Binary Neural Networks: Algorithms, Architectures, and Applications by Baochang Zhang, Sheng Xu, Mingbao Lin, Tiancheng Wang, David Doermann
- Binary Neural Networks: Algorithms, Architectures, and Applications
- Baochang Zhang, Sheng Xu, Mingbao Lin, Tiancheng Wang, David Doermann
- Page: 215
- Format: pdf, ePub, mobi, fb2
- ISBN: 9781032452500
- Publisher: CRC Press
Free downloads e-book Binary Neural Networks: Algorithms, Architectures, and Applications (English literature) by Baochang Zhang, Sheng Xu, Mingbao Lin, Tiancheng Wang, David Doermann PDB
Deep learning has achieved impressive results in image classification, computer vision, and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floatingpoint operations (FLOPs) has increased dramatically with larger networks, and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context, Binary Neural Networks: Algorithms, Architectures, and Applications will focus on CNN compression and acceleration, which are important for the research community. We will describe numerous methods, including parameter quantization, network pruning, low-rank decomposition, and knowledge distillation. More recently, to reduce the burden of handcrafted architecture design, neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS and binary NAS and its superiority and state-of-the-art performance in various applications, such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification, speech recognition, object detection, and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover, interested readers should have basic knowledge of machine learning and deep learning to better understand the methods described in this book. Key Features Reviews recent advances in CNN compression and acceleration Elaborates recent advances on binary neural network (BNN) technologies Introduces applications of BNN in image classification, speech recognition, object detection, and more
Binary Neural Networks: Algorithms, Architectures, and Applications .
These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover, interested .
[PDF] neural networks
ARCHITECTURES, ALGORITHMS,. AND APPLICATIONS. FUNDAMENTALS. OF NEURAL NETWORKS. Laurene Fausett. Page 2. Contents viii. Contents. 1.5. Who Is Developing Neural .
Binary convolutional neural network acceleration framework for .
Introduction. Convolutional neural networks (CNNs) have been widely used and achieved great success in various computer vision applications such as image .
[PDF] Fundamentals of Neural Networks - viXra.org
Neural Network. REFERENCES. [1]. Fausett, Laurene V. Fundamentals of neural networks: architectures, algorithms and applications. Pearson Education India .
[PDF] Binary Neural Networks: A Survey - arXiv
The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices .
Deep Learning and Neural Architecture Search for Optimizing .
In this paper, we introduce a novel and efficient binary network search method tailored for image super-resolution tasks. Therefore, for the application of a .
Binary Neural Networks: Algorithms, Architectures, and Applications
In this paper, two neural network models-one of which is with reel coding and the other one is with binary coding-are developed to dynamic lot sizing. Back- .
[PDF] Neural Networks For Pattern Recognition
This book is aimed at researchers in neural computing as well as those wishing to apply neural networks to practical applications. It is also intended to be .
Neural Networks: An Overview - SpringerLink
neural network, Neurocomputing: algorithms, architectures and applications, pp 41–50, Springer. Google Scholar. Knerr S. [1991], Un méthode nouvelle de .
Advancements in Algorithms and Neuromorphic Hardware for .
Spiking neural networks (SNNs) provide a power-efficient and brain-inspired computing paradigm for machine learning applications. However, .
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Our book will also introduce NAS due to its . Algorithms, Architectures, and Applications - Multimedia Computing, Communication and Intelligence.
Embedded Deep Learning: Algorithms, Architectures and Circuits for .
Request PDF | Embedded Deep Learning: Algorithms, Architectures and Circuits for Always-on Neural Network Processing | This book covers algorithmic and .
Hardware Architectures for Deep Learning | IET Digital Library
This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks.
4. Major Architectures of Deep Networks - Deep Learning [Book]
In Chapter 2, we gave you a deeper understanding of the algorithms and math that underlie neural networks in general. In this chapter, we focus more on the .