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A Machine Learning-oriented Survey on Tiny Machine Learning

A Machine Learning-oriented Survey on Tiny Machine Learning

21 September 2023
Luigi Capogrosso
Federico Cunico
D. Cheng
Franco Fummi
Marco Cristani
    SyDa
    MU
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Papers citing "A Machine Learning-oriented Survey on Tiny Machine Learning"

12 / 12 papers shown
Title
MinUn: Accurate ML Inference on Microcontrollers
MinUn: Accurate ML Inference on Microcontrollers
Shikhar Jaiswal
R. Goli
Aayan Kumar
Vivek Seshadri
Rahul Sharma
8
2
0
29 Oct 2022
Overcoming Oscillations in Quantization-Aware Training
Overcoming Oscillations in Quantization-Aware Training
Markus Nagel
Marios Fournarakis
Yelysei Bondarenko
Tijmen Blankevoort
MQ
93
70
0
21 Mar 2022
A TinyML Platform for On-Device Continual Learning with Quantized Latent
  Replays
A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays
Leonardo Ravaglia
Manuele Rusci
D. Nadalini
Alessandro Capotondi
Francesco Conti
Luca Benini
BDL
37
62
0
20 Oct 2021
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision
  Transformer
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer
Sachin Mehta
Mohammad Rastegari
ViT
175
1,148
0
05 Oct 2021
Measuring what Really Matters: Optimizing Neural Networks for TinyML
Measuring what Really Matters: Optimizing Neural Networks for TinyML
Lennart Heim
Andreas Biri
Zhongnan Qu
Lothar Thiele
20
24
0
21 Apr 2021
TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems
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
86
461
0
17 Oct 2020
A Survey on Evolutionary Neural Architecture Search
A Survey on Evolutionary Neural Architecture Search
Yuqiao Liu
Y. Sun
Bing Xue
Mengjie Zhang
Gary G. Yen
Kay Chen Tan
112
303
0
25 Aug 2020
Benchmarking TinyML Systems: Challenges and Direction
Benchmarking TinyML Systems: Challenges and Direction
Colby R. Banbury
Vijay Janapa Reddi
Max Lam
William Fu
A. Fazel
...
Jae-sun Seo
Jeff Sieracki
Urmish Thakker
Marian Verhelst
Poonam Yadav
93
226
0
10 Mar 2020
What is the State of Neural Network Pruning?
What is the State of Neural Network Pruning?
Davis W. Blalock
Jose Javier Gonzalez Ortiz
Jonathan Frankle
John Guttag
170
1,018
0
06 Mar 2020
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
  Applications
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
20,214
0
17 Apr 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
234
11,568
0
09 Mar 2017
Xception: Deep Learning with Depthwise Separable Convolutions
Xception: Deep Learning with Depthwise Separable Convolutions
François Chollet
MDE
BDL
PINN
190
12,781
0
07 Oct 2016
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