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references-original.bib
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@InProceedings{Chen2015,
Title = {{Compressing Neural Networks with the Hashing Trick.}},
Author = {Wenlin Chen and James T. Wilson and Stephen Tyree and Kilian Q. Weinberger and Yixin Chen},
Booktitle = {{ICML}},
Year = {2015},
Editor = {Francis R. Bach and David M. Blei},
Pages = {2285--2294},
Publisher = {JMLR.org},
Series = {{JMLR Proceedings}},
Volume = {37},
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Keywords = {dblp},
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Timestamp = {2016.07.31}
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@InProceedings{Fahlman1989,
Title = {{The Cascade-Correlation Learning Architecture.}},
Author = {Scott E. Fahlman and Christian Lebiere},
Booktitle = {{NIPS}},
Year = {1989},
Editor = {David S. Touretzky},
Pages = {524--532},
Publisher = {Morgan Kaufmann},
__markedentry = {[yani:6]},
Crossref = {conf/nips/1989},
ISBN = {1-55860-100-7},
Owner = {yani},
Timestamp = {2016.07.31}
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@InProceedings{He2015b,
Title = {{Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification.}},
Author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
Booktitle = {{ICCV}},
Year = {2015},
Pages = {1026--1034},
Publisher = {IEEE},
__markedentry = {[yani:6]},
Crossref = {conf/iccv/2015},
Keywords = {dblp},
Owner = {yani},
Timestamp = {2016.07.31}
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@InProceedings{Krizhevsky2012,
Title = {{ImageNet Classification with Deep Convolutional Neural Networks.}},
Author = {Alex Krizhevsky and Ilya Sutskever and Geoffrey E. Hinton},
Booktitle = {{NIPS}},
Year = {2012},
Editor = {Peter L. Bartlett and Fernando C. N. Pereira and Christopher J. C. Burges and L{\'e}on Bottou and Kilian Q. Weinberger},
Pages = {1106--1114},
__markedentry = {[yani:6]},
Crossref = {conf/nips/2012},
Keywords = {Convolutional with Classification Deep ImageNet Networks. Neural},
Owner = {yani},
Timestamp = {2016.07.31}
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@InProceedings{conf/icml/NairH10,
Title = {{Rectified Linear Units Improve Restricted Boltzmann Machines.}},
Author = {Vinod Nair and Geoffrey E. Hinton},
Booktitle = {{ICML}},
Year = {2010},
Editor = {Johannes F{\"u}rnkranz and Thorsten Joachims},
Pages = {807--814},
Publisher = {Omnipress},
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@InProceedings{conf/cvpr/RigamontiSLF13,
Title = {{Learning Separable Filters.}},
Author = {Roberto Rigamonti and Amos Sironi and Vincent Lepetit and Pascal Fua},
Booktitle = {{CVPR}},
Year = {2013},
Pages = {2754--2761},
Publisher = {IEEE},
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@InProceedings{Sutskever2013momentum,
Title = {On the importance of initialization and momentum in deep learning},
Author = {Ilya Sutskever and
James Martens and
George E. Dahl and
Geoffrey E. Hinton},
Booktitle = {Proceedings of the 30th International Conference on Machine Learning,
{ICML} 2013, Atlanta, GA, USA, 16-21 June 2013},
Year = {2013},
Pages = {1139--1147},
Crossref = {conf/icml/2013},
Url = {http://jmlr.org/proceedings/papers/v28/sutskever13.html}
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@Article{journals/neco/AmitG97,
Title = {{Shape Quantization And Recognition With Randomized Trees.}},
Author = {Yali Amit and Donald Geman},
Journal = {Neural Computation},
Year = {1997},
Number = {7},
Pages = {1545--1588},
Volume = {9},
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Biburl = {http://www.bibsonomy.org/bibtex/28c6c1c1e70a8a960dbb7d991012f231c/dblp},
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@InProceedings{Ba2013dothey,
Title = {{Do Deep Nets Really Need to be Deep}},
Author = {L. J. Ba and R. Caruana},
Booktitle = {{eprint ar{X}iv:1312.6184v5}},
Year = {2013}
}
@InProceedings{bastani2016measuring,
Title = {{Measuring Neural Net Robustness with Constraints}},
Author = {Osbert Bastani and Yani Ioannou and Leonidas Lampropoulos and Dimitrios Vytiniotis and Aditya Nori and Antonio Criminisi},
Booktitle = {{Neural Information Processing Systems (NIPS), 2016}},
Year = {2016},
Owner = {yani},
Timestamp = {2016.08.30}
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@InProceedings{Bengio2010labeltree,
Title = {{Label Embedding Trees for Large Multi-Class Tasks}},
Author = {S. Bengio and J. Weston and D. Grangier},
Booktitle = {{Conference and Workshop on Neural Information Processing Systems}},
Year = {2010}
}
@Article{bengio:ieeenn94,
Title = {{Learning Long-Term Dependencies With Gradient Descent Is Difficult}},
Author = {Yoshua Bengio and Patrick Simard and Paolo Frasconi},
Journal = {IEEE Transactions on Neural Networks},
Year = {1994},
Number = {2},
Pages = {157--166},
Volume = {5},
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@Book{Bishop1995,
Title = {{Neural Networks for Pattern Recognition}},
Author = {Christopher M. Bishop},
Publisher = {Oxford University Press},
Year = {1995},
Address = {Oxford},
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@InCollection{Bottou2012sgdtricks,
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Author = {L{\'e}on Bottou},
Booktitle = {{Neural Networks: Tricks of the Trade (2nd ed.)}},
Publisher = {Springer},
Year = {2012},
Editor = {Gr{\'e}goire Montavon and Genevieve B. Orr and Klaus-Robert M{\"u}ller},
Pages = {421--436},
Series = {{Lecture Notes in Computer Science}},
Volume = {7700},
ISBN = {978-3-642-35288-1},
Keywords = {dblp}
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@Article{breiman2001random,
Title = {{Random Forests}},
Author = {Leo Breiman},
Journal = {Machine Learning},
Year = {2001},
Pages = {5--32},
Volume = {45},
Added-at = {2012-10-21T11:54:04.000+0200},
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@Article{breiman1996bagging,
Title = {Bagging predictors},
Author = {Breiman, Leo},
Journal = {Machine learning},
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Number = {2},
Pages = {123--140},
Volume = {24},
Owner = {yani},
Publisher = {Springer},
Timestamp = {2016.10.28}
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@Book{breiman84,
Title = {{Classification and regression trees}},
Author = {Leo Breiman and Jerome Friedman and Charles J Stone and Richard A Olshen},
Publisher = {CRC press},
Year = {1984},
Owner = {yani},
Timestamp = {2016.08.07}
}
@InProceedings{Ciresan2012,
Title = {{Multi-column Deep Neural Networks for Image Classification}},
Author = {Dan Claudiu Ciresan and Ueli Meier and J{\"u}rgen Schmidhuber},
Booktitle = {{arXiv:1202.2745v1 [cs.CV]}},
Year = {2012},
__markedentry = {[yani:6]},
Owner = {yani},
Timestamp = {2016.07.31}
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@InProceedings{Cogswell2016,
Title = {{Reducing Overfitting in Deep Networks by Decorrelating Representations.}},
Author = {Michael Cogswell and Faruk Ahmed and Ross B. Girshick and Larry Zitnick and Dhruv Batra},
Booktitle = {{International Conference on Learning Representations}},
Year = {2016},
__markedentry = {[yani:6]},
Owner = {yani},
Timestamp = {2016.07.31}
}
@Article{journals/mcss/Cybenko92,
Title = {{Approximation by superpositions of a sigmoidal function.}},
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Pages = {455},
Volume = {5},
Added-at = {2013-09-30T00:00:00.000+0200},
Biburl = {http://www.bibsonomy.org/bibtex/2d9af5e76699fe5b1c4152990919bd15a/dblp},
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@Book{damelin2011,
Title = {The Mathematics of Signal Processing:},
Author = {Steven B. Damelin and Willard Miller, Jr},
Publisher = {Cambridge University Press},
Year = {2011},
Address = {Cambridge},
Month = {12},
Abstract = {Arising from courses taught by the authors, this largely self-contained treatment is ideal for mathematicians who are interested in applications or for students from applied fields who want to understand the mathematics behind their subject. Early chapters cover Fourier analysis, functional analysis, probability and linear algebra, all of which have been chosen to prepare the reader for the applications to come. The book includes rigorous proofs of core results in compressive sensing and wavelet convergence. Fundamental is the treatment of the linear system y=Φx in both finite and infinite dimensions. There are three possibilities: the system is determined, overdetermined or underdetermined, each with different aspects. The authors assume only basic familiarity with advanced calculus, linear algebra and matrix theory and modest familiarity with signal processing, so the book is accessible to students from the advanced undergraduate level. Many exercises are also included.},
Day = {15},
Doi = {10.1017/CBO9781139003896},
ISBN = {9781139003896},
Owner = {yani},
Timestamp = {2016.10.28},
Url = {https://www.cambridge.org/core/books/the-mathematics-of-signal-processing/A3F20BC5FBB820E923E66C8CCB13B173}
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@InProceedings{Deng2011fastbalanced,
Title = {{Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition}},
Author = {J. Deng and S. Satheesh and A. C. Berg and F.-F. Li},
Booktitle = {{Conference and Workshop on Neural Information Processing Systems}},
Year = {2011}
}
@InProceedings{Denil2013predicting,
Title = {{Predicting Parameters in Deep Learning}},
Author = {M. Denil and B. Shakibi and L. Dinh and M. A. Ranzato and N deFreitas},
Booktitle = {{eprint ar{X}iv:1306.0543v2}},
Year = {2013}
}
@InProceedings{Denton2014efficient,
Title = {{Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation}},
Author = {E. Denton and W. Zaremba and J. Bruna and Y. LeCun and R.Fergus},
Booktitle = {{eprint ar{X}iv:1404.0736v2}},
Year = {2014}
}
@Misc{fodor2002survey,
Title = {{A survey of dimension reduction techniques}},
Author = {Imola K Fodor},
Year = {2002},
Publisher = {Technical Report UCRL-ID-148494, Lawrence Livermore National Laboratory}
}
@Article{fukushima2013artificial,
Title = {{Artificial vision by multi-layered neural networks: Neocognitron and its advances}},
Author = {Kunihiko Fukushima},
Journal = {Neural Networks},
Year = {2013},
Pages = {103--119},
Volume = {37},
Publisher = {Elsevier}
}
@Article{Fuk80,
Title = {{Neocognitron: A self-organizing neural network model for a mechanish of pattern recognition unaffected by shifts in position}},
Author = {K. Fukushima},
Journal = {Biological Cybernetics},
Year = {1980},
Pages = {193--202},
Volume = {36},
Added-at = {2013-12-07T19:21:28.000+0100},
Biburl = {http://www.bibsonomy.org/bibtex/25b916ae8612805f9c0caaf04cf2abe46/prlz77},
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@InProceedings{girshick2015deformable,
Title = {{Deformable Part Models are Convolutional Neural Networks}},
Author = {Ross Girshick and Forrest Iandola and Trevor Darrell and Jitendra Malik},
Booktitle = {{Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}},
Year = {2015},
Pages = {437--446},
__markedentry = {[yani:]},
Owner = {yani},
Timestamp = {2016.07.27}
}
@InProceedings{glorot2010understanding,
Title = {{Understanding the difficulty of training deep feedforward neural networks}},
Author = {Xavier Glorot and Yoshua Bengio},
Booktitle = {{International conference on artificial intelligence and statistics}},
Year = {2010},
Pages = {249--256},
__markedentry = {[yani:]},
Owner = {yani},
Timestamp = {2016.07.27}
}
@InProceedings{goodfellow2013maxout,
Title = {{Maxout Networks}},
Author = {Ian Goodfellow and David Warde-farley and Mehdi Mirza and Aaron Courville and Yoshua Bengio},
Booktitle = {{Proceedings of the 30th International Conference on Machine Learning (ICML-13)}},
Year = {2013},
Pages = {1319--1327},
__markedentry = {[yani:]},
Owner = {yani},
Timestamp = {2016.07.27}
}
@Unpublished{Goodfellow-et-al-2016-Book,
Title = {Deep Learning},
Author = {Ian Goodfellow and Yoshua Bengio and Aaron Courville},
Note = {Book in preparation for MIT Press},
Year = {2016},
Owner = {yani},
Timestamp = {2016.10.27},
Url = {http://www.deeplearningbook.org}
}
@InProceedings{Gupta2015,
Title = {{Deep Learning with Limited Numerical Precision}},
Author = {Suyog Gupta and Ankur Agrawal and Kailash Gopalakrishnan and Pritish Narayanan},
Booktitle = {{Proceedings of the 32nd International Conference on Machine Learning (ICML-15)}},
Year = {2015},
Editor = {David Blei and Francis Bach},
Pages = {1737--1746},
Publisher = {JMLR Workshop and Conference Proceedings},
__markedentry = {[yani:6]},
Owner = {yani},
Timestamp = {2016.07.31}
}
@Misc{1502.02551v1,
Title = {{Deep Learning with Limited Numerical Precision}},
Author = {Suyog Gupta and Ankur Agrawal and Kailash Gopalakrishnan and Pritish Narayanan},
Month = feb,
Year = {2015},
__markedentry = {[yani:]},
Abstract = {Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of low-precision fixed-point computations, we observe the rounding scheme to play a crucial role in determining the network's behavior during training. Our results show that deep networks can be trained using only 16-bit wide fixed-point number representation when using stochastic rounding, and incur little to no degradation in the classification accuracy. We also demonstrate an energy-efficient hardware accelerator that implements low-precision fixed-point arithmetic with stochastic rounding.},
Archiveprefix = {arXiv},
Comment = {published = 2015-02-09T16:37:29Z, updated = 2015-02-09T16:37:29Z, 10 pages, 6 figures, 1 table},
Eprint = {1502.02551v1},
Owner = {yani},
Primaryclass = {cs.LG},
Timestamp = {2016.07.27},
X-fetchedfrom = {arXiv.org}
}
@Article{journals/iandc/HancockJLT96,
Title = {{Lower Bounds on Learning Decision Lists and Trees}},
Author = {Thomas R. Hancock and Tao Jiang and Ming Li and John Tromp},
Journal = {Inf. Comput.},
Year = {1996},
Number = {2},
Pages = {114--122},
Volume = {126},
Bibsource = {dblp computer science bibliography, http://dblp.org},
Biburl = {http://dblp.uni-trier.de/rec/bib/journals/iandc/HancockJLT96},
Doi = {10.1006/inco.1996.0040},
Owner = {yani},
Timestamp = {Thu, 31 Mar 2016 18:10:36 +0200},
Url = {http://dx.doi.org/10.1006/inco.1996.0040}
}
@Article{Happel1994,
Title = {{Design and evolution of modular neural network architectures.}},
Author = {Bart L. M. Happel and Jacob M. J. Murre},
Journal = {Neural Networks},
Year = {1994},
Number = {6-7},
Pages = {985--1004},
Volume = {7},
__markedentry = {[yani:6]},
Date = {2005-12-15},
Owner = {yani},
Timestamp = {2016.07.31}
}
@InProceedings{he2015convolutional,
Title = {{Convolutional Neural Networks at Constrained Time Cost}},
Author = {Kaiming He and Jian Sun},
Booktitle = {{Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}},
Year = {2015},
Pages = {5353--5360},
__markedentry = {[yani:]},
Owner = {yani},
Timestamp = {2016.07.27}
}
@Article{He2016,
Title = {{Identity Mappings in Deep Residual Networks}},
Author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
Year = {2016},
Volume = {abs/1603.05027},
__markedentry = {[yani:6]},
Owner = {yani},
Timestamp = {2016.07.31}
}
@Article{He2015,
Title = {{Deep Residual Learning for Image Recognition}},
Author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
Journal = {arXiv preprint arXiv:1512.03385},
Year = {2015},
__markedentry = {[yani:6]},
Owner = {yani},
Timestamp = {2016.07.31}
}
@Article{ieee7005506,
Title = {{Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition}},
Author = {K. He and X. Zhang and S. Ren and J. Sun},
Journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
Year = {2015},
Number = {99},
Pages = {1--1},
Volume = {PP},
Abstract = {Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224224) input image. This requirement is ``artificial'' and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, ``spatial pyramid pooling'', to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-theart classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102 faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank \#2 in object detection and \#3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.},
Arnumber = {7005506},
Doi = {10.1109/TPAMI.2015.2389824},
ISSN = {0162-8828},
Keywords = {Accuracy; Agriculture; Convolutional codes; Feature extraction; Testing; Training; Vectors; Convolutional Neural Networks; Image Classification; Object Detection; Spatial Pyramid Pooling},
X-fetchedfrom = {IEEEXplore}
}
@Article{hinton2006reducing,
Title = {Reducing the dimensionality of data with neural networks},
Author = {Hinton, Geoffrey E and Salakhutdinov, Ruslan R},
Journal = {Science},
Year = {2006},
Number = {5786},
Pages = {504--507},
Volume = {313},
Owner = {yani},
Publisher = {American Association for the Advancement of Science},
Timestamp = {2016.10.26}
}
@Misc{Hinton2012,
Title = {{Improving neural networks by preventing co-adaptation of feature detectors}},
Author = {Geoffrey E. Hinton and Nitish Srivastava and Alex Krizhevsky and Ilya Sutskever and Ruslan R. Salakhutdinov},
Month = jul,
Year = {2012},
__markedentry = {[yani:6]},
Archiveprefix = {arXiv},
Eprint = {1207.0580v1},
Owner = {yani},
Primaryclass = {cs.NE},
Timestamp = {2016.07.31},
X-fetchedfrom = {arXiv.org}
}
@PhdThesis{hochreiter1991untersuchungen,
Title = {Untersuchungen zu dynamischen neuronalen Netzen},
Author = {Hochreiter, Sepp},
School = {Technische Universit{\"a}t M{\"u}nchen},
Year = {1991},
Journal = {Diploma, Technische Universit{\"a}t M{\"u}nchen},
Pages = {91}
}
@Misc{Hochreiter01gradientflow,
Title = {{Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies}},
Author = {Sepp Hochreiter and Yoshua Bengio and Paolo Frasconi and J{\"u}rgen Schmidhuber},
Year = {2001},
__markedentry = {[yani:]},
Owner = {yani},
Timestamp = {2016.07.27}
}
@Article{hornik89a,
Title = {{Multilayer feedforward networks are universal approximators}},
Author = {K. Hornik and M. Stinchcombe and H. White},
Journal = {Neural Networks},
Year = {1989},
Pages = {356--366},
Volume = {2},
Abstract = {Thesis BIB},
Added-at = {2009-10-27T06:49:28.000+0100},
Biburl = {http://www.bibsonomy.org/bibtex/2895e5e2654209932aa72b1a31feb35b4/chrmina},
Interhash = {9948713640c726f50c4c5a57e121f7ac},
Intrahash = {895e5e2654209932aa72b1a31feb35b4},
Keywords = {imported},
X-fetchedfrom = {Bibsonomy}
}
@Article{ioannou2016deep,
Title = {{Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups}},
Author = {Yani Ioannou and Duncan Robertson and Roberto Cipolla and Antonio Criminisi},
Journal = {arXiv preprint arXiv:1605.06489},
Year = {2016}
}
@TechReport{Ioannou2015,
Title = {{Decision Forests, Convolutional Networks and the Models in-Between}},
Author = {Yani Ioannou and Duncan Robertson and Darko Zikic and Peter Kontschieder and Jamie Shotton and Matthew Brown and Antonio Criminisi},
Institution = {Microsoft Research},
Year = {2015},
Month = apr,
Number = {MSR-TR-2015-58},
__markedentry = {[yani:6]},
Archiveprefix = {arXiv},
Journal = {Technical Report},
Owner = {yani},
Timestamp = {2016.07.31}
}
@InProceedings{Ioannou2016,
Title = {{Training CNNs with Low-Rank Filters for Efficient Image Classification}},
Author = {Yani Ioannou and Duncan P. Robertson and Jamie Shotton and Roberto Cipolla and Antonio Criminisi},
Booktitle = {{International Conference on Learning Representations}},
Year = {2016},
__markedentry = {[yani:6]},
Owner = {yani},
Timestamp = {2016.07.31}
}
@InProceedings{Ioffe2015,
Title = {{Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.}},
Author = {Sergey Ioffe and Christian Szegedy},
Booktitle = {{Proceedings of the 32 nd International Conference on Machine Learning, Lille, France, 2015}},
Year = {2015},
__markedentry = {[yani:6]},
Owner = {yani},
Timestamp = {2016.07.31}
}
@InProceedings{journals/corr/JaderbergVZ14,
Title = {{Speeding up Convolutional Neural Networks with Low Rank Expansions.}},
Author = {Max Jaderberg and Andrea Vedaldi and Andrew Zisserman},
Booktitle = {{British Machine Vision Conference}},
Year = {2014},
__markedentry = {[yani:]},
Journal = {British Machine Vision Conference},
Keywords = {dblp},
Owner = {yani},
Timestamp = {2016.07.27}
}
@Article{Jhurani2015,
Title = {{A GEMM interface and implementation on NVIDIA GPUs for multiple small matrices}},
Author = {Chetan Jhurani and Paul Mullowney},
Journal = {Journal of Parallel and Distributed Computing},
Year = {2015},
Pages = {133--140},
Volume = {75},
__markedentry = {[yani:6]},
Owner = {yani},
Publisher = {Elsevier},
Timestamp = {2016.07.31}
}
@Article{Jia2014,
Title = {{Caffe: Convolutional Architecture for Fast Feature Embedding}},
Author = {Yangqing Jia and Evan Shelhamer and Jeff Donahue and Sergey Karayev and Jonathan Long and Ross Girshick and Sergio Guadarrama and Trevor Darrell},
Journal = {arXiv preprint arXiv:1408.5093},
Year = {2014},
__markedentry = {[yani:6]},
Owner = {yani},
Timestamp = {2016.07.31}
}
@Article{johnson1984extensions,
Title = {{Extensions of Lipschhitz maps into a Hilbert space}},
Author = {W Johnson and J Lindenstrauss},
Journal = {Contemporary Math},
Year = {1984},
Volume = {26},
Owner = {yani},
Timestamp = {2016.10.12}
}
@InProceedings{kaski1998dimensionality,
Title = {{Dimensionality reduction by random mapping: Fast similarity computation for clustering}},
Author = {Samuel Kaski},
Booktitle = {{Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on}},
Year = {1998},
Organization = {IEEE},
Pages = {413--418},
Volume = {1}
}
@InProceedings{Kim2016,
Title = {{Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications}},
Author = {Yong{-}Deok Kim and Eunhyeok Park and Sungjoo Yoo and Taelim Choi and Lu Yang and Dongjun Shin},
Booktitle = {{International Conference on Learning Representations}},
Year = {2016},
__markedentry = {[yani:6]},
Owner = {yani},
Timestamp = {2016.07.31}
}
@InProceedings{Krizhevsky2014,
Title = {{One Weird Trick for Parallelizing Convolutional Neural Networks}},
Author = {A. Krizhevsky},
Booktitle = {{eprint ar{X}iv:1404.5997v2}},
Year = {2014}
}
@Article{Krizhevsky2014a,
Title = {{One weird trick for parallelizing convolutional neural networks}},
Author = {Alex Krizhevsky},
Journal = {arXiv preprint arXiv:1404.5997},
Year = {2014},
__markedentry = {[yani:6]},
Owner = {yani},
Timestamp = {2016.07.31}
}
@TechReport{CIFAR10,
Title = {{Learning Multiple Layers of Features from Tiny Images}},
Author = {A. Krizhevsky},
Institution = {Univ. Toronto},
Year = {2009},
Type = {Technical Report}
}
@InProceedings{Krizhevsky2012imanet,
Title = {{Image{N}et Classification with Deep Convolutional Neural Networks}},
Author = {A. Krizhevsky and I. Sutskever and G. Hinton},
Year = {2012}
}
@Article{larsen2016optimality,
Title = {{Optimality of the Johnson-Lindenstrauss Lemma}},
Author = {Kasper Green Larsen and Jelani Nelson},
Journal = {arXiv preprint arXiv:1609.02094},
Year = {2016}
}
@InProceedings{Lebedev2015,
Title = {{Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition.}},
Author = {V. Lebedev and Y. Ganin and M. Rakhuba and I. Oseledets and V. Lempitsky},
Booktitle = {{International Conference on Learning Representations}},
Year = {2015},
__markedentry = {[yani:6]},
Owner = {yani},
Timestamp = {2016.07.31}
}
@Article{journals/corr/LebedevGROL14,
Title = {{Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition.}},
Author = {Vadim Lebedev and Yaroslav Ganin and Maksim Rakhuba and Ivan V. Oseledets and Victor S. Lempitsky},
Journal = {CoRR},
Year = {2014},
Volume = {abs/1412.6553},
__markedentry = {[yani:]},
Added-at = {2015-01-01T00:00:00.000+0100},
Ee = {http://arxiv.org/abs/1412.6553},
Interhash = {25af9f394cbece05c7cee601a306de7c},
Intrahash = {199e5821916b1eec932dde3ad74c8d97},
Keywords = {dblp},
Owner = {yani},
Timestamp = {2016.07.27},
X-fetchedfrom = {Bibsonomy}
}
@Article{Lecun1998,
Title = {{Gradient-based learning applied to document recognition}},
Author = {Y. Lecun and L. Bottou and Y. Bengio and P. Haffner},
Journal = {Proceedings of the IEEE},
Year = {1998},
Number = {11},
Pages = {2278--2324},
Volume = {86},
Abstract = {Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day},
Added-at = {2009-09-12T19:19:34.000+0200},
Biburl = {http://www.bibsonomy.org/bibtex/24d0e761be0edc4f631b3af679c2b33ab/mozaher},
File = {00726791.pdf:Lecun1998.pdf:PDF},
Interhash = {7a82cccacd23cf06b25ff5325a6c86c7},
Intrahash = {4d0e761be0edc4f631b3af679c2b33ab},
ISSN = {0018-9219},
Keywords = {2D GTN; back-propagation; backpropagation; based character cheque complex convolution; convolutional decision digit document extraction; field gradient gradient-based graph handwritten high-dimensional language learning learning; measure minimization; modeling; multilayer multimodule network networks; neural optical patterns; perceptrons; performance reading; recognition recognition; recognizers; segmentation shape surface synthesis; systems; task; technique; transformer variability},
Owner = {mozaher},
Timestamp = {2007.05.15},
X-fetchedfrom = {Bibsonomy}
}
@InProceedings{lecun1989optimal,
Title = {{Optimal brain damage.}},
Author = {Yann LeCun and John S Denker and Sara A Solla and Richard E Howard and Lawrence D Jackel},
Booktitle = {{NIPs}},
Year = {1989},
Volume = {89},
__markedentry = {[yani:]},
Owner = {yani},
Timestamp = {2016.07.27}
}
@Article{lee2014deeply,
Title = {{Deeply-supervised nets}},
Author = {Chen-Yu Lee and Saining Xie and Patrick Gallagher and Zhengyou Zhang and Zhuowen Tu},
Journal = {arXiv preprint arXiv:1409.5185},
Year = {2014}
}
@InProceedings{Lin2014,
Title = {{Network in network}},
Author = {Min Lin and Qiang Chen and Shuicheng Yan},
Booktitle = {{International Conference on Learning Representations}},
Year = {2014},
Series = {{2014}},
__markedentry = {[yani:6]},
Owner = {yani},
Timestamp = {2016.07.31}
}
@Article{Lin2013NiN,
Title = {{Network In Network.}},
Author = {Min Lin and Qiang Chen and Shuicheng Yan},
Journal = {CoRR},
Year = {2013},
Volume = {abs/1312.4400},
Added-at = {2014-06-05T19:00:44.000+0200},
Interhash = {67da1ef3d0d91a16dbe3ec25474b9aaf},
Intrahash = {78171ed7a70a99e98b4e320a5b32b837},
Keywords = {In Network},
Url = {http://dblp.uni-trier.de/db/journals/corr/corr1312.html#LinCY13; http://arxiv.org/abs/1312.4400; http://www.bibsonomy.org/bibtex/278171ed7a70a99e98b4e320a5b32b837/prlz77},
X-fetchedfrom = {Bibsonomy}
}
@InProceedings{Luo2010switchable,
Title = {{Switchable Deep Networks for Pedestrian Detection}},
Author = {P. Luo and Y. Tian and X. Wang and X. Tang},
Booktitle = {{Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition}},
Year = {2010}
}
@InCollection{mamalet2012simplifying,
Title = {{Simplifying convnets for fast learning}},
Author = {Franck Mamalet and Christophe Garcia},
Booktitle = {{Artificial Neural Networks and Machine Learning--ICANN 2012}},
Publisher = {Springer},
Year = {2012},
Pages = {58--65},
__markedentry = {[yani:]},
Owner = {yani},
Timestamp = {2016.07.27}
}
@InProceedings{martens2010deep,
Title = {{Deep learning via Hessian-free optimization}},
Author = {James Martens},
Booktitle = {{Proceedings of the 27th International Conference on Machine Learning (ICML-10)}},
Year = {2010},
Pages = {735--742}
}
@InProceedings{Mathieu2014,
Title = {{Fast training of convolutional networks through {FFTs}}},
Author = {Michael Mathieu and Mikael Henaff and Yann LeCun},
Booktitle = {{International Conference on Learning Representations}},
Year = {2014},
__markedentry = {[yani:6]},
Owner = {yani},
Timestamp = {2016.07.31}
}
@Article{mathieu2013fast,
Title = {{Fast training of convolutional networks through {FFTs}}},
Author = {Michael Mathieu and Mikael Henaff and Yann LeCun},
Journal = {arXiv preprint arXiv:1312.5851},
Year = {2013},
__markedentry = {[yani:]},
Owner = {yani},
Timestamp = {2016.07.27}
}
@Book{minsky1988perceptrons,
Title = {{Perceptrons}},
Author = {Marvin Minsky and Seymour Papert},
Publisher = {MIT press},
Year = {1988}
}
@InProceedings{montillo2011entangled,
Title = {{Entangled decision forests and their application for semantic segmentation of {CT} images}},
Author = {A. Montillo and J. Shotton and J. Winn and J. Iglesias and D. Metaxas and A. Criminisi},
Year = {2011}
}
@InProceedings{Oquab:2014:LTM:2679600.2680210,
Title = {{Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks}},
Author = {Maxime Oquab and Leon Bottou and Ivan Laptev and Josef Sivic},
Booktitle = {{Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition}},
Year = {2014},
Address = {Washington, DC, USA},
Pages = {1717--1724},
Publisher = {IEEE Computer Society},
Series = {{CVPR '14}},
Acmid = {2680210},
Doi = {10.1109/CVPR.2014.222},
ISBN = {978-1-4799-5118-5},
Numpages = {8},
Url = {http://dx.doi.org/10.1109/CVPR.2014.222},
X-fetchedfrom = {ACM Digital Library}
}
@InProceedings{Wu2015scalingup,
Title = {{Deep Image: Scaling up Image Recognition}},
Author = {R.Wu and S.Yan and Y.Shan and Q.Dang and G.Sun},
Booktitle = {{eprint ar{X}iv:1501.02876v2}},
Year = {2015}
}
@Article{ren2015noc,
Title = {{Object Detection Networks on Convolutional Feature Maps}},
Author = {Shaoqing Ren and Kaiming He and Ross Girshick and Xiangyu Zhang and Jian Sun},
Journal = {arXiv preprint arXiv:1504.06066},
Year = {2015},
__markedentry = {[yani:]},
Owner = {yani},
Timestamp = {2016.07.27}
}
@InProceedings{Rippel2015,
Title = {{Spectral Representations for Convolutional Neural Networks}},
Author = {Oren Rippel and Jasper Snoek and Ryan P Adams},
Booktitle = {{Advances in Neural Information Processing Systems}},
Year = {2015},
Pages = {2440--2448},
__markedentry = {[yani:6]},
Owner = {yani},
Timestamp = {2016.07.31}
}
@Article{rippel2015spectral,
Title = {{Spectral Representations for Convolutional Neural Networks}},
Author = {Oren Rippel and Jasper Snoek and Ryan P Adams},
Journal = {arXiv preprint arXiv:1506.03767},
Year = {2015},
__markedentry = {[yani:]},
Owner = {yani},
Timestamp = {2016.07.27}
}
@InProceedings{BuloKontsch2014,
Title = {{Neural Decision Forests for Semantic Image Labelling}},
Author = {S. {Rota Bul{\`o}} and P. Kontschieder},
Booktitle = {{Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition}},
Year = {2014},
Month = {June}
}
@Article{ILSVRC2015,
Title = {{ImageNet Large Scale Visual Recognition Challenge}},
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Journal = {International Journal of Computer Vision (IJCV)},
Year = {2015},
Doi = {10.1007/s11263-015-0816-y}
}
@InProceedings{Sermanet2013overfeat,
Title = {{OverFeat: Integrated Recognition, localization and Detection using Convolutional Networks}},
Author = {P. Sermanet and D. Eigen and X. Zhang and M. Mathieu and R. Fergus and Y. LeCun},
Booktitle = {{eprint ar{X}iv:1312.6229}},
Year = {2013}