Fetching the paper…
Reading the bibliography…
In the last decade, we have witnessed the introduction of several novel deep neural network (DNN) architectures exhibiting ever-increasing performance across diverse tasks.
Dynamic programming algorithm optimization for spoken word recognition
Hiroaki Sakoe and Seibi Chiba · 1978
Earlier work this paper cites.
Learning representations by back-propagating errors
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1986
Earlier work this paper cites.
Note on the optimal transportation of distributions
Cyril S Smith and Martin Knott · 1987
Earlier work this paper cites.
Approximation by superpositions of a sigmoidal function
G. Cybenko · 1989
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
Kurt Hornik · 1989
Earlier work this paper cites.
On a formula for the l2 wasserstein metric between measures on euclidean and hilbert spaces
Matthias Gelbrich · 1990
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Kurt and Hornik · 1991
Earlier work this paper cites.
Approximation and estimation bounds for artificial neural networks
Andrew R. Barron · 1994
Earlier work this paper cites.
Honey, i shrunk the sample covariance matrix
Olivier Ledoit and Michael Wolf · 2004
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E. Hinton · 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.
Rectifier nonlinearities improve neural network acoustic models
Andrew L Maas, Awni Y Hannun, and Andrew Y Ng · 2013
Earlier work this paper cites.
On the number of linear regions of deep neural networks
Guido Montúfar, Razvan Pascanu, KyungHyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2014
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Machine learning: Trends, perspectives, and prospects
Michael I Jordan and Tom M Mitchell · 2015
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Earlier work this paper cites.
Deep learning in neural networks: An overview
Jürgen Schmidhuber · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
The power of depth for feedforward neural networks
Ronen Eldan and Ohad Shamir · 2016
Cited alongside, same era.
Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
Cited alongside, same era.
Torchvision: Pytorch’s computer vision library
TorchVision maintainers and contributors · 2016
Cited alongside, same era.
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.
Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Later among the works it cites.
Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
Later among the works it cites.
Expressivity of deep neural networks
Ingo Gühring, Mones Raslan, and Gitta Kutyniok · 2020
Later among the works it cites.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Arman Alemi · 2016
Cited alongside, same era.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Wei Wang, Wenhan Weng, Marco Andreetto, and Hartwig Adam · 2017
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
A survey on deep learning in medical image analysis
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud A.A. Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, and Clara I. Sánchez · 2017
Cited alongside, same era.
The expressive power of neural networks: a view from the width
Zhou Lu, Hongming Pu, Feicheng Wang, Zhiqiang Hu, and Liwei Wang · 2017
Cited alongside, same era.
Depth-width tradeoffs in approximating natural functions with neural networks
Itay Safran and Ohad Shamir · 2017
Cited alongside, same era.
Later among the works it cites.
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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, Jakob Uszkoreit, and Neil Houlsby · 2021
Later among the works it cites.
Deberta: Decoding-enhanced bert with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Wei Chen · 2021
Later among the works it cites.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Later among the works it cites.
The nonlinearity coefficient - A practical guide to neural architecture design
George Philipp · 2021
Later among the works it cites.
Solving inefficiency of self-supervised representation learning
Guangrun Wang, Keze Wang, Guangcong Wang, Philip HS Torr, and Liang Lin · 2021
Later among the works it cites.
Ressl: Relational self-supervised learning with weak augmentation
Mingkai Zheng, Shan You, Fei Wang, Chen Qian, Changshui Zhang, Xiaogang Wang, and Chang Xu · 2021
Later among the works it cites.
Activation functions in deep learning: A comprehensive survey and benchmark
Shiv Ram Dubey, Satish Kumar Singh, and Bidyut Baran Chaudhuri · 2022
Later among the works it cites.
A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
Later among the works it cites.
On the optimal memorization power of relu neural networks
Gal Vardi, Gilad Yehudai, and Ohad Shamir · 2022
Later among the works it cites.
Reliability of CKA as a similarity measure in deep learning
MohammadReza Davari, Stefan Horoi, Amine Natik, Guillaume Lajoie, Guy Wolf, and Eugene Belilovsky · 2023
Closest in time.
Similarity contrastive estimation for self-supervised soft contrastive learning
Julien Denize, Jaonary Rabarisoa, Astrid Orcesi, Romain Hérault, and Stéphane Canu · 2023
Closest in time.
OpenAI · 2023
Closest in time.
No reason for no supervision: Improved generalization in supervised models
Mert Bülent Sarıyıldız, Yannis Kalantidis, Karteek Alahari, and Diane Larlus · 2023
Closest in time.
Llama: Open and efficient foundation language models, 2023
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
Closest in time.
Ai and compute
OpenAI · 2024
Closest in time.
Neural redshift: Random networks are not random functions
Damien Teney, Armand Mihai Nicolicioiu, Valentin Hartmann, and Ehsan Abbasnejad · 2024
Closest in time.