in Efficient Deep Solvers. We adopt the algorithm unrolling method to transform classic optimization algorithms into feed-forward deep neural networks that can accelerate convergence by over 100x times. We also provide theoretical guarantees of linear convergence over the newly developed solvers, which is faster than the convergence rate achievable with classic optimization. Meanwhile, the number of parameters to be trained is reduced from millions to tens and even to 3 hyperparameters, decreasing the training time from hours to 6 minutes. Sparsity Prior in Efficient Deep Learning. We investigate compressing deep networks by unifying pruning, quantization and matrix factorization techniques to remove as much redundancy as possible, so that the resulting networks have low inference and/or training costs. The developed methods improve memory/storage efficiency and latency by at least 5x times, varying over data sets and models used. Sparsity Prior in Sparse Neural Networks. We discuss the properties and behaviors of sparse deep networks with the tool of lottery ticket hypothesis (LTH) and dynamic sparse training (DST) and explore their application for efficient training in computer vision, natural language processing and Internet-of-Things (IoT) systems. With our developed sparse neural networks, performance loss is significantly mitigated while by training much fewer parameters, bringing benefits of saving computation costs in general and communication costs specifically for IoT systems
Book Synopsis Deep Learning for Data Analytics by : Himansu Das
Download or read book Deep Learning for Data Analytics written by Himansu Das and published by Academic Press. This book was released on 2020-05-29 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis. Presents the latest advances in Deep Learning for data analytics and biomedical engineering applications. Discusses Deep Learning techniques as they are being applied in the real world of biomedical engineering and data science, including Deep Learning networks, deep feature learning, deep learning toolboxes, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networks Provides readers with an introduction to Deep Learning, along with coverage of deep belief networks, convolutional neural networks, Restricted Boltzmann Machines, data analytics basics, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning
Book Synopsis Deep Generative Models, and Data Augmentation, Labelling, and Imperfections by : Sandy Engelhardt
Download or read book Deep Generative Models, and Data Augmentation, Labelling, and Imperfections written by Sandy Engelhardt and published by Springer Nature. This book was released on 2021-09-29 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community. For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorous study of medical data related to machine learning systems.
Book Synopsis Computer Vision – ECCV 2022 by : Shai Avidan
Download or read book Computer Vision – ECCV 2022 written by Shai Avidan and published by Springer Nature. This book was released on 2022-11-02 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Book Synopsis Computational Intelligence Methods for Bioinformatics and Biostatistics by : Paolo Cazzaniga
Download or read book Computational Intelligence Methods for Bioinformatics and Biostatistics written by Paolo Cazzaniga and published by Springer Nature. This book was released on 2020-12-09 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the 16th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2019, which was held in Bergamo, Italy, during September 4-6, 2019. The 28 full papers presented in this volume were carefully reviewed and selected from 55 submissions. The papers are grouped in topical sections as follows: Computational Intelligence Methods for Bioinformatics and Biostatistics; Algebraic and Computational Methods for the Study of RNA Behaviour; Intelligence methods for molecular characterization medicine; Machine Learning in Healthcare Informatics and Medical Biology; Modeling and Simulation Methods for Computational Biology and Systems Medicine.
Book Synopsis Deep Learning for Biomedical Image Reconstruction by : Jong Chul Ye
Download or read book Deep Learning for Biomedical Image Reconstruction written by Jong Chul Ye and published by Cambridge University Press. This book was released on 2023-09-30 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the power of deep neural networks for image reconstruction with this state-of-the-art review of modern theories and applications. Including interdisciplinary examples and a step-by-step background of deep learning, this book provides insight into the future of biomedical image reconstruction with clinical studies and mathematical theory.