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% This file was created with JabRef 2.10.
% Encoding: UTF-8
@IEEEtranBSTCTL{IEEEexample:BSTcontrol,
CTLuse_forced_etal = "yes",
CTLmax_names_forced_etal = "3",
CTLnames_show_etal = "2" }
@Article{abadi2016tensorflow,
Title = {Tensorflow: {Large-scale} machine learning on heterogeneous distributed systems},
Author = {Abadi, Mart{\'\i}n and Agarwal, Ashish and Barham, Paul and Brevdo, Eugene and Chen, Zhifeng and Citro, Craig and Corrado, Greg S and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and others},
Journal = {arXiv preprint arXiv:1603.04467},
Year = {2016},
Month = mar,
Url = {https://arxiv.org/abs/1603.04467}
}
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Title = {Inceptionism: {Going} Deeper into Neural Networks},
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Month = jun,
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Title = {Learning to learn by gradient descent by gradient descent},
Author = {Andrychowicz, Marcin and Denil, Misha and G\'{o}mez, Sergio and Hoffman, Matthew W and Pfau, David and Schaul, Tom and de Freitas, Nando},
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Title = {{CVAE-GAN}: {Fine}-Grained Image Generation through Asymmetric Training},
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Year = {2017},
Month = mar,
Url = {https://arxiv.org/abs/1703.10155}
}
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Url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/}
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Title = {Deep learning with s-shaped rectified linear activation units},
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Title = {Inception-v4, inception-resnet and the impact of residual connections on learning},
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@InProceedings{GoogleNet-Inception,
Title = {Going deeper with convolutions},
Author = {Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew},
Booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
Year = {2015},
Month = sep,
Organization = {IEEE},
Pages = {1--9},
Url = {https://arxiv.org/abs/1409.4842}
}
@Article{Inception-v3,
Title = {Rethinking the inception architecture for computer vision},
Author = {Szegedy, Christian and Vanhoucke, Vincent and Ioffe, Sergey and Shlens, Jonathon and Wojna, Zbigniew},
Journal = {arXiv preprint arXiv:1512.00567},
Year = {2015},
Month = dec,
Url = {https://arxiv.org/abs/1512.00567v3}
}
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Title = {Intriguing properties of neural networks},
Author = {Szegedy, Christian and Zaremba, Wojciech and Sutskever, Ilya and Bruna, Joan and Erhan, Dumitru and Goodfellow, Ian and Fergus, Rob},
Journal = {arXiv preprint arXiv:1312.6199},
Year = {2013},
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Url = {https://arxiv.org/abs/1312.6199v4}
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Title = {The {HASYv2} dataset},
Author = {Thoma, Martin},
Journal = {arXiv preprint arXiv:1701.08380},
Year = {2017},
Month = jan,
Url = {https://arxiv.org/abs/1701.08380}
}
@Misc{thoma-msthesis-blog,
Title = {Master Thesis (Blog post)},
Author = {Martin Thoma},
Month = apr,
Year = {2017},
Url = {https://martin-thoma.com/msthesis}
}
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Title = {A Survey of Semantic Segmentation},
Author = {Martin Thoma},
Journal = {arXiv preprint arXiv:1602.06541},
Year = {2016},
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Author = {Martin Thoma},
Month = sep,
Year = {2014},
Url = {https://martin-thoma.com/twiddle/}
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@Misc{Thoma:2014,
Title = {On-line Recognition of Handwritten Mathematical Symbols},
Author = {Martin Thoma},
Month = nov,
Year = {2014},
Address = {Karlsruhe, Germany},
Keywords = {handwriting recognition; on-line; machine learning;
artificial neural networks; mathematics; classification;
supervised learning; MLP; multilayer perceptrons; hwrt;
write-math},
School = {Karlsruhe Institute of Technology},
Type = {{B.S. Thesis}},
Url = {http://martin-thoma.com/write-math}
}
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Address = {Berlin, Heidelberg},
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Year = {2014},
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Pages = {818--833},
Url = {https://arxiv.org/abs/1311.2901}
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Year = {2016},
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Pages = {1082--1090},
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}
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Title = {Understanding deep learning requires rethinking generalization},
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Year = {2016},
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}
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Year = {2014},
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Pages = {834--849},
Url = {https://arxiv.org/abs/1407.3867}
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Title = {Stacked what-where auto-encoders},
Author = {Zhao, Junbo and Mathieu, Michael and Goroshin, Ross and Lecun, Yann},
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Year = {2015},
Month = jun,
Url = {https://arxiv.org/abs/1506.02351v1}
}
@InProceedings{7280459,
Title = {Improving deep neural networks using softplus units},
Author = {Hao Zheng and Zhanlei Yang and Wenju Liu and Jizhong Liang and Yanpeng Li},
Booktitle = {International Joint Conference on Neural Networks (IJCNN)},
Year = {2015},
Month = jul,
Pages = {1-4},
Abstract = {Recently, DNNs have achieved great improvement for acoustic modeling in speech recognition tasks. However, it is difficult to train the models well when the depth grows. One main reason is that when training DNNs with traditional sigmoid units, the derivatives damp sharply while back-propagating between layers, which restrict the depth of model especially with insufficient training data. To deal with this problem, some unbounded activation functions have been proposed to preserve sufficient gradients, including ReLU and softplus. Compared with ReLU, the smoothing and nonzero properties of the in gradient makes softplus-based DNNs perform better in both stabilization and performance. However, softplus-based DNNs have been rarely exploited for the phoneme recognition task. In this paper, we explore the use of softplus units for DNNs in acoustic modeling for context-independent phoneme recognition tasks. The revised RBM pre-training and dropout strategy are also applied to improve the performance of softplus units. Experiments show that, the DNNs with softplus units get significantly performance improvement and uses less epochs to get convergence compared to the DNNs trained with standard sigmoid units and ReLUs.},
Doi = {10.1109/IJCNN.2015.7280459},
ISSN = {2161-4393},
Keywords = {backpropagation;neural nets;speech recognition;DNN data training;ReLU;acoustic modeling;backpropagation;context-independent phoneme recognition tasks;deep neural networks;dropout strategy;revised RBM pre-training;sigmoid units;softplus units;speech recognition tasks;unbounded activation functions;Speech;TIMIT;deep neural networks;dropout;softplus}
}
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Title = {Places2 Download},
Author = {Bolei Zhou},
Year = {2016},
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Title = {Learning Deep Features for Discriminative Localization},
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Journal = {arXiv preprint arXiv:1512.04150},
Year = {2015},
Month = dec,
Url = {https://arxiv.org/abs/1512.04150}
}
@Article{zhou2016places,
Title = {Places: {An} Image Database for Deep Scene Understanding},
Author = {Zhou, Bolei and Khosla, Aditya and Lapedriza, Agata and Torralba, Antonio and Oliva, Aude},
Journal = {arXiv preprint arXiv:1610.02055},
Year = {2016},
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Url = {https://arxiv.org/abs/1610.02055}
}
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Title = {Neural architecture search with reinforcement learning},
Author = {Zoph, Barret and Le, Quoc V},
Journal = {arXiv preprint arXiv:1611.01578},
Year = {2016},
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Url = {https://arxiv.org/abs/1611.01578}
}
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Title = {Kaggle Cats and Dogs Dataset},
Month = oct,
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Url = {https://www.microsoft.com/en-us/download/details.aspx?id=54765}
}
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Title = {Noise layers},
Month = jan,
Year = {2017},
Url = {http://lasagne.readthedocs.io/en/latest/modules/layers/noise.html#lasagne.layers.DropoutLayer}
}
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Title = {tf.nn.dropout},
Month = dec,
Year = {2016},
Url = {https://www.tensorflow.org/api_docs/python/nn/activation_functions_#dropout}
}
@Misc{TF-MNIST-2016,
Title = {{MNIST} For {ML} Beginners},
Month = dec,
Year = {2016},
Url = {https://www.tensorflow.org/tutorials/mnist/beginners/}
}
@Misc{ImageNet-download,
Title = {ImageNet Large Scale Visual Recognition Challenge 2012 ({ILSVRC2012})},
Year = {2012},
Url = {http://www.image-net.org/challenges/LSVRC/2012/nonpub-downloads}
}
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Title = {The training performed by qnstrn},
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