Next: About this document ...
Up: Summary of References Related
Previous: Dropout: A Simple Way
Contents
- 1
-
O Abdel-Hamid and A Mohamed.
Applying convolutional neural networks concepts to hybrid NN-HMM
model for speech recognition.
Acoustics, Speech and ..., 2012.
- 2
-
R Arandjelovic, Andrew Zisserman, and Basura Fernando.
AXES at TRECVid 2012: KIS, INS, and MED.
2012.
- 3
-
A F De Araujo, F Silveira, H Lakshman, J Zepeda, A Sheth, and B Girod.
The Stanford / Technicolor / Fraunhofer HHI Video.
2012.
- 4
-
LJ Ba and R Caurana.
Do Deep Nets Really Need to be Deep?
arXiv preprint arXiv:1312.6184, pages 1-6, 2013.
- 5
-
Yoshua Bengio.
Learning Deep Architectures for AI, volume 2.
2009.
- 6
-
Yoshua Bengio and Y LeCun.
Scaling learning algorithms towards AI.
Large-Scale Kernel Machines, (1):1-41, 2007.
- 7
-
Yoshua Bengio, Éric Thibodeau-Laufer, Guillaume Alain, and Jason Yosinski.
Deep Generative Stochastic Networks Trainable by Backprop.
June 2013.
- 8
-
Christopher M. Bishop.
Pattern recognition and machine learning., volume 1.
New York: springer, 2006., 2006.
- 9
-
JA Bogovic, GB Huang, and Viren Jain.
Learned versus Hand-Designed Feature Representations for 3d
Agglomeration.
arXiv preprint arXiv:1312.6159, pages 1-14, 2013.
- 10
-
Joan Bruna, Arthur Szlam, Wojciech Zaremba, and Yann LeCun.
Spectral Networks and Deep Locally Connected Networks on Graphs.
pages 1-14, 2014.
- 11
-
P Le Callet.
A convolutional neural network approach for objective video quality
assessment.
Neural Networks, IEEE ..., 5:1316-1327, 2006.
- 12
-
Ken Chatfield and Karen Simonyan.
Return of the Devil in the Details: Delving Deep into Convolutional
Nets.
arXiv preprint arXiv: ..., pages 1-11, 2014.
- 13
-
DC Ciresan and Alessandro Giusti.
Mitosis detection in breast cancer histology images with deep neural
networks.
Medical Image ..., 2013.
- 14
-
Kostas Daniilidis, Petros Maragos, and Nikos Paragios.
Computer Vision-ECCV 2010.
2010.
- 15
-
Sander Dieleman, P Brakel, and Benjamin Schrauwen.
Audio-based music classification with a pretrained convolutional
network.
...International Society for Music ...,
(Ismir):669-674, 2011.
- 16
-
David Eigen, Jason Rolfe, Rob Fergus, and Y LeCun.
Understanding Deep Architectures using a Recursive Convolutional
Network.
arXiv preprint arXiv:1312.1847, pages 1-9, 2013.
- 17
-
D Erhan, Yoshua Bengio, and Aaron Courville.
Why does unsupervised pre-training help deep learning?
...of Machine Learning ..., 9(2007):201-208, 2010.
- 18
-
Haoqiang Fan, Zhimin Cao, Yunin Jiang, Qi Yin, C Doudou, and Chinchilla Doudou.
Learning Deep Face Representation.
arXiv preprint arXiv:1403.2802, pages 1-10, 2014.
- 19
-
C Farabet, Camille Couprie, Laurent Najman, and Y LeCun.
Learning hierarchical features for scene labeling.
8:1915-1929, 2012.
- 20
-
Clément Farabet.
Towards Real-Time Image Understanding with Convolutional
Networks.
PhD thesis, Université Paris-Est, 2014.
- 21
-
Cecille Freeman.
Feature selection and hierarchical classifier design with
applications to human motion recognition.
PhD thesis, 2014.
- 22
-
Jerome Friedman, Trevor Hastie, and Robert Tibshirani.
The elements of statistical learning.
2001.
- 23
-
Yanwei Fu, Timothy M Hospedales, Tao Xiang, and Shaogang Gong.
Learning Multi-modal Latent Attributes.
IEEE transactions on pattern analysis and machine intelligence,
36(2):303-16, February 2014.
- 24
-
Yanwei Fu, TM Hospedales, Tao Xiang, and Shaogang Gong.
Attribute learning for understanding unstructured social activity.
Computer Vision-ECCV 2012, 2012.
- 25
-
Kunihiko Fukushima.
Neocognitron: A Self-organizing Neural Network Model for a Mechanism
of Pattern Recognition Unaffected by Shift in Position.
202, 1980.
- 26
-
AS Glassner.
Principles of digital image synthesis: Vol. 1.
Elsevier, 1995.
- 27
-
Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, and Yoshua
Bengio.
Maxout Networks.
pages 1319-1327, February 2013.
- 28
-
IJ Goodfellow, Yaroslav Bulatov, Julian Ibarz, Sacha Arnoud, and Vinay Shet.
Multi-digit Number Recognition from Street View Imagery using Deep
Convolutional Neural Networks.
arXiv preprint arXiv: ..., pages 1-13, 2013.
- 29
-
IJ Goodfellow, Dumitru Erhan, PL Carrier, Aaron Courville, Mehdi Mirza, Ben
Hamner, Will Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo
Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris
Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Ionescu,
Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz
Romaszko, Bing Xu, Zhang Chuang, and Yoshua Bengio.
Challenges in Representation Learning: A report on three machine
learning contests.
Neural Information ..., pages 1-8, 2013.
- 30
-
IJ Goodfellow, M Mirza, X Da, Aaron Courville, and Yoshua Bengio.
An Empirical Investigation of Catastrophic Forgeting in
Gradient-Based Neural Networks.
arXiv preprint arXiv: ..., 2013.
- 31
-
Fredric M. Ham and Ivica Kostanic.
Principles of Neurocomputing for Science and Engineering.
McGraw-Hill Higher Education, 1st edition, 2000.
- 32
-
Tele Hao, Tapani Raiko, Alexander Ilin, and Juha Karhunen.
Gated boltzmann machine in texture modeling.
...Neural Networks and Machine ..., 2012.
- 33
-
S Haykin.
Neural networks: a comprehensive foundation.
1994.
- 34
-
GE Hinton, Simon Osindero, and YW Teh.
A fast learning algorithm for deep belief nets.
Neural computation, 2006.
- 35
-
GE Hinton and RR Salakhutdinov.
Reducing the dimensionality of data with neural networks.
Science, 313(July):504-507, 2006.
- 36
-
GE Hinton, N Srivastava, Alex Krizhevsky, I Sutskever, and RR Salakhutdinov.
Improving neural networks by preventing co-adaptation of feature
detectors.
arXiv preprint arXiv: ..., pages 1-18, 2012.
- 37
-
Geoffrey Hinton.
To recognize shapes, first learn to generate images.
Progress in brain research, 2007.
- 38
-
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep
Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, and
Brian Kingsbury.
Deep Neural Networks for Acoustic Modeling in Speech Recognition.
IEEE Signal Processing Magazine, (November):82-97, 2012.
- 39
-
P O Hoyer and a Hyvärinen.
Independent component analysis applied to feature extraction from
colour and stereo images.
Network (Bristol, England), 11(3):191-210, August 2000.
- 40
-
DH Hubel and TN Wiesel.
Receptive fields, binocular interaction and functional architecture
in the cat's visual cortex.
The Journal of physiology, pages 106-154, 1962.
- 41
-
DH Hubel and TN Wiesel.
Receptive fields and functional architecture of monkey striate
cortex.
The Journal of physiology, pages 215-243, 1968.
- 42
-
A Hyvärinen and Patrik Hoyer.
Emergence of phase-and shift-invariant features by decomposition of
natural images into independent feature subspaces.
Neural computation, 1720:1705-1720, 2000.
- 43
-
Kevin Jarrett, Koray Kavukcuoglu, Marc' Aurelio Ranzato, and Yann LeCun.
What is the best multi-stage architecture for object recognition?
2009 IEEE 12th International Conference on Computer Vision,
pages 2146-2153, September 2009.
- 44
-
Shuiwang Ji, Ming Yang, and Kai Yu.
3D convolutional neural networks for human action recognition.
IEEE transactions on pattern analysis and machine intelligence,
35(1):221-31, January 2013.
- 45
-
Andrej Karpathy, G Toderici, S Shetty, Thomas Leung, Rahul Sukthankar, and
Li Fei-Fei.
Large-scale Video Classification with Convolutional Neural
Networks.
vision.stanford.edu, 2014.
- 46
-
Koray Kavukcuoglu, Pierre Sermanet, Y-lan Boureau, Yann LeCun, Karol Gregor,
and Michaël Mathieu.
Learning Convolutional Feature Hierarchies for Visual Recognition.
NIPS, (1):1-9, 2010.
- 47
-
FS Khan and RM Anwer.
Coloring Action Recognition in Still Images.
International journal of ..., pages 1-18, 2013.
- 48
-
JC King.
Why color management?
9th Congress of the International Color ..., 2002.
- 49
-
Alex Krizhevsky.
Convolutional Deep Belief Networks on CIFAR-10.
pages 1-9, 2010.
- 50
-
Alex Krizhevsky, I Sutskever, and GE Hinton.
ImageNet Classification with Deep Convolutional Neural Networks.
NIPS, pages 1-9, 2012.
- 51
-
Ivan Laptev and M Marszalek.
Learning realistic human actions from movies.
Computer Vision and ..., 2008.
- 52
-
Hugo Larochelle, D Erhan, Aaron Courville, James Bergstra, and Yoshua Bengio.
An empirical evaluation of deep architectures on problems with many
factors of variation.
Proceedings of the 24th ..., (2006):8, 2007.
- 53
-
Quoc V. Le, Will Y. Zou, Serena Y. Yeung, and Andrew Y. Ng.
Learning hierarchical invariant spatio-temporal features for action
recognition with independent subspace analysis.
Cvpr 2011, pages 3361-3368, June 2011.
- 54
-
Quoc V. Le, Will Y. Zou, Serena Y. Yeung, and Andrew Y. Ng.
Learning hierarchical invariant spatio-temporal features for action
recognition with independent subspace analysis.
Cvpr 2011, pages 3361-3368, June 2011.
- 55
-
QV Le, Jiquan Ngiam, Zhenghao Chen, DJ hao Chia, and PW Koh.
Tiled convolutional neural networks.
NIPS, pages 1-9, 2010.
- 56
-
QV Le, MA Ranzato, R Monga, and Matthieu Devin.
Building high-level features using large scale unsupervised
learning.
arXiv preprint arXiv: ..., 2011.
- 57
-
Nicolas Le Roux and Yoshua Bengio.
Representational power of restricted boltzmann machines and deep
belief networks.
Neural computation, 20(6):1631-49, June 2008.
- 58
-
Y LeCun.
Generalization and network design strategies.
Connections in Perspective. North-Holland, ..., 1989.
- 59
-
Y LeCun and Y Bengio.
Convolutional networks for images, speech, and time series.
...handbook of brain theory and neural networks, pages
1-14, 1995.
- 60
-
Y LeCun, B Boser, JS Denker, D Henderson, RE Howard, W Hubbard, and LD Jackel.
Backpropagation applied to handwritten zip code recognition.
Neural ..., 1989.
- 61
-
Y LeCun and L Bottou.
Gradient-based learning applied to document recognition.
Proceedings of the ..., 1998.
- 62
-
Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng.
Convolutional deep belief networks for scalable unsupervised
learning of hierarchical representations.
Proceedings of the 26th Annual International Conference on
Machine Learning - ICML '09, pages 1-8, 2009.
- 63
-
Honglak Lee, PT Pham, Y Largman, and AY Ng.
Unsupervised feature learning for audio classification using
convolutional deep belief networks.
NIPS, pages 1-9, 2009.
- 64
-
Min Lin, Qiang Chen, and Shuicheng Yan.
Network In Network.
page 10, December 2013.
- 65
-
M Marszalek, Ivan Laptev, and Cordelia Schmid.
Actions in context.
Computer Vision and ..., (i):2929-2936, 2009.
- 66
-
Jonathan Masci, Ueli Meier, D Ciresan, and J Schmidhuber.
Stacked convolutional auto-encoders for hierarchical feature
extraction.
Artificial Neural Networks ..., pages 52-59, 2011.
- 67
-
G Mesnil, Y Dauphin, X Glorot, Salah Rifai, Yoshua Bengio, Ian Goodfellow,
Erick Lavoie, Xavier Muller, Guillaume Desjardins, David Warde-Farley, Pascal
Vincent, Aaron Courville, and James Bergstra.
Unsupervised and Transfer Learning Challenge: a Deep Learning
Approach.
...of Machine Learning ..., 7:1-15, 2012.
- 68
-
Jiquan Ngiam, Zhenghao Chen, Daniel Chia, Pan Wei Koh, and Andrew Y. Ng.
Tiled convolutional neural networks.
Advances in Neural ..., pages 1-9, 2010.
- 69
-
Juan Carlos Niebles, Hongcheng Wang, and Li Fei-Fei.
Unsupervised Learning of Human Action Categories Using
Spatial-Temporal Words.
International Journal of Computer Vision, 79(3):299-318, March
2008.
- 70
-
Feng Ning, Damien Delhomme, Yann LeCun, Fabio Piano, Léon Bottou, and
Paolo Emilio Barbano.
Toward automatic phenotyping of developing embryos from videos.
IEEE transactions on image processing : a publication of the
IEEE Signal Processing Society, 14(9):1360-71, September 2005.
- 71
-
Mohammad Norouzi, Mani Ranjbar, and Greg Mori.
Stacks of convolutional restricted Boltzmann machines for
shift-invariant feature learning.
Computer Vision and Pattern ..., pages 2735-2742, 2009.
- 72
-
D Oneata, Jakob Verbeek, and C Schmid.
Action and event recognition with Fisher vectors on a compact
feature set.
IEEE Intenational Conference on Computer Vision (ICCV), 2013.
- 73
-
Paul Over, George Awad, Jon Fiscus, and Greg Sanders.
TRECVID 2013 - An Introduction to the Goals , Tasks , Data ,
Evaluation Mechanisms , and Metrics.
2013.
- 74
-
Razvan Pascanu and YN Dauphin.
On the saddle point problem for non-convex optimization.
arXiv preprint arXiv: ..., pages 1-11, 2014.
- 75
-
EA Perez, VF Mota, LM Maciel, Dhiego Sad, and Marcelo B. Vieira.
Combining gradient histograms using orientation tensors for human
action recognition.
Pattern Recognition (ICPR), 2012 21st International Conference
on. IEEE, 2012.
- 76
-
Kishore K. Reddy and Mubarak Shah.
Recognizing 50 human action categories of web videos.
Machine Vision and Applications, 24(5):971-981, November 2012.
- 77
-
Erik Reinhard, Wolfgang Heidrich, Paul Debevec, Sumanta Pattanaik, Greg Ward,
and Karol Myszkowski.
High Dynamic Range Imaging: Acquisition, Display, and
Image-Based Lighting.
Morgan Kaufmann, May 2010.
- 78
-
Roberto Rigamonti, Matthew a. Brown, and Vincent Lepetit.
Are sparse representations really relevant for image
classification?
Cvpr 2011, pages 1545-1552, June 2011.
- 79
-
Bahjat Safadi, Nadia Derbas, Abdelkader Hamadi, Thi-thu-thuy Vuong, Han Dong,
Philippe Mulhem, and Georges Qu.
Quaero at TRECVid 2013 : Semantic Indexing.
2013.
- 80
-
Konrad Schindler and Luc van Gool.
Action snippets: How many frames does human action recognition
require?
2008 IEEE Conference on Computer Vision and Pattern
Recognition, pages 1-8, June 2008.
- 81
-
Jürgen Schmidhuber.
Deep Learning in Neural Networks: An Overview.
Manno-Lugano, 2014.
- 82
-
C Schuldt, I Laptev, and B Caputo.
Recognizing human actions: a local SVM approach.
Pattern Recognition, 2004. ..., pages 3-7, 2004.
- 83
-
Pierre Sermanet, David Eigen, X Zhang, Michael Mathieu, Rob Fergus, and Yann
LeCun.
OverFeat: Integrated Recognition, Localization and Detection using
Convolutional Networks.
arXiv preprint arXiv: ..., pages 1-16, 2014.
- 84
-
T. Serre, L. Wolf, and T. Poggio.
Object Recognition with Features Inspired by Visual Cortex.
2005 IEEE Computer Society Conference on Computer Vision and
Pattern Recognition (CVPR'05), 2:994-1000, 2005.
- 85
-
Thomas Serre, Lior Wolf, Stanley Bileschi, Maximilian Riesenhuber, and Tomaso
Poggio.
Robust object recognition with cortex-like mechanisms.
IEEE transactions on pattern analysis and machine intelligence,
29(3):411-26, March 2007.
- 86
-
P Simard, Dave Steinkraus, and JC Platt.
Best Practices for Convolutional Neural Networks Applied to Visual
Document Analysis.
ICDAR, 2003.
- 87
-
Karen Simonyan, A Vedaldi, and A Zisserman.
Deep Inside Convolutional Networks: Visualising Image Classification
Models and Saliency Maps.
arXiv preprint arXiv:1312.6034, pages 1-8, 2013.
- 88
-
CGM Snoek and KEA van de Sande.
MediaMill at TRECVID 2013: Searching Concepts, Objects, Instances
and Events in Video.
...of TRECVID, 2013.
- 89
-
Jost Tobias Springenberg and Martin Riedmiller.
Improving Deep Neural Networks with Probabilistic Maxout Units.
December 2013.
- 90
-
Nathan Srebro and Adi Shraibman.
Rank, trace-norm and max-norm.
Learning Theory, pages 545-560, 2005.
- 91
-
N Srivastava and Geoffrey Hinton.
Dropout: A Simple Way to Prevent Neural Networks from Overfitting.
Journal of Machine ..., 15:1929-1958, 2014.
- 92
-
Yongqing Sun, Kyoko Sudo, Yukinobu Taniguchi, and H Li.
TRECVid 2012 Semantic Video Concept Detection by NTT-MD-DUT.
Proc. TRECVID 2012 ..., 2012.
- 93
-
Christian Szegedy, W Zaremba, and I Sutskever.
Intriguing properties of neural networks.
arXiv preprint arXiv: ..., pages 1-9, 2013.
- 94
-
R Szeliski.
Computer vision: algorithms and applications.
2010.
- 95
-
Yaniv Taigman, Ming Yang, Marc Aurelio Ranzato, and Lior Wolf.
DeepFace: Closing the Gap to Human-Level Performance in Face
Verification.
- 96
-
GW Taylor, Rob Fergus, Y LeCun, and Christoph Bregler.
Convolutional learning of spatio-temporal features.
Computer Vision-ECCV 2010, 2010.
- 97
-
Heng Wang, A Klaser, Cordelia Schmid, and Cheng-Lin Liu.
Action recognition by dense trajectories.
...and Pattern Recognition ( ..., 2011.
- 98
-
Heng Wang, Muhammad Muneeb Ullah, Alexander Klaser, Ivan Laptev, and Cordelia
Schmid.
Evaluation of local spatio-temporal features for action
recognition.
Procedings of the British Machine Vision Conference 2009, pages
124.1-124.11, 2009.
- 99
-
Heng Wang, Muhammad Muneeb Ullah, Alexander Klaser, Ivan Laptev, and Cordelia
Schmid.
Evaluation of local spatio-temporal features for action
recognition.
Procedings of the British Machine Vision Conference 2009, pages
124.1-124.11, 2009.
- 100
-
Jason Weston, Frédéric Ratle, and Ronan Collobert.
Deep learning via semi-supervised embedding.
Proceedings of the 25th international conference on Machine
learning - ICML '08, pages 1168-1175, 2008.
- 101
-
Lior Wolf, Tal Hassner, and Itay Maoz.
Face Recognition in Unconstrained Videos with Matched Background
Similarity.
Cvpr 2011, pages 529-534, June 2011.
- 102
-
Yang Yang, Guang Shu, and Mubarak Shah.
Semi-supervised Learning of Feature Hierarchies for Object Detection
in a Video.
2013 IEEE Conference on Computer Vision and Pattern
Recognition, pages 1650-1657, June 2013.
- 103
-
Matthew D. Zeiler, Graham W. Taylor, and Rob Fergus.
Adaptive deconvolutional networks for mid and high level feature
learning.
2011 International Conference on Computer Vision, pages
2018-2025, November 2011.
- 104
-
MD Zeiler and Rob Fergus.
Visualizing and Understanding Convolutional Networks.
arXiv preprint arXiv:1311.2901, 2013.
Miquel Perello Nieto
2014-11-28