@inproceedings {hubara2016binarized,
	title = {Binarized neural networks},
	year = {2016},
	pages = {4107{\textendash}4115},
	keywords = {Deep Learning, ref},
	author = {Hubara, Itay and Courbariaux, Matthieu and Soudry, Daniel and El-Yaniv, Ran and Bengio, Yoshua}
}
@article {courbariaux2016binarized,
	title = {Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1},
	journal = {arXiv preprint arXiv:1602.02830},
	year = {2016},
	keywords = {Deep Learning, ref},
	author = {Courbariaux, Matthieu and Hubara, Itay and Soudry, Daniel and El-Yaniv, Ran and Bengio, Yoshua}
}
@article {kim2016bitwise,
	title = {Bitwise neural networks},
	journal = {arXiv preprint arXiv:1601.06071},
	year = {2016},
	keywords = {Deep Learning, ref},
	author = {Kim, Minje and Smaragdis, Paris}
}
@article {palangi2016distributed,
	title = {Distributed Compressive Sensing: A Deep Learning Approach.},
	journal = {IEEE Trans. Signal Processing},
	volume = {64},
	number = {17},
	year = {2016},
	pages = {4504{\textendash}4518},
	keywords = {Compressive Sensing, ref},
	author = {Palangi, Hamid and Ward, Rabab K and Deng, Li}
}
@inproceedings {kulkarni2016reconnet,
	title = {Reconnet: Non-iterative reconstruction of images from compressively sensed measurements},
	year = {2016},
	pages = {449{\textendash}458},
	keywords = {Compressive Sensing, ref},
	author = {Kulkarni, Kuldeep and Lohit, Suhas and Turaga, Pavan and Kerviche, Ronan and Ashok, Amit}
}
@inproceedings {rastegari2016xnor,
	title = {Xnor-net: Imagenet classification using binary convolutional neural networks},
	year = {2016},
	pages = {525{\textendash}542},
	publisher = {Springer},
	keywords = {Deep Learning, ref},
	author = {Rastegari, Mohammad and Ordonez, Vicente and Redmon, Joseph and Farhadi, Ali}
}
@inproceedings {courbariaux2015binaryconnect,
	title = {Binaryconnect: Training deep neural networks with binary weights during propagations},
	year = {2015},
	pages = {3123{\textendash}3131},
	keywords = {Deep Learning, ref},
	author = {Courbariaux, Matthieu and Bengio, Yoshua and David, Jean-Pierre}
}
@article {chen2015compressed,
	title = {Compressed sensing and dictionary learning},
	journal = {Preprint},
	volume = {106},
	year = {2015},
	keywords = {Compressive Sensing, ref},
	author = {Chen, Guangliang and Needell, Deanna}
}
@article {lecun2015deep,
	title = {Deep learning},
	journal = {Nature},
	volume = {521},
	number = {7553},
	year = {2015},
	pages = {436{\textendash}444},
	publisher = {Nature Research},
	keywords = {Deep Learning, ref},
	author = {LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey}
}
@inproceedings {mousavi2015deep,
	title = {A deep learning approach to structured signal recovery},
	year = {2015},
	pages = {1336{\textendash}1343},
	publisher = {IEEE},
	keywords = {Compressive Sensing, ref},
	author = {Mousavi, Ali and Patel, Ankit B and Baraniuk, Richard G}
}
@article {schmidhuber2015deep,
	title = {Deep learning in neural networks: An overview},
	journal = {Neural networks},
	volume = {61},
	year = {2015},
	pages = {85{\textendash}117},
	publisher = {Elsevier},
	keywords = {Deep Learning, ref},
	author = {Schmidhuber, J{\"u}rgen}
}
@inproceedings {gupta2015deep,
	title = {Deep learning with limited numerical precision},
	year = {2015},
	pages = {1737{\textendash}1746},
	keywords = {Deep Learning, ref},
	author = {Gupta, Suyog and Agrawal, Ankur and Gopalakrishnan, Kailash and Narayanan, Pritish}
}
@inproceedings {278,
	title = {Enabling scientific data storage and processing on big-data systems},
	year = {2015},
	pages = {1978{\textendash}1984},
	publisher = {IEEE},
	abstract = {<p>Big-data systems are increasingly important for solving the data-driven problems in many science domains including geosciences. However, existing big-data systems cannot support the self-describing data formats such as NetCDF which are commonly used by scientific communities for data distribution and sharing. This limitation presents a serious hurdle to the further adoption of big-data systems by science domains and prevents scientific users from leveraging these systems to improve their productivity. This paper presents a solution to this problem by enabling big-data systems to directly store and process scientific data. Specifically, it enables Hadoop to efficiently store NetCDF data on HDFS and process them in MapReduce using convenient APIs. It also enables Hive to support standard queries on NetCDF data, transparently to users. The paper also presents an evaluation of the proposed solution using several representative queries on a typical geoscientific dataset. The results show that the proposed approach achieves substantial speedup (up to 20 times) and space saving (83\% reduction), compared to the traditional approach which has to convert NetCDF data to CSV format for Hadoop and Hive to use them.</p>
},
	keywords = {ref},
	author = {Biookaghazadeh, Saman and Xu, Yiqi and Zhou, Shujia and Zhao, Ming}
}
@inproceedings {szegedy2015going,
	title = {Going deeper with convolutions},
	year = {2015},
	pages = {1{\textendash}9},
	keywords = {Deep Learning, ref},
	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}
}
@article {dally2015high,
	title = {High-performance hardware for machine learning},
	journal = {NIPS Tutorial},
	year = {2015},
	keywords = {Deep Learning, ref},
	url = {https://www.microsoft.com/en-us/research/video/tutorial-high-performance-hardware-for-machine-learning/?from=http\%3A\%2F\%2Fresearch.microsoft.com\%2Fapps\%2Fvideo\%2F\%3Fid\%3D259571},
	author = {Dally, William}
}
@article {hegde2015numax,
	title = {NuMax: A convex approach for learning near-isometric linear embeddings},
	journal = {IEEE Transactions on Signal Processing},
	volume = {63},
	number = {22},
	year = {2015},
	pages = {6109{\textendash}6121},
	publisher = {IEEE},
	keywords = {Compressive Sensing, ref},
	author = {Hegde, Chinmay and Sankaranarayanan, Aswin C and Yin, Wotao and Baraniuk, Richard G}
}
@inproceedings {wang2015optimizing,
	title = {Optimizing boolean embedding matrix for compressive sensing in rram crossbar},
	year = {2015},
	pages = {13{\textendash}18},
	publisher = {IEEE},
	keywords = {Compressive Sensing, ref},
	author = {Wang, Yuhao and Li, Xin and Yu, Hao and Ni, Leibin and Yang, Wei and Weng, Chuliang and Zhao, Junfeng}
}
@inproceedings {zhang2015optimizing,
	title = {Optimizing fpga-based accelerator design for deep convolutional neural networks},
	year = {2015},
	pages = {161{\textendash}170},
	publisher = {ACM},
	keywords = {Deep Learning, ref},
	author = {Zhang, Chen and Li, Peng and Sun, Guangyu and Guan, Yijin and Xiao, Bingjun and Cong, Jason}
}
@article {putnam2015reconfigurable,
	title = {A reconfigurable fabric for accelerating large-scale datacenter services},
	journal = {IEEE Micro},
	volume = {35},
	number = {3},
	year = {2015},
	pages = {10{\textendash}22},
	publisher = {IEEE},
	keywords = {Deep Learning, ref},
	author = {Putnam, Andrew and Caulfield, Adrian M and Chung, Eric S and Chiou, Derek and Constantinides, Kypros and Demme, John and Esmaeilzadeh, Hadi and Fowers, Jeremy and Gopal, Gopi Prashanth and Gray, Jan and others}
}
@inproceedings {shen2014compressed,
	title = {Compressed Sensing Image Reconstruction Algorithm by Dictionary Learning},
	year = {2014},
	pages = {193},
	publisher = {ACM},
	keywords = {Compressive Sensing, ref},
	author = {Shen, Yanfei and Zhu, Guangyu and Li, Jintao and Zhu, Zhenmin}
}
@inproceedings {chen2014dadiannao,
	title = {Dadiannao: A machine-learning supercomputer},
	year = {2014},
	pages = {609{\textendash}622},
	publisher = {IEEE Computer Society},
	keywords = {Deep Learning, ref},
	author = {Chen, Yunji and Luo, Tao and Liu, Shaoli and Zhang, Shijin and He, Liqiang and Wang, Jia and Li, Ling and Chen, Tianshi and Xu, Zhiwei and Sun, Ninghui and others}
}
@inproceedings {chen2014diannao,
	title = {Diannao: A small-footprint high-throughput accelerator for ubiquitous machine-learning},
	volume = {49},
	number = {4},
	year = {2014},
	pages = {269{\textendash}284},
	publisher = {ACM},
	keywords = {Deep Learning, ref},
	author = {Chen, Tianshi and Du, Zidong and Sun, Ninghui and Wang, Jia and Wu, Chengyong and Chen, Yunji and Temam, Olivier}
}
@inproceedings {ouyang2014sda,
	title = {SDA: Software-defined accelerator for large-scale DNN systems},
	year = {2014},
	pages = {1{\textendash}23},
	publisher = {IEEE},
	keywords = {Deep Learning, ref},
	author = {Ouyang, Jian and Lin, Shiding and Qi, Wei and Wang, Yong and Yu, Bo and Jiang, Song}
}
@article {qaisar2013compressive,
	title = {Compressive sensing: From theory to applications, a survey},
	journal = {Journal of Communications and networks},
	volume = {15},
	number = {5},
	year = {2013},
	pages = {443{\textendash}456},
	publisher = {IEEE},
	keywords = {Compressive Sensing, ref},
	author = {Qaisar, Saad and Bilal, Rana Muhammad and Iqbal, Wafa and Naureen, Muqaddas and Lee, Sungyoung}
}
@article {nam2013cosparse,
	title = {The cosparse analysis model and algorithms},
	journal = {Applied and Computational Harmonic Analysis},
	volume = {34},
	number = {1},
	year = {2013},
	pages = {30{\textendash}56},
	publisher = {Elsevier},
	keywords = {Compressive Sensing, ref},
	url = {http://www.small-project.eu/index.html},
	author = {Nam, Sangnam and Davies, Mike E and Elad, Michael and Gribonval, R{\'e}mi}
}
@book {james2013introduction,
	title = {An introduction to statistical learning},
	volume = {112},
	year = {2013},
	publisher = {Springer},
	organization = {Springer},
	keywords = {Deep Learning, ref},
	url = {https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about},
	author = {James, Gareth and Witten, Daniela and Hastie, Trevor and Tibshirani, Robert}
}
@inproceedings {palangi2013using,
	title = {Using deep stacking network to improve structured compressed sensing with Multiple Measurement Vectors.},
	year = {2013},
	pages = {3337{\textendash}3341},
	keywords = {Compressive Sensing, ref},
	author = {Palangi, Hamid and Ward, Rabab K and Deng, Li}
}
@inproceedings {157,
	title = {From OpenCL to high-performance hardware on FPGAs},
	year = {2012},
	pages = {531{\textendash}534},
	publisher = {IEEE},
	keywords = {Parallel and Reconfigurable Computing, ref},
	author = {Czajkowski, Tomasz S and Aydonat, Utku and Denisenko, Dmitry and Freeman, John and Kinsner, Michael and Neto, David and Wong, Jason and Yiannacouras, Peter and Singh, Deshanand P}
}
@inproceedings {krizhevsky2012imagenet,
	title = {Imagenet classification with deep convolutional neural networks},
	year = {2012},
	pages = {1097{\textendash}1105},
	keywords = {Deep Learning, ref},
	author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E}
}
@inbook {bengio2012practical,
	title = {Practical recommendations for gradient-based training of deep architectures},
	booktitle = {Neural networks: Tricks of the trade},
	year = {2012},
	pages = {437{\textendash}478},
	publisher = {Springer},
	organization = {Springer},
	keywords = {Deep Learning, ref},
	author = {Bengio, Yoshua}
}
@article {davenport2011introduction,
	title = {Introduction to compressed sensing},
	journal = {preprint},
	volume = {93},
	number = {1},
	year = {2011},
	pages = {2},
	keywords = {ref},
	author = {Davenport, Mark A and Duarte, Marco F and Eldar, Yonina C and Kutyniok, Gitta}
}
@article {davenport2011introduction,
	title = {Introduction to compressed sensing},
	journal = {preprint},
	volume = {93},
	number = {1},
	year = {2011},
	pages = {2},
	keywords = {Compressive Sensing, ref},
	author = {Davenport, Mark A and Duarte, Marco F and Eldar, Yonina C and Kutyniok, Gitta}
}
@article {needell2010cosamp,
	title = {Cosamp: iterative signal recovery from incomplete and inaccurate samples},
	journal = {Communications of the ACM},
	volume = {53},
	number = {12},
	year = {2010},
	pages = {93{\textendash}100},
	publisher = {ACM},
	keywords = {Compressive Sensing, ref},
	author = {Needell, Deanna and Tropp, Joel A}
}
@inproceedings {zinkevich2010parallelized,
	title = {Parallelized stochastic gradient descent},
	year = {2010},
	pages = {2595{\textendash}2603},
	keywords = {Deep Learning, ref},
	author = {Zinkevich, Martin and Weimer, Markus and Li, Lihong and Smola, Alex J}
}
@article {duarte2009learning,
	title = {Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization},
	journal = {IEEE Transactions on Image Processing},
	volume = {18},
	number = {7},
	year = {2009},
	pages = {1395{\textendash}1408},
	publisher = {IEEE},
	keywords = {Compressive Sensing, ref},
	author = {Duarte-Carvajalino, Julio Martin and Sapiro, Guillermo}
}
@inproceedings {perez2009supervised,
	title = {A supervised learning method for neural networks based on sensitivity analysis with automatic regularization},
	year = {2009},
	pages = {157{\textendash}164},
	publisher = {Springer},
	keywords = {Deep Learning, ref},
	author = {P{\'e}rez-S{\'a}nchez, Beatriz and Fontenla-Romero, Oscar and Guijarro-Berdi{\~n}as, Bertha}
}
@article {donoho2008fast,
	title = {Fast solution of $$\backslash$ell \_ $\{$1$\}$ $-norm minimization problems when the solution may be sparse},
	journal = {IEEE Transactions on Information Theory},
	volume = {54},
	number = {11},
	year = {2008},
	pages = {4789{\textendash}4812},
	publisher = {IEEE},
	keywords = {Compressive Sensing, ref},
	author = {Donoho, David L and Tsaig, Yaakov}
}
@article {candes2008introduction,
	title = {An introduction to compressive sampling},
	journal = {IEEE signal processing magazine},
	volume = {25},
	number = {2},
	year = {2008},
	pages = {21{\textendash}30},
	publisher = {IEEE},
	keywords = {Compressive Sensing, ref},
	author = {Cand{\`e}s, Emmanuel J and Wakin, Michael B}
}
@article {candes2008introduction,
	title = {An introduction to compressive sampling},
	journal = {IEEE signal processing magazine},
	volume = {25},
	number = {2},
	year = {2008},
	pages = {21{\textendash}30},
	publisher = {IEEE},
	keywords = {ref},
	author = {Cand{\`e}s, Emmanuel J and Wakin, Michael B}
}
@article {figueiredo2007gradient,
	title = {Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems},
	journal = {IEEE Journal of selected topics in signal processing},
	volume = {1},
	number = {4},
	year = {2007},
	pages = {586{\textendash}597},
	publisher = {IEEE},
	keywords = {Compressive Sensing, ref},
	author = {Figueiredo, M{\'a}rio AT and Nowak, Robert D and Wright, Stephen J}
}
@article {tropp2007signal,
	title = {Signal recovery from random measurements via orthogonal matching pursuit},
	journal = {IEEE Transactions on information theory},
	volume = {53},
	number = {12},
	year = {2007},
	pages = {4655{\textendash}4666},
	publisher = {IEEE},
	keywords = {Compressive Sensing, ref},
	author = {Tropp, Joel A and Gilbert, Anna C}
}
@book {friedman2001elements,
	title = {The elements of statistical learning},
	volume = {1},
	year = {2001},
	publisher = {Springer series in statistics New York},
	organization = {Springer series in statistics New York},
	keywords = {Deep Learning, ref},
	url = {https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about},
	author = {Friedman, Jerome and Hastie, Trevor and Tibshirani, Robert}
}
