메뉴 건너뛰기




Volumn 2017-December, Issue , 2017, Pages 4237-4246

VAE learning via Stein variational gradient descent

Author keywords

[No Author keywords available]

Indexed keywords

SIGNAL ENCODING;

EID: 85046994842     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (79)

References (36)
  • 2
    • 85047018513 scopus 로고    scopus 로고
    • Symmetric variational autoencoder and connections to adversarial learning
    • L. Chen, S. Dai, Y. Pu, C. Li, and Q. Su Lawrence Carin. Symmetric variational autoencoder and connections to adversarial learning. In arXiv, 2017.
    • (2017) ArXiv
    • Chen, L.1    Dai, S.2    Pu, Y.3    Li, C.4    Su Lawrence Carin, Q.5
  • 3
    • 85031125843 scopus 로고    scopus 로고
    • Learning to draw samples with amortized Stein variational gradient descent
    • Y. Feng, D. Wang, and Q. Liu. Learning to draw samples with amortized stein variational gradient descent. In UAI, 2017.
    • (2017) UAI
    • Feng, Y.1    Wang, D.2    Liu, Q.3
  • 4
    • 84970024465 scopus 로고    scopus 로고
    • Scalable deep poisson factor analysis for topic modeling
    • Z. Gan, C. Chen, R. Henao, D. Carlson, and L. Carin. Scalable deep poisson factor analysis for topic modeling. In ICML, 2015.
    • (2015) ICML
    • Gan, Z.1    Chen, C.2    Henao, R.3    Carlson, D.4    Carin, L.5
  • 5
    • 84983208884 scopus 로고    scopus 로고
    • Draw: A recurrent neural network for image generation
    • K. Gregor, I. Danihelka, A. Graves, and D. Wierstra. Draw: A recurrent neural network for image generation. In ICML, 2015.
    • (2015) ICML
    • Gregor, K.1    Danihelka, I.2    Graves, A.3    Wierstra, D.4
  • 6
    • 85031097360 scopus 로고    scopus 로고
    • Stein variational adaptive importance sampling
    • J. Han and Q. Liu. Stein variational adaptive importance sampling. In UAI, 2017.
    • (2017) UAI
    • Han, J.1    Liu, Q.2
  • 8
    • 84986274465 scopus 로고    scopus 로고
    • Deep residual learning for image recognition
    • K. He, X. Zhang, S. Ren, and Sun J. Deep residual learning for image recognition. In CVPR, 2016.
    • (2016) CVPR
    • He, K.1    Zhang, X.2    Ren, S.3    Sun, J.4
  • 9
    • 85083951076 scopus 로고    scopus 로고
    • Adam: A method for stochastic optimization
    • D. Kingma and J. Ba. Adam: A method for stochastic optimization. In ICLR, 2015.
    • (2015) ICLR
    • Kingma, D.1    Ba, J.2
  • 11
    • 85083952489 scopus 로고    scopus 로고
    • Auto-encoding variational bayes
    • D. P. Kingma and M. Welling. Auto-encoding variational Bayes. In ICLR, 2014.
    • (2014) ICLR
    • Kingma, D.P.1    Welling, M.2
  • 13
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 14
    • 84877761544 scopus 로고    scopus 로고
    • A neural autoregressive topic model
    • H. Larochelle and S. Laulyi. A neural autoregressive topic model. In NIPS, 2012.
    • (2012) NIPS
    • Larochelle, H.1    Laulyi, S.2
  • 15
    • 85018878907 scopus 로고    scopus 로고
    • Stein variational gradient descent: A general purpose Bayesian inference algorithm
    • Q. Liu and D. Wang. Stein variational gradient descent: A general purpose bayesian inference algorithm. In NIPS, 2016.
    • (2016) NIPS
    • Liu, Q.1    Wang, D.2
  • 16
    • 84893676344 scopus 로고    scopus 로고
    • Rectifier nonlinearities improve neural network acoustic models
    • A. L. Maas, A. Y. Hannun, and A. Y. Ng. Rectifier nonlinearities improve neural network acoustic models. In ICML, 2013.
    • (2013) ICML
    • Maas, A.L.1    Hannun, A.Y.2    Ng, A.Y.3
  • 17
    • 84998953749 scopus 로고    scopus 로고
    • Neural variational inference for text processing
    • Y. Miao, L. Yu, and Phil Blunsomi. Neural variational inference for text processing. In ICML, 2016.
    • (2016) ICML
    • Miao, Y.1    Yu, L.2    Blunsomi, P.3
  • 18
    • 84919786239 scopus 로고    scopus 로고
    • Neural variational inference and learning in belief networks
    • A. Mnih and K. Gregor. Neural variational inference and learning in belief networks. In ICML, 2014.
    • (2014) ICML
    • Mnih, A.1    Gregor, K.2
  • 19
    • 84998673781 scopus 로고    scopus 로고
    • Variational inference for Monte Carlo objectives
    • A. Mnih and D. J. Rezende. Variational inference for monte carlo objectives. In ICML, 2016.
    • (2016) ICML
    • Mnih, A.1    Rezende, D.J.2
  • 21
    • 85018916536 scopus 로고    scopus 로고
    • Variational autoencoder for deep learning of images, labels and captions
    • Y. Pu, Z. Gan, R. Henao, X. Yuan, C. Li, A. Stevens, and L. Carin. Variational autoencoder for deep learning of images, labels and captions. In NIPS, 2016.
    • (2016) NIPS
    • Pu, Y.1    Gan, Z.2    Henao, R.3    Yuan, X.4    Li, C.5    Stevens, A.6    Carin, L.7
  • 22
    • 85083953057 scopus 로고    scopus 로고
    • Generative deep deconvolutional learning
    • Y. Pu, X. Yuan, and L. Carin. Generative deep deconvolutional learning. In ICLR workshop, 2015.
    • (2015) ICLR Workshop
    • Pu, Y.1    Yuan, X.2    Carin, L.3
  • 25
  • 27
    • 84919796093 scopus 로고    scopus 로고
    • Stochastic backpropagation and approximate inference in deep generative models
    • D. J. Rezende, S. Mohamed, and D. Wierstra. Stochastic backpropagation and approximate inference in deep generative models. In ICML, 2014.
    • (2014) ICML
    • Rezende, D.J.1    Mohamed, S.2    Wierstra, D.3
  • 28
    • 84969776493 scopus 로고    scopus 로고
    • Variational inference with Normalizing flows
    • D.J. Rezende and S. Mohamed. Variational inference with normalizing flows. In ICML, 2015.
    • (2015) ICML
    • Rezende, D.J.1    Mohamed, S.2
  • 30
    • 85047018593 scopus 로고    scopus 로고
    • Deconvolutional latent-variable model for text sequence matching
    • D. Shen, Y. Zhang, R. Henao, Q. Su, and L. Carin. Deconvolutional latent-variable model for text sequence matching. In arXiv, 2017.
    • (2017) ArXiv
    • Shen, D.1    Zhang, Y.2    Henao, R.3    Su, Q.4    Carin, L.5
  • 33
    • 79551480483 scopus 로고    scopus 로고
    • Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
    • P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. JMLR, 2010.
    • (2010) JMLR
    • Vincent, P.1    Larochelle, H.2    Lajoie, I.3    Bengio, Y.4    Manzagol, P.-A.5
  • 36
    • 84866013303 scopus 로고    scopus 로고
    • Beta-negative binomial process and poisson factor analysis
    • M. Zhou, L. Hannah, D. Dunson, and L. Carin. Beta-negative binomial process and Poisson factor analysis. In AISTATS, 2012.
    • (2012) AISTATS
    • Zhou, M.1    Hannah, L.2    Dunson, D.3    Carin, L.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.