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Performant Convolutional Neural Network (CNN) architectures must be tailored to specific tasks in order to consider the length, resolution, and dimensionality of the input data.
A simple weight decay can improve generalization
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Gradient-based learning applied to document recognition
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Learning multiple layers of features from tiny images
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Understanding the difficulty of training deep feedforward neural networks
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Learning separable filters
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Convolutional neural networks for speech recognition
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U-net: Convolutional networks for biomedical image segmentation
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Going deeper with convolutions
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Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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Deep residual learning for image recognition
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Dynamic filter networks
Xu Jia, Bert De Brabandere, Tinne Tuytelaars, and Luc V Gool · 2016
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Semi-supervised classification with graph convolutional networks
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Sgdr: Stochastic gradient descent with warm restarts
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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
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Wavenet: A generative model for raw audio
Aäron Van Den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew W Senior, and Koray Kavukcuoglu · 2016
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Deformable convolutional networks
Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Active convolution: Learning the shape of convolution for image classification
Yunho Jeon and Junmo Kim · 2017
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Raw waveform-based audio classification using sample-level cnn architectures
Jongpil Lee, Taejun Kim, Jiyoung Park, and Juhan Nam · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Experiment tracking with weights and biases, 2020
Lukas Biewald · 2020
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Principled weight initialization for hypernetworks
Oscar Chang, Lampros Flokas, and Hod Lipson · 2020
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Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data
Marc Finzi, Samuel Stanton, Pavel Izmailov, and Andrew Gordon Wilson · 2020
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Neural controlled differential equations for irregular time series
Patrick Kidger, James Morrill, James Foster, and Terry Lyons · 2020
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Group equivariant stand-alone self-attention for vision
David W Romero and Jean-Baptiste Cordonnier · 2020
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Deep neural networks motivated by partial differential equations
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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Harmonic networks: Deep translation and rotation equivariance
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An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
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Pointwise convolutional neural networks
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Deep parametric continuous convolutional neural networks
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3d steerable cnns: Learning rotationally equivariant features in volumetric data
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Implicit neural representations with periodic activation functions
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Fourier features let networks learn high frequency functions in low dimensional domains
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On layer normalization in the transformer architecture
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Multiplicative filter networks
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Combining recurrent, convolutional, and continuous-time models with linear state space layers
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Perceiver: General perception with iterative attention
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Exploiting redundancy: Separable group convolutional networks on lie groups
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Disco: accurate discrete scale convolutions
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Log-polar space convolution for convolutional neural networks
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Long range arena : A benchmark for efficient transformers
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Deep continuous networks
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Efficiently modeling long sequences with structured state spaces
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Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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S4nd: Modeling images and videos as multidimensional signals using state spaces
Eric Nguyen, Karan Goel, Albert Gu, Gordon W Downs, Preey Shah, Tri Dao, Stephen A Baccus, and Christopher Ré · 2022
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Learning strides in convolutional neural networks
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