Content Gazette - September 2020 - 25
(Contents Continued from Page 603)
PoPS: Policy Pruning and Shrinking for Deep Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . D. Livne and K. Cohen
Cluster Pruning: An Efficient Filter Pruning Method for Edge AI Vision Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Gamanayake, L. Jayasinghe, B. K. K. Ng, and C. Yuen
Accelerating Convolutional Neural Network via Structured Gaussian Scale Mixture Models: A Joint Grouping and
Pruning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. Huang, W. Dong, J. Liu, F. Wu, G. Shi, and X. Li
Discriminative Layer Pruning for Convolutional Neural Networks . . . . . . . . . . . . . . A. Jordao, M. Lie, and W. R. Schwartz
Acceleration of Deep Convolutional Neural Networks Using Adaptive Filter Pruning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Singh, V. K. Verma, P. Rai, and V. P. Namboodiri
Propagating Asymptotic-Estimated Gradients for Low Bitwidth Quantized Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Chen, Y. Liu, H. Zhang, S. Hou, and J. Yang
Iteratively Training Look-Up Tables for Network Quantization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . F. Cardinaux, S. Uhlich, K. Yoshiyama, J. A. Garc´ıa, L. Mauch, S. Tiedemann, T. Kemp, and A. Nakamura
Binary Outer Product Expansion of Convolutional Kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Z. H. Sun
Circulant Binary Convolutional Networks for Object Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Liu, W. Ding, Y. Hu, X. Xia, B. Zhang, J. Z. Liu, and D. Doermann
LeanConvNets: Low-Cost Yet Effective Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Ephrath, M. Eliasof, L. Ruthotto, E. Haber, and E. Treister
www.signalprocessingsociety.org
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https://ieeexplore.ieee.org/document/8962235
https://ieeexplore.ieee.org/document/8979425
https://ieeexplore.ieee.org/document/9006798
https://ieeexplore.ieee.org/document/9007458
https://ieeexplore.ieee.org/document/9086749
https://ieeexplore.ieee.org/document/8957489
https://ieeexplore.ieee.org/document/9126178
https://ieeexplore.ieee.org/document/8954742
https://ieeexplore.ieee.org/document/8970353
https://ieeexplore.ieee.org/document/8989808
http://www.signalprocessingsociety.org
Content Gazette - September 2020
Table of Contents for the Digital Edition of Content Gazette - September 2020
Content Gazette - September 2020 - Cover1
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Content Gazette - September 2020 - 1
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