Fetching the paper…
Reading the bibliography…
The successive subspace learning (SSL) principle was developed and used to design an interpretable learning model, known as the PixelHop method,for image classification in our prior work.
“Eigenfaces for recognition,”
Matthew Turk and Alex Pentland, · 1991
Earlier work this paper cites.
“Gradient-based learning applied to document recognition,”
Yann Lecun, Léon Bottou, Yoshua Bengio, and Patrick Haffner, · 1998
Earlier work this paper cites.
“Learning multiple layers of features from tiny images,”
Alex Krizhevsky and Geoffrey Hinton, · 2002
Earlier work this paper cites.
“Subspace learning for background modeling: A survey,”
Thierry Bouwmans, · 2009
Earlier work this paper cites.
“Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering,”
Hans-Peter Kriegel, Peer Kröger, and Arthur Zimek, · 2009
Earlier work this paper cites.
“A survey of multilinear subspace learning for tensor data,”
Haiping Lu, Konstantinos N Plataniotis, and Anastasios N Venetsanopoulos, · 2011
Earlier work this paper cites.
“Joint feature selection and subspace learning,”
Quanquan Gu, Zhenhui Li, and Jiawei Han, · 2011
Earlier work this paper cites.
Song Han, Huizi Mao, and William J Dally, · 2015
Earlier work this paper cites.
“Joint feature selection and subspace learning for cross-modal retrieval,”
Kaiye Wang, Ran He, Liang Wang, Wei Wang, and Tieniu Tan, · 2015
Cited alongside, same era.
“Binarized neural networks,”
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio, · 2016
Cited alongside, same era.
“Xnor-net: Imagenet classification using binary convolutional neural networks,”
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi, · 2016
Cited alongside, same era.
“Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size,”
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer, · 2016
Cited alongside, same era.
“Understanding convolutional neural networks with a mathematical model,”
C.-C. Jay Kuo, · 2016
Cited alongside, same era.
“Model compression and acceleration for deep neural networks: The principles, progress, and challenges,”
Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang, · 2018
Later among the works it cites.
“The lottery ticket hypothesis: Finding sparse, trainable neural networks,”
Jonathan Frankle and Michael Carbin, · 2018
Later among the works it cites.
“Reducing squeezenet storage size with depthwise separable convolutions,”
Aline Gondim Santos, Camila Oliveira de Souza, Cleber Zanchettin, David Macedo, Adriano LI Oliveira, and Teresa Ludermir, · 2018
Later among the works it cites.
“On data-driven Saak transform,”
C.-C. Jay Kuo and Yueru Chen, · 2018
Later among the works it cites.
“Pointhop: An explainable machine learning method for point cloud classification,”
Min Zhang, Haoxuan You, Pranav Kadam, Shan Liu, and C-C Jay Kuo, · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mohammad Javad Shafiee, Francis Li, Brendan Chwyl, and Alexander Wong, · 2017
Cited alongside, same era.
“The CNN as a guided multilayer RECOS transform [lecture notes],”
C.-C. Jay Kuo, · 2017
Cited alongside, same era.
“Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,”
Han Xiao, Kashif Rasul, and Roland Vollgraf, · 2017
Cited alongside, same era.
“Interpretable convolutional neural networks via feedforward design,”
C-C Jay Kuo, Min Zhang, Siyang Li, Jiali Duan, and Yueru Chen, · 2019
Later among the works it cites.
“Pixelhop: A successive subspace learning (ssl) method for object recognition,”
Yueru Chen and C-C Jay Kuo, · 2020
Closest in time.