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Volumn 2015-January, Issue , 2015, Pages 2971-2979

Preconditioned spectral descent for deep learning

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; GRADIENT METHODS; INFORMATION SCIENCE; NEURAL NETWORKS;

EID: 84965148019     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (20)

References (30)
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  • 3
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    • K. Cho, T. Raiko, and A. Ilin. Enhanced Gradient for Training Restricted Boltzmann Machines. Neural Computation, 2013.
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    • Y. N. Dauphin, R. Pascanu, C. Gulcehre, K. Cho, S. Ganguli, and Y. Bengio. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. In NIPS, 2014.
    • (2014) NIPS
    • Dauphin, Y.N.1    Pascanu, R.2    Gulcehre, C.3    Cho, K.4    Ganguli, S.5    Bengio, Y.6
  • 7
    • 78649435995 scopus 로고    scopus 로고
    • Adaptive subgradient methods for online learning and stochastic optimization
    • J. Duchi, E. Hazan, and Y. Singer. Adaptive subgradient methods for online learning and stochastic optimization. JMLR, 2010.
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    • Duchi, J.1    Hazan, E.2    Singer, Y.3
  • 9
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    • Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
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    • Krizhevsky, A.1    Hinton, G.E.2
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    • Inductive principles for restricted Boltzmann machine learning
    • B. Marlin and K. Swersky. Inductive principles for restricted Boltzmann machine learning. ICML, 2010.
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    • Marlin, B.1    Swersky, K.2
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    • Parallelizable sampling of Markov Random fields
    • J. Martens and I. Sutskever. Parallelizable Sampling of Markov Random Fields. AISTATS, 2010.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.