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We study mechanisms to characterize how the asymptotic convergence of backpropagation in deep architectures, in general, is related to the network structure, and how it may be influenced by other design choices including activation type, denoising and dropout rate.
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Ways toward an early diagnosis in alzheimer’s disease: the alzheimer’s disease neuroimaging initiative (adni)
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A very fast learning method for neural networks based on sensitivity analysis
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Reducing the dimensionality of data with neural networks
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Greedy layer-wise training of deep networks
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
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The alzheimer’s disease neuroimaging initiative (adni): Mri methods
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The difficulty of training deep architectures and the effect of unsupervised pre-training
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On the manifold structure of the space of brain images
Samuel Gerber, Tolga Tasdizen, Sarang Joshi, and Ross Whitaker · 2009
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Learning multiple layers of features from tiny images
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
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Large-scale machine learning with stochastic gradient descent
Léon Bottou · 2010
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Why does unsupervised pre-training help deep learning?
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The clinical use of structural mri in alzheimer disease
Giovanni B Frisoni, Nick C Fox, Clifford R Jack, Philip Scheltens, and Paul M Thompson · 2010
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A practical guide to training restricted boltzmann machines
Geoffrey Hinton · 2010
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Learning convolutional feature hierarchies for visual recognition
K. Kavukcuoglu, P. Sermanet, Y. Boureau, K. Gregor, M. Mathieu, and Y. LeCun · 2010
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Breaking the curse of dimensionality with convex neural networks
Francis Bach · 2014
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The dropout learning algorithm
Pierre Baldi and Peter Sadowski · 2014
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Searching for exotic particles in high-energy physics with deep learning
Pierre Baldi, Peter Sadowski, and Daniel Whiteson · 2014
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Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Yann N Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio · 2014
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On the computational efficiency of training neural networks
R. Livni, S. Shalev-Shwartz, and O. Shamir · 2014
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On the number of linear regions of deep neural networks
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P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol · 2010
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Meta-analysis in medical research
AB Haidich · 2011
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Predictive markers for ad in a multi-modality framework: an analysis of mci progression in the adni population
Chris Hinrichs, Vikas Singh, Guofan Xu, Sterling C Johnson, Alzheimers Disease Neuroimaging Initiative, et al · 2011
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Max-pooling convolutional neural networks for vision-based hand gesture recognition
Jawad Nagi, Frederick Ducatelle, Gianni A Di Caro, Dan Ciresan, Ueli Meier, Alessandro Giusti, Farrukh Nagi, Jürgen Schmidhuber, and Luca Maria Gambardella · 2011
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On optimization methods for deep learning
J. Ngiam, A. Coates, A. Lahiri, B. Prochnow, Q. Le, and A. Ng · 2011
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Adding noise to the input of a model trained with a regularized objective
Salah Rifai, Xavier Glorot, Yoshua Bengio, and Pascal Vincent · 2011
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On random weights and unsupervised feature learning
A. Saxe, P. Koh, Z. Chen, M. Bhand, B. Suresh, and A. Ng · 2011
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Guido F Montufar, Razvan Pascanu, Kyunghyun Cho, and Yoshua Bengio · 2014
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Deep learning for neuroimaging: a validation study
Sergey M Plis, Devon R Hjelm, Ruslan Salakhutdinov, Elena A Allen, et al · 2014
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Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
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Very deep convolutional networks for large-scale image recognition
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The a4 study: stopping ad before symptoms begin?
Reisa A Sperling, Dorene M Rentz, Keith A Johnson, Jason Karlawish, Michael Donohue, David P Salmon, and Paul Aisen · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc VV Le · 2014
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Why are deep nets reversible: A simple theory, with implications for training
Sanjeev Arora, Yingyu Liang, and Tengyu Ma · 2015
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Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt, Benjamin Recht, and Yoram Singer · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment
Vamsi K Ithapu, Vikas Singh, Ozioma C Okonkwo, Richard J Chappell, N Maritza Dowling, Sterling C Johnson, Alzheimer’s Disease Neuroimaging Initiative, et al · 2015
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Beating the perils of non-convexity: Guaranteed training of neural networks using tensor methods
Majid Janzamin, Hanie Sedghi, and Anima Anandkumar · 2015
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The centiloid project: standardizing quantitative amyloid plaque estimation by pet
William E Klunk, Robert A Koeppe, Julie C Price, Tammie L Benzinger, Michael D Devous, William J Jagust, Keith A Johnson, Chester A Mathis, Davneet Minhas, Michael J Pontecorvo, et al · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Deep learning for detecting robotic grasps
Ian Lenz, Honglak Lee, and Ashutosh Saxena · 2015
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I Jordan · 2015
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A probabilistic theory of deep learning
Ankit B Patel, Tan Nguyen, and Richard G Baraniuk · 2015
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Haohan Wang and Bhiksha Raj · 2015
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Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, and Nando de Freitas · 2016
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Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization
Saeed Ghadimi, Guanghui Lan, and Hongchao Zhang · 2016
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Efficient hyperparameter optimization and infinitely many armed bandits
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2016
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Tao Wei, Changhu Wang, Rong Rui, and Chang Wen Chen · 2016
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