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Volumn , Issue , 2016, Pages 4114-4122

Binarized neural networks

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL ACTIVATION; GRAPHICS PROCESSING UNIT;

EID: 85013626529     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (2300)

References (34)
  • 1
    • 84942134575 scopus 로고    scopus 로고
    • Subdominant dense clusters allow for simple learning and high computational performance in neural networks with discrete synapses
    • Baldassi, C., Ingrosso, A., Lucibello, C., Saglietti, L., and Zecchina, R. Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses. Physical Review Letters, 115(12): 1-5, 2015.
    • (2015) Physical Review Letters , vol.115 , Issue.12 , pp. 1-5
    • Baldassi, C.1    Ingrosso, A.2    Lucibello, C.3    Saglietti, L.4    Zecchina, R.5
  • 9
    • 84888340666 scopus 로고    scopus 로고
    • Torch7: A matlab-like environment for machine learning
    • NIPS Workshop
    • Collobert, R., Kavukcuoglu, K., and Farabet, C. Torch7: A matlab-like environment for machine learning. In BigLearn, NIPS Workshop, 2011.
    • (2011) BigLearn
    • Collobert, R.1    Kavukcuoglu, K.2    Farabet, C.3
  • 14
    • 79951563340 scopus 로고    scopus 로고
    • Understanding the difficulty of training deep feedforward neural networks
    • Glorot, X. and Bengio, Y. Understanding the difficulty of training deep feedforward neural networks. In AISTATS'2010, 2010.
    • (2010) AISTATS'2010
    • Glorot, X.1    Bengio, Y.2
  • 17
    • 12444275638 scopus 로고    scopus 로고
    • Analysis of high-performance floating-point arithmetic on FPGAs
    • 2004. Proceedings. 18th International IEEE
    • Govindu, G., Zhuo, L., Choi, S., and Prasanna, V. Analysis of high-performance floating-point arithmetic on FPGAs. In Parallel and Distributed Processing Symposium, 2004. Proceedings. 18th International, pp. 149. IEEE, 2004.
    • (2004) Parallel and Distributed Processing Symposium , pp. 149
    • Govindu, G.1    Zhuo, L.2    Choi, S.3    Prasanna, V.4
  • 21
    • 84893548516 scopus 로고    scopus 로고
    • Neural networks for machine learning
    • video lectures
    • Hinton, G. Neural networks for machine learning. Coursera, video lectures, 2012.
    • (2012) Coursera
    • Hinton, G.1
  • 24
    • 84920265200 scopus 로고    scopus 로고
    • Fixed-point feedforward deep neural network design using weights+ 1, 0, and- 1
    • IEEE
    • Hwang, K. and Sung, W. Fixed-point feedforward deep neural network design using weights+ 1, 0, and- 1. In Signal Processing Systems (SiPS), 2014 IEEE Workshop on, pp. 1-6. IEEE, 2014.
    • (2014) Signal Processing Systems (SiPS), 2014 IEEE Workshop on , pp. 1-6
    • Hwang, K.1    Sung, W.2
  • 28
    • 84930630277 scopus 로고    scopus 로고
    • Deep learning
    • LeCun, Y., Bengio, Y., and Hinton, G. Deep learning. Nature, 521(7553): 436-444, 2015.
    • (2015) Nature , vol.521 , Issue.7553 , pp. 436-444
    • LeCun, Y.1    Bengio, Y.2    Hinton, G.3
  • 31
    • 84937908919 scopus 로고    scopus 로고
    • Expectation backpropagation: Parameter-free training of multilayer neural networks with continuous or discrete weights
    • Soudry, D., Hubara, I., and Meir, R. Expectation backpropagation: Parameter-free training of multilayer neural networks with continuous or discrete weights. In NIPS'2014, 2014.
    • (2014) NIPS'2014
    • Soudry, D.1    Hubara, I.2    Meir, R.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.