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Convolutional Neural Networks (CNNs) have revolutionized the research in computer vision, due to their ability to capture complex patterns, resulting in high inference accuracies.
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Compressing deep convolutional networks using vector quantization
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Caffe: Convolutional Architecture for Fast Feature Embedding. In ACM International Conference on Multimedia
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In International Conference on Machine Learning
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An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things Devices. In International Workshop on Internet of Things towards Applications
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Very Deep Convolutional Networks for Large-Scale Image recognition. In International Conference on Learning Representations
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Going Deeper with Convolutions. In IEEE Conference on Computer Vision and Pattern Recognition
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An Analysis of Deep Neural Network Models for Practical Applications
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Deep Residual Learning for Image Recognition. In IEEE Conference on Computer Vision and Pattern Recognition
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < < 0.5MB model size
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Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications. In International Conference on Learning Representations
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MCDNN: An Approximation-Based Execution Framework for Deep Stream Processing Under Resource Constraints. In International Conference on Mobile Systems, Applications, and Services
Seungyeop Han, Haichen Shen, Matthai Philipose, Sharad Agarwal, Alec Wolman, and Arvind Krishnamurthy. 2016 · 2016
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CUDA C Programming Guide
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TensorFlow
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Quantized Convolutional Neural Networks for Mobile Devices. In IEEE Conference on Computer Vision and Pattern Recognition
Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, and Jian Cheng. 2016 · 2016
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