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2001
J. Friedman, Hastie, T. , and Tibshirani, R. , The elements of statistical learning, vol. 1. Springer series in statistics New York, 2001.
2007
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.
2008
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.
2009
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.
2010
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.
F. Ren and Marković, D. , “True Energy-performance Analysis Of The MTJ-based Logic-in-memory Architecture (1-bit Full Adder)”, IEEE Transactions on Electron Devices, vol. 57, no. 5, pp. 1023–1028, 2010. (632.59 KB)
2011
H. Park, Dorrance, R. , Amin, A. , Ren, F. , Marković, D. , and Yang, C. K. Ken, “Analysis of STT-RAM Cell Design With Multiple MTJs Per Access”, Proceedings of the 2011 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH). IEEE Computer Society, pp. 53-58, 2011. (320.88 KB)
F. Ren, “Energy-performance Characterization of CMOS/Magnetic Tunnel Junction (MTJ) Hybrid Logic Circuits”, University of California, Los Angeles, Los Angeles, 2011. (1.05 MB)
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.
R. Dorrance, Ren, F. , Toriyama, Y. , Amin, A. , Yang, C. - K. K. , and Marković, D. , “Scalability And Design-space Analysis of A 1T-1MTJ Memory Cell”, Proceedings of the 2011 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH). IEEE, pp. 32-36, 2011. (1.1 MB)
2012
F. Ren, Park, H. , Dorrance, R. , Toriyama, Y. , Yang, C. - K. K. , and Marković, D. , “A Body-voltage-sensing-based Short Pulse Reading Circuit for Spin-torque Transfer RAMs (STT-RAMs)”, Proceedings of the 2012 13th International Symposium on Quality Electronic Design (ISQED). IEEE, pp. 275-282, 2012. (559.47 KB)
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.
R. Dorrance, Ren, F. , Toriyama, Y. , Hafez, A. Amin, Yang, C. - K. K. , and Marković, D. , “Scalability and Design-space Analysis of A 1T-1MTJ Memory Cell For STT-RAMs”, IEEE Transactions on Electron Devices, vol. 59, no. 4, pp. 878–887, 2012. (1.1 MB)
2013
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.
W. Xu, Huang, M. - C. , Liu, J. J. , Ren, F. , Shen, X. , Liu, X. , and Sarrafzadeh, M. , “mCOPD: Mobile Phone Based Lung Function Diagnosis and Exercise System for COPD”, Proceedings of the 6th International Conference on Pervasive Technologies Related to Assistive Environments (PETRA). ACM, 2013. (646.4 KB)
M. Mehdi Ghahremanpour, Arab, S. Shahriar, Biookaghazadeh, S. , Zhang, J. , and van der Spoel, D. , “MemBuilder: a web-based graphical interface to build heterogeneously mixed membrane bilayers for the GROMACS biomolecular simulation program”, Bioinformatics, vol. 30, pp. 439–441, 2013. (192.44 KB)
X. Zhang, Huang, M. - C. , Ren, F. , Xu, W. , Guan, N. , and Yi, W. , “Proper Running Posture Guide: A Wearable Biomechanics Capture System”, Proceedings of the 8th International Conference on Body Area Networks (BodyNets). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2013. (1.54 MB)
F. Ren, Park, H. , Yang, C. - K. K. , and Marković, D. , “Reference Calibration of Body-voltage Sensing Circuit for High-speed STT-RAMs”, IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 60, no. 11, pp. 2932–2939, 2013. (1.78 MB)
F. Ren, Xu, W. , and Marković, D. , “Scalable and Parameterised VLSI Architecture for Efficient Sparse Approximation in FPGAs And SoCs”, IET Electronics Letters, vol. 49, no. 23, pp. 1440–1441, 2013. (154.45 KB)
X. Zhang, Xu, W. , Huang, M. - C. , Amini, N. , and Ren, F. , “See UV on Your Skin: An Ultraviolet Sensing and Visualization System”, Proceedings of the 8th International Conference on Body Area Networks (BodyNets). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp. 22-28, 2013. (1.82 MB)
F. Ren, Dorrace, R. , Xu, W. , and Marković, D. , “A Single-precision Compressive Sensing Signal Reconstruction Engine on FPGAs”, Proceedings of the 23rd International Conference on Field Programmable Logic and Applications (FPL). IEEE, pp. 1-4, 2013. (358.12 KB)
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.
2014
K. - L. Wang, Yang, C. - K. K. , Markovic, D. , and Ren, F. , “Body Voltage Sensing Based Short Pulse Reading Circuit”, PCT/US2012/056136, 2014.
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. Li, Liang, J. , Li, L. , Ren, F. , Hu, W. , Li, J. , Qi, S. , and Pei, Q. , “Healable Capacitive Touch Screen Sensors Based on Transparent Composite Electrodes Comprising Silver Nanowires and a Furan/Maleimide Diels-Alder Cycloaddition Polymer”, ACS Nano, vol. 8, no. 12, pp. 12874–12882, 2014. (6.99 MB)
R. Dorrance, Ren, F. , and Marković, D. , “A Scalable Sparse Matrix-vector Multiplication Kernel for Energy-efficient Sparse-BLAS on FPGAs”, Proceedings of the 2014 ACM/SIGDA International Symposium on Field-programmable Gate Arrays (FPGA). ACM, pp. 161-170, 2014. (558.35 KB)
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.
F. Ren, Zhang, C. , Liu, L. , Xu, W. , Owall, V. , and Marković, D. , “A Square-Root-Free Matrix Decomposition Method for Energy-Efficient Least Square Computation on Embedded Systems”, IEEE Embedded Systems Letters, vol. 6, no. 4, pp. 73–76, 2014. (912.74 KB)
L. Cheng, Xu, W. , Ren, F. , Gong, F. , Gupta, P. , and He, L. , “Statistical Timing and Power Analysis of VLSI Considering Non-linear Dependence”, the VLSI Journal Integration, vol. 47, no. 4, pp. 487–498, 2014. (845.26 KB)
2015
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.
F. Ren and Marković, D. , “A Configurable 12-to-237KS/s 12.8 mW Sparse-approximation Engine for Mobile ExG Data Aggregation”, Proceedings of the 2015 IEEE International Solid-State Circuits Conference (ISSCC). IEEE, pp. 68-78, 2015. (6.94 MB)
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.
L. I. S. A. lab, “Deep Learning Tutorial.”. University of Montreal, 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)

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