Fetching the paper…
Reading the bibliography…
Convolutional neural networks typically contain several downsampling operators, such as strided convolutions or pooling layers, that progressively reduce the resolution of intermediate representations.
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
K. Fukushima · 1980
Earlier work this paper cites.
Handwritten digit recognition with a back-propagation network
Yann LeCun, Bernhard Boser, John Denker, Donnie Henderson, Richard Howard, Wayne Hubbard, and Lawrence Jackel · 1989
Earlier work this paper cites.
The statistics of natural images
Daniel L Ruderman · 1994
Earlier work this paper cites.
Online learning and stochastic approximations
Léon Bottou et al · 1998
Earlier work this paper cites.
On the momentum term in gradient descent learning algorithms
Ning Qian · 1999
Earlier work this paper cites.
Modulation spectra of natural sounds and ethological theories of auditory processing
Nandini C Singh and Frédéric E Theunissen · 2003
Earlier work this paper cites.
Understanding Digital Signal Processing (2nd Edition)
Richard G. Lyons · 2004
Earlier work this paper cites.
Learning a similarity metric discriminatively, with application to face verification
S. Chopra, R. Hadsell, and Y. LeCun · 2005
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
The modulation transfer function for speech intelligibility
Taffeta M Elliott and Frédéric E Theunissen · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
Earlier work this paper cites.
A theoretical analysis of feature pooling in visual recognition
Y-Lan Boureau, Jean Ponce, and Yann LeCun · 2010
Earlier work this paper cites.
Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
Earlier work this paper cites.
Benjamin Graham · 2014
Earlier work this paper cites.
Learned-norm pooling for deep feedforward and recurrent neural networks
Caglar Gulcehre, Kyunghyun Cho, Razvan Pascanu, and Yoshua Bengio · 2014
Earlier work this paper cites.
What’s wrong with convolutional nets
Geoffrey Hinton · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Spectral representations for convolutional neural networks
Oren Rippel, Jasper Snoek, and Ryan P Adams · 2015
Cited alongside, same era.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Cited alongside, same era.
Deep speech 2: End-to-end speech recognition in english and mandarin
Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, et al · 2016
Cited alongside, same era.
Human superior temporal gyrus organization of spectrotemporal modulation tuning derived from speech stimuli
Patrick W Hullett, Liberty S Hamilton, Nima Mesgarani, Christoph E Schreiner, and Edward F Chang · 2016
Cited alongside, same era.
Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree
Chen-Yu Lee, Patrick W Gallagher, and Zhuowen Tu · 2016
Cited alongside, same era.
Fully convolutional speech recognition
Neil Zeghidour, Qiantong Xu, Vitaliy Liptchinsky, Nicolas Usunier, Gabriel Synnaeve, and Ronan Collobert · 2018
Later among the works it cites.
Hartley spectral pooling for deep learning
Hao Zhang and Jianwei Ma · 2018
Later among the works it cites.
Spectrotemporal modulation provides a unifying framework for auditory cortical asymmetries
Adeen Flinker, Werner K Doyle, Ashesh D Mehta, Orrin Devinsky, and David Poeppel · 2019
Later among the works it cites.
Searching for mobilenetv3
Andrew G. Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, and Hartwig Adam · 2019
Later among the works it cites.
Adaptive attention span in transformers
Sainbayar Sukhbaatar, Édouard Grave, Piotr Bojanowski, and Armand Joulin · 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…
Very deep multilingual convolutional neural networks for lvcsr
Tom Sercu, Christian Puhrsch, Brian Kingsbury, and Yann LeCun · 2016
Cited alongside, same era.
Conditional image generation with pixelcnn decoders
Aäron van den Oord, Nal Kalchbrenner, Lasse Espeholt, K. Kavukcuoglu, Oriol Vinyals, and A. Graves · 2016
Cited alongside, same era.
Designing neural network architectures using reinforcement learning
Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar · 2017
Cited alongside, same era.
Very deep convolutional networks for text classification
Alexis Conneau, Holger Schwenk, Loïc Barrault, and Yann Lecun · 2017
Cited alongside, same era.
Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders
Jesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Douglas Eck, Karen Simonyan, and Mohammad Norouzi · 2017
Cited alongside, same era.
Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Cited alongside, same era.
Self-supervised audio representation learning for mobile devices
Marco Tagliasacchi, Beat Gfeller, Félix de Chaumont Quitry, and Dominik Roblek · 2019
Later among the works it cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
Later among the works it cites.
Mnasnet: Platform-aware neural architecture search for mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, and Quoc V Le · 2019
Later among the works it cites.
Understanding straight-through estimator in training activation quantized neural nets
P Yin, J Lyu, S Zhang, S Osher, YY Qi, and J Xin · 2019
Later among the works it cites.
Making convolutional networks shift-invariant again
Richard Zhang · 2019
Later among the works it cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Later among the works it cites.
Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2020
Later among the works it cites.
Panns: Large-scale pretrained audio neural networks for audio pattern recognition
Qiuqiang Kong, Yin Cao, Turab Iqbal, Yuxuan Wang, Wenwu Wang, and Mark D Plumbley · 2020
Later among the works it cites.
Minimizing flops to learn efficient sparse representations
Biswajit Paria, Chih-Kuan Yeh, N. Xu, B. Póczos, Pradeep Ravikumar, and I. E. Yen · 2020
Later among the works it cites.
Revisiting resnets: Improved training and scaling strategies
Irwan Bello, William Fedus, Xianzhi Du, Ekin Dogus Cubuk, A. Srinivas, Tsung-Yi Lin, Jonathon Shlens, and Barret Zoph · 2021
Later among the works it cites.
Coatnet: Marrying convolution and attention for all data sizes
Zihang Dai, Hanxiao Liu, Quoc V Le, and Mingxing Tan · 2021
Later among the works it cites.
Improving sound event classification by increasing shift invariance in convolutional neural networks
Eduardo Fonseca, Andrés Ferraro, and Xavier Serra · 2021
Later among the works it cites.
Learning to downsample for segmentation of ultra-high resolution images, 2021
Chen Jin, Ryutaro Tanno, Thomy Mertzanidou, Eleftheria Panagiotaki, and Daniel C. Alexander · 2021
Later among the works it cites.
How convolutional neural networks deal with aliasing
Antônio H. Ribeiro and Thomas B. Schön · 2021
Later among the works it cites.
Learning to resize images for computer vision tasks
Hossein Talebi and Peyman Milanfar · 2021
Later among the works it cites.