Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation

14 January 2020

Papers citing "Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation"

9 / 9 papers shown

Title
Implications of Noise in Resistive Memory on Deep Neural Networks for Image Classification Yannick Emonds Kai Xi Holger Fröning 22 0 0 11 Jan 2024
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators Malte J. Rasch C. Mackin Manuel Le Gallo An Chen A. Fasoli ... P. Narayanan H. Tsai G. Burr Abu Sebastian Vijay Narayanan 13 83 0 16 Feb 2023
Effect of Batch Normalization on Noise Resistant Property of Deep Learning Models Omobayode Fagbohungbe Lijun Qian 24 10 0 15 May 2022
Impact of Learning Rate on Noise Resistant Property of Deep Learning Models Omobayode Fagbohungbe Lijun Qian 26 3 0 08 May 2022
Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning Models Omobayode Fagbohungbe Lijun Qian 29 0 0 07 May 2022
Denoising Noisy Neural Networks: A Bayesian Approach with Compensation Yulin Shao Soung Chang Liew Deniz Gunduz 56 14 0 22 May 2021
Benchmarking Inference Performance of Deep Learning Models on Analog Devices Omobayode Fagbohungbe Lijun Qian 19 7 0 24 Nov 2020
$All-Optical Machine Learning Using Diffractive Deep Neural Networks$ All-Optical Machine Learning Using Diffractive Deep Neural Networks Xing Lin Y. Rivenson N. Yardimci Muhammed Veli Mona Jarrahi Aydogan Ozcan 76 1,628 0 14 Apr 2018
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015