Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review

20 November 2023

Papers citing "Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review"

9 / 9 papers shown

Title
QReg: On Regularization Effects of Quantization Mohammadhossein Askarihemmat Reyhane Askari Hemmat Alexander Hoffman Ivan Lazarevich Ehsan Saboori Olivier Mastropietro Sudhakar Sah Yvon Savaria J. David MQ 29 3 0 24 Jun 2022
Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations Xinyu Zhang Ian Colbert Ken Kreutz-Delgado Srinjoy Das MQ 18 10 0 15 Oct 2021
Bayesian neural network unit priors and generalized Weibull-tail property M. Vladimirova Julyan Arbel Stéphane Girard BDL 41 9 0 06 Oct 2021
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 125 526 0 31 Jan 2021
TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems R. David Jared Duke Advait Jain Vijay Janapa Reddi Nat Jeffries ... Meghna Natraj Shlomi Regev Rocky Rhodes Tiezhen Wang Pete Warden 73 399 0 17 Oct 2020
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 948 18,450 0 17 Apr 2017
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights Aojun Zhou Anbang Yao Yiwen Guo Lin Xu Yurong Chen MQ 284 1,002 0 10 Feb 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 240 8,157 0 06 Jun 2015
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 173 1,134 0 30 Nov 2014