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I. Hubara, Courbariaux, M. , Soudry, D. , El-Yaniv, R. , and Bengio, Y. , Binarized neural networks, Advances in neural information processing systems. pp. 4107–4115, 2016.
M. Courbariaux, Hubara, I. , Soudry, D. , El-Yaniv, R. , and Bengio, Y. , Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1, arXiv preprint arXiv:1602.02830, 2016.
M. Kim and Smaragdis, P. , Bitwise neural networks, arXiv preprint arXiv:1601.06071, 2016.
H. Palangi, Ward, R. K. , and Deng, L. , Distributed Compressive Sensing: A Deep Learning Approach., IEEE Trans. Signal Processing, vol. 64, pp. 4504–4518, 2016.
K. Kulkarni, Lohit, S. , Turaga, P. , Kerviche, R. , and Ashok, A. , Reconnet: Non-iterative reconstruction of images from compressively sensed measurements, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 449–458, 2016.
M. Rastegari, Ordonez, V. , Redmon, J. , and Farhadi, A. , Xnor-net: Imagenet classification using binary convolutional neural networks, European Conference on Computer Vision. Springer, pp. 525–542, 2016.
M. Courbariaux, Bengio, Y. , and David, J. - P. , Binaryconnect: Training deep neural networks with binary weights during propagations, Advances in Neural Information Processing Systems. pp. 3123–3131, 2015.
G. Chen and Needell, D. , Compressed sensing and dictionary learning, Preprint, vol. 106, 2015.
Y. LeCun, Bengio, Y. , and Hinton, G. , Deep learning, Nature, vol. 521, pp. 436–444, 2015.
A. Mousavi, Patel, A. B. , and Baraniuk, R. G. , A deep learning approach to structured signal recovery, Communication, Control, and Computing (Allerton), 2015 53rd Annual Allerton Conference on. IEEE, pp. 1336–1343, 2015.
J. Schmidhuber, Deep learning in neural networks: An overview, Neural networks, vol. 61, pp. 85–117, 2015.
S. Gupta, Agrawal, A. , Gopalakrishnan, K. , and Narayanan, P. , Deep learning with limited numerical precision, Proceedings of the 32nd International Conference on Machine Learning (ICML-15). pp. 1737–1746, 2015.
S. Biookaghazadeh, Xu, Y. , Zhou, S. , and Zhao, M. , Enabling scientific data storage and processing on big-data systems, Big Data (Big Data), 2015 IEEE International Conference on. IEEE, pp. 1978–1984, 2015. (966.13 KB)
C. Szegedy, Liu, W. , Jia, Y. , Sermanet, P. , Reed, S. , Anguelov, D. , Erhan, D. , Vanhoucke, V. , and Rabinovich, A. , Going deeper with convolutions, Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1–9, 2015.
W. Dally, High-performance hardware for machine learning, NIPS Tutorial, 2015.
C. Hegde, Sankaranarayanan, A. C. , Yin, W. , and Baraniuk, R. G. , NuMax: A convex approach for learning near-isometric linear embeddings, IEEE Transactions on Signal Processing, vol. 63, pp. 6109–6121, 2015.
Y. Wang, Li, X. , Yu, H. , Ni, L. , Yang, W. , Weng, C. , and Zhao, J. , Optimizing boolean embedding matrix for compressive sensing in rram crossbar, Low Power Electronics and Design (ISLPED), 2015 IEEE/ACM International Symposium on. IEEE, pp. 13–18, 2015.
C. Zhang, Li, P. , Sun, G. , Guan, Y. , Xiao, B. , and Cong, J. , Optimizing fpga-based accelerator design for deep convolutional neural networks, Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. ACM, pp. 161–170, 2015.
A. Putnam, Caulfield, A. M. , Chung, E. S. , Chiou, D. , Constantinides, K. , Demme, J. , Esmaeilzadeh, H. , Fowers, J. , Gopal, G. Prashanth, Gray, J. , and , , A reconfigurable fabric for accelerating large-scale datacenter services, IEEE Micro, vol. 35, pp. 10–22, 2015.
Y. Shen, Zhu, G. , Li, J. , and Zhu, Z. , Compressed Sensing Image Reconstruction Algorithm by Dictionary Learning, Proceedings of International Conference on Internet Multimedia Computing and Service. ACM, p. 193, 2014.
Y. Chen, Luo, T. , Liu, S. , Zhang, S. , He, L. , Wang, J. , Li, L. , Chen, T. , Xu, Z. , Sun, N. , and , , Dadiannao: A machine-learning supercomputer, Proceedings of the 47th Annual IEEE/ACM International Symposium on Microarchitecture. IEEE Computer Society, pp. 609–622, 2014.
T. Chen, Du, Z. , Sun, N. , Wang, J. , Wu, C. , Chen, Y. , and Temam, O. , Diannao: A small-footprint high-throughput accelerator for ubiquitous machine-learning, ACM Sigplan Notices, vol. 49. ACM, pp. 269–284, 2014.
J. Ouyang, Lin, S. , Qi, W. , Wang, Y. , Yu, B. , and Jiang, S. , SDA: Software-defined accelerator for large-scale DNN systems, Hot Chips 26 Symposium (HCS), 2014 IEEE. IEEE, pp. 1–23, 2014.
S. Qaisar, Bilal, R. Muhammad, Iqbal, W. , Naureen, M. , and Lee, S. , Compressive sensing: From theory to applications, a survey, Journal of Communications and networks, vol. 15, pp. 443–456, 2013.
S. Nam, Davies, M. E. , Elad, M. , and Gribonval, R. , The cosparse analysis model and algorithms, Applied and Computational Harmonic Analysis, vol. 34, pp. 30–56, 2013.
G. James, Witten, D. , Hastie, T. , and Tibshirani, R. , An introduction to statistical learning, vol. 112. Springer, 2013.
H. Palangi, Ward, R. K. , and Deng, L. , Using deep stacking network to improve structured compressed sensing with Multiple Measurement Vectors., ICASSP. pp. 3337–3341, 2013.
T. S. Czajkowski, Aydonat, U. , Denisenko, D. , Freeman, J. , Kinsner, M. , Neto, D. , Wong, J. , Yiannacouras, P. , and Singh, D. P. , From OpenCL to high-performance hardware on FPGAs, Field Programmable Logic and Applications (FPL), 2012 22nd International Conference on. IEEE, pp. 531–534, 2012.
A. Krizhevsky, Sutskever, I. , and Hinton, G. E. , Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems. pp. 1097–1105, 2012.
Y. Bengio, Practical recommendations for gradient-based training of deep architectures, in Neural networks: Tricks of the trade, Springer, 2012, pp. 437–478.
M. A. Davenport, Duarte, M. F. , Eldar, Y. C. , and Kutyniok, G. , Introduction to compressed sensing, preprint, vol. 93, p. 2, 2011.
M. A. Davenport, Duarte, M. F. , Eldar, Y. C. , and Kutyniok, G. , Introduction to compressed sensing, preprint, vol. 93, p. 2, 2011.
D. Needell and Tropp, J. A. , Cosamp: iterative signal recovery from incomplete and inaccurate samples, Communications of the ACM, vol. 53, pp. 93–100, 2010.
M. Zinkevich, Weimer, M. , Li, L. , and Smola, A. J. , Parallelized stochastic gradient descent, Advances in neural information processing systems. pp. 2595–2603, 2010.
J. Martin Duarte-Carvajalino and Sapiro, G. , Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization, IEEE Transactions on Image Processing, vol. 18, pp. 1395–1408, 2009.
B. Pérez-Sánchez, Fontenla-Romero, O. , and Guijarro-Berdiñas, B. , A supervised learning method for neural networks based on sensitivity analysis with automatic regularization, International Work-Conference on Artificial Neural Networks. Springer, pp. 157–164, 2009.
D. L. Donoho and Tsaig, Y. , Fast solution of $$\backslash$ell \_ $\$1$\$ $-norm minimization problems when the solution may be sparse, IEEE Transactions on Information Theory, vol. 54, pp. 4789–4812, 2008.
E. J. Candès and Wakin, M. B. , An introduction to compressive sampling, IEEE signal processing magazine, vol. 25, pp. 21–30, 2008.
E. J. Candès and Wakin, M. B. , An introduction to compressive sampling, IEEE signal processing magazine, vol. 25, pp. 21–30, 2008.
M. A. T. Figueiredo, Nowak, R. D. , and Wright, S. J. , Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems, IEEE Journal of selected topics in signal processing, vol. 1, pp. 586–597, 2007.
J. A. Tropp and Gilbert, A. C. , Signal recovery from random measurements via orthogonal matching pursuit, IEEE Transactions on information theory, vol. 53, pp. 4655–4666, 2007.
J. Friedman, Hastie, T. , and Tibshirani, R. , The elements of statistical learning, vol. 1. Springer series in statistics New York, 2001.