Fetching the paper…
Reading the bibliography…
In this paper we study the problem of learning the weights of a deep convolutional neural network.
Parafac. tutorial and applications
Rasmus Bro · 1997
Earlier work this paper cites.
The generic chaining: upper and lower bounds of stochastic processes
Michel Talagrand · 2006
Earlier work this paper cites.
A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston · 2008
Earlier work this paper cites.
Recovery of sparse 1-d signals from the magnitudes of their fourier transform
Kishore Jaganathan, Samet Oymak, and Babak Hassibi · 2012
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.
Tail bounds via generic chaining
Sjoerd Dirksen · 2013
Earlier work this paper cites.
Deep content-based music recommendation
Aaron Van den Oord, Sander Dieleman, and Benjamin Schrauwen · 2013
Earlier work this paper cites.
Tensor decompositions for learning latent variable models
Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M Kakade, and Matus Telgarsky · 2014
Earlier work this paper cites.
Guaranteed non-orthogonal tensor decomposition via alternating rank-
Animashree Anandkumar, Rong Ge, and Majid Janzamin · 2014
Earlier work this paper cites.
Provable bounds for learning some deep representations
Sanjeev Arora, Aditya Bhaskara, Rong Ge, and Tengyu Ma · 2014
Earlier work this paper cites.
Phase retrieval via wirtinger flow: Theory and algorithms
Emmanuel J. Candes, Xiaodong Li, and Mahdi Soltanolkotabi · 2014
Earlier work this paper cites.
Gaussian processes and the generic chaining
Michel Talagrand · 2014
Earlier work this paper cites.
Spectral norm of random tensors
Ryota Tomioka and Taiji Suzuki · 2014
Earlier work this paper cites.
Escaping from saddle points – online stochastic gradient for tensor decomposition
Rong Ge, Furong Huang, Chi Jin, and Yang Yuan · 2015
Earlier work this paper cites.
Beating the perils of non-convexity: Guaranteed training of neural networks using tensor methods
Majid Janzamin, Hanie Sedghi, and Anima Anandkumar · 2015
Cited alongside, same era.
On the expressive power of deep learning: A tensor analysis
Nadav Cohen, Or Sharir, and Amnon Shashua · 2016
Cited alongside, same era.
Convolutional rectifier networks as generalized tensor decompositions
Nadav Cohen and Amnon Shashua · 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.
Tensorly: Tensor learning in python
Jean Kossaifi, Yannis Panagakis, and Maja Pantic · 2016
Cited alongside, same era.
Tensor decomposition for signal processing and machine learning
Nicholas D Sidiropoulos, Lieven De Lathauwer, Xiao Fu, Kejun Huang, Evangelos E Papalexakis, and Christos Faloutsos · 2017
Later among the works it cites.
Learning ReLUs via gradient descent
Mahdi Soltanolkotabi · 2017
Later among the works it cites.
Mahdi Soltanolkotabi · 2017
Later among the works it cites.
Theoretical insights into the optimization landscape of over-parameterized shallow neural networks
Mahdi Soltanolkotabi, Adel Javanmard, and Jason D Lee · 2017
Later among the works it cites.
Learning non-overlapping convolutional neural networks with multiple kernels
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, and Jascha Sohl-Dickstein · 2016
Cited alongside, same era.
No bad local minima: Data independent training error guarantees for multilayer neural networks
Daniel Soudry and Yair Carmon · 2016
Cited alongside, same era.
Globally optimal gradient descent for a convnet with Gaussian inputs
A. Alon Brutzkus and Amir Globerson · 2017
Cited alongside, same era.
When is a convolutional filter easy to learn?
Simon S Du, Jason D Lee, and Yuandong Tian · 2017
Cited alongside, same era.
Gradient descent learns one-hidden-layer cnn: Don’t be afraid of spurious local minima
Simon S Du, Jason D Lee, Yuandong Tian, Barnabas Poczos, and Aarti Singh · 2017
Cited alongside, same era.
Learning one-hidden-layer neural networks with landscape design
Rong Ge, Jason D Lee, and Tengyu Ma · 2017
Cited alongside, same era.
Jean Kossaifi, Zachary C Lipton, Aran Khanna, Tommaso Furlanello, and Anima Anandkumar · 2017
Cited alongside, same era.
Kai Zhong, Zhao Song, and Inderjit S Dhillon · 2017
Later among the works it cites.
Recovery guarantees for one-hidden-layer neural networks
Kai Zhong, Zhao Song, Prateek Jain, Peter L Bartlett, and Inderjit S Dhillon · 2017
Later among the works it cites.
Local geometry of one-hidden-layer neural networks for logistic regression
Haoyu Fu, Yuejie Chi, and Yingbin Liang · 2018
Closest in time.
Predicting splicing from primary sequence with deep learning
Kishore Jaganathan et al · 2018
Closest in time.
A provably correct algorithm for deep learning that actually works
Eran Malach and Shai Shalev-Shwartz · 2018
Closest in time.
A mean field view of the landscape of two-layers neural networks
Song Mei, Andrea Montanari, and Phan-Minh Nguyen · 2018
Closest in time.
On the connection between learning two-layers neural networks and tensor decomposition
Marco Mondelli and Andrea Montanari · 2018
Closest in time.
Learning compact neural networks with regularization
Samet Oymak · 2018
Closest in time.
Learning the input layer of a deep convolutional neural network via centered gradient descent
Samet Oymak and Mahdi Soltanolkotabi · 2018
Closest in time.