Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables-Reference-Cited by-同舟云学术

Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables

Published:2016-11-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM
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Author:

Bhattacharya Sourav¹,Lane Nicholas D.²

Affiliation:

1. Nokia Bell Labs

2. Nokia Bell Labs and University College London

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/2994551.2994564

Reference52 articles.

1. Y. Bengio I. J. Goodfellow and A. Courville "Deep learning " 2015 book in preparation for MIT Press. {Online}. Available: http://www.iro.umontreal.ca/~bengioy/dlbook Y. Bengio I. J. Goodfellow and A. Courville "Deep learning " 2015 book in preparation for MIT Press. {Online}. Available: http://www.iro.umontreal.ca/~bengioy/dlbook

2. Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

3. Y. Taigman M. Yang M. Ranzato and L. Wolf "Deepface: Closing the gap to human-level performance in face verification " in Conference on Computer Vision and Pattern Recognition (CVPR) 2014. 10.1109/CVPR.2014.220 Y. Taigman M. Yang M. Ranzato and L. Wolf "Deepface: Closing the gap to human-level performance in face verification " in Conference on Computer Vision and Pattern Recognition (CVPR) 2014. 10.1109/CVPR.2014.220

4. Compressing lstms into cnns;Geras K. J.;CoRR,2015

5. DianNao

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