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Volumn 6, Issue , 2018, Pages 4196-4208

Semi-amortized variational autoencoders

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; STOCHASTIC SYSTEMS;

EID: 85057251532     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (105)

References (67)
  • 1
    • 85046994372 scopus 로고    scopus 로고
    • OptNet: Differentiable optimization as a layer in neural networks
    • Amos, Brandon and Kolter, J. Zico. OptNet: Differentiable Optimization as a Layer in Neural Networks. In Proceedings of ICML, 2017.
    • (2017) Proceedings of ICML
    • Amos, B.1    Kolter, J.Z.2
  • 3
    • 85048667360 scopus 로고    scopus 로고
    • End-toend learning for structured prediction energy networks
    • Belanger, David, Yang, Bishan, and McCallum, Andrew. End-toend Learning for Structured Prediction Energy Networks. In Proceedings of ICML, 2017.
    • (2017) Proceedings of ICML
    • Belanger, D.1    Yang, B.2    McCallum, A.3
  • 8
    • 84983198385 scopus 로고    scopus 로고
    • Accurate and conservative estimates of mrf log-likelihood using reverse annealing
    • Burda, Yuri, Grosse, Roger, and Salakhutdinov, Ruslan. Accurate and Conservative Estimates of MRF Log-likelihood using Reverse Annealing. In Proceedings of AISTATS, 2015b.
    • (2015) Proceedings of AISTATS
    • Burda, Y.1    Grosse, R.2    Salakhutdinov, R.3
  • 10
    • 84893358740 scopus 로고    scopus 로고
    • A two-stage pretraining algorithm for deep boltzmann machines
    • Cho, Kyunghyun, Raiko, Tapani, Hin, Alexander, and Karhunen, Juha. A Two-Stage Pretraining Algorithm for Deep Boltzmann Machines. In Proceedings of ICANN, 2013.
    • (2013) Proceedings of ICANN
    • Cho, K.1    Raiko, T.2    Hin, A.3    Karhunen, J.4
  • 12
    • 85057248218 scopus 로고    scopus 로고
    • Inference suboptimality in variational autoencoders
    • Cremer, Chris, Li, Xuechen, and Duvenaud, David. Inference Suboptimality in Variational Autoencoders. In Proceedings of ICML, 2018.
    • (2018) Proceedings of ICML
    • Cremer, C.1    Li, X.2    Duvenaud, D.3
  • 13
    • 85088228479 scopus 로고    scopus 로고
    • TopicRNN: A recurrent neural network with long-range semantic dependency
    • Dieng, Adji B., Wang, Chong, Gao, Jianfeng,, and Paisley, John. TopicRNN: A Recurrent Neural Network With Long-Range Semantic Dependency. In Proceedings of ICLR, 2017.
    • (2017) Proceedings of ICLR
    • Dieng, A.B.1    Wang, C.2    Gao, J.3    Paisley, J.4
  • 14
    • 84983107941 scopus 로고    scopus 로고
    • Generic methods for optimization-based modeling
    • Domke, Justin. Generic Methods for Optimization-based Modeling. In Proceedings of AISTATS, 2012.
    • (2012) Proceedings of AISTATS
    • Domke, J.1
  • 16
    • 84899003086 scopus 로고    scopus 로고
    • Propagation algorithms for variational Bayesian learning
    • Ghahramani, Zoubin and Beal, Matthew. Propagation algorithms for variational bayesian learning. In Proceedings of NIPS, 2001.
    • (2001) Proceedings of NIPS
    • Ghahramani, Z.1    Beal, M.2
  • 18
    • 85041927783 scopus 로고    scopus 로고
    • Nonparametric variational auto-encoders for hierarchical representation learning
    • Goyal, Prasoon, Hu, Zhiting, Liang, Xiaodan, Wang, Chenyu, and Xing, Eric. Nonparametric Variational Auto-encoders for Hierarchical Representation Learning. In Proceedings of ICCV, 2017b.
    • (2017) Proceedings of ICCV
    • Goyal, P.1    Hu, Z.2    Liang, X.3    Wang, C.4    Xing, E.5
  • 23
    • 84986274465 scopus 로고    scopus 로고
    • Deep residual learning for image recognition
    • He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian. Deep residual learning for image recognition. In Proceedings of CVPR, 2016.
    • (2016) Proceedings of CVPR
    • He, K.1    Zhang, X.2    Ren, S.3    Sun, J.4
  • 26
    • 0024880831 scopus 로고
    • Multilayer feedforward networks are universal approximators
    • Hornik, Kur, Stinchcombe, Maxwell, and White, Halber. Multilayer Feedforward Networks are Universal Approximators. Neural Networks, 2:359-366, 1989.
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 29
    • 85017433100 scopus 로고    scopus 로고
    • Composing graphical models with neural networks for structured representations and fast inference
    • Johnson, Matthew, Duvenaud, David K., Wiltschko, Alex, Adams, Ryan P., and Datta, Sandeep R. Composing Graphical Models with Neural Networks for Structured Representations and Fast Inference. In Proceedings of NIPS, 2016.
    • (2016) Proceedings of NIPS
    • Johnson, M.1    Duvenaud, D.K.2    Wiltschko, A.3    Adams, R.P.4    Datta, S.R.5
  • 30
    • 0033225865 scopus 로고    scopus 로고
    • Introduction to variational methods for graphical models
    • Jordan, Michael, Ghahramani, Zoubin, Jaakkola, Tommi, and Saul, Lawrence. Introduction to Variational Methods for Graphical Models. Machine Learning, 37:183-233, 1999.
    • (1999) Machine Learning , vol.37 , pp. 183-233
    • Jordan, M.1    Ghahramani, Z.2    Jaakkola, T.3    Saul, L.4
  • 34
    • 85030484471 scopus 로고    scopus 로고
    • Structured inference networks for nonlinear state space models
    • Krishnan, Rahul G., Shalit, Uri, and Sontag, David. Structured Inference Networks for Nonlinear State Space Models. In Proceedings of AAAI, 2017.
    • (2017) Proceedings of AAAI
    • Krishnan, R.G.1    Shalit, U.2    Sontag, D.3
  • 35
    • 85057273468 scopus 로고    scopus 로고
    • On the challenges of learning with inference networks on sparse, high-dimensional data
    • Krishnan, Rahul G., Liang, Dawen, and Hoffman, Matthew. On the Challenges of Learning with Inference Networks on Sparse, High-dimensional Data. In Proceedings of AISTATS, 2018.
    • (2018) Proceedings of AISTATS
    • Krishnan, R.G.1    Liang, D.2    Hoffman, M.3
  • 36
    • 84949683101 scopus 로고    scopus 로고
    • Human-level concept learning through probabilistic program induction
    • Lake, Brendan M., Salakhutdinov, Ruslan, and Tenenbaum, Joshua B. Human-level Concept Learning through Probabilistic Program Induction. Science, 350:1332-1338, 2015.
    • (2015) Science , vol.350 , pp. 1332-1338
    • Lake, B.M.1    Salakhutdinov, R.2    Tenenbaum, J.B.3
  • 37
    • 0001298583 scopus 로고
    • Automatic learning rate maximization by on-line estimation of the hessians eigenvectors
    • LeCun, Yann, Simard, Patrice, and Pearlmutter, Barak. Automatic Learning Rate Maximization by On-line Estimation of the Hessians Eigenvectors. In Proceedings of NIPS, 1993.
    • (1993) Proceedings of NIPS
    • LeCun, Y.1    Simard, P.2    Pearlmutter, B.3
  • 38
    • 84989338543 scopus 로고    scopus 로고
    • Gradient-based hyperparameter optimization through reversible learning
    • Maclaurin, Dougal, Duvenaud, David, and Adams, Ryan P. Gradient-based Hyperparameter Optimization through Reversible Learning. In Proceedings of ICML, 2015.
    • (2015) Proceedings of ICML
    • Maclaurin, D.1    Duvenaud, D.2    Adams, R.P.3
  • 41
    • 84998953749 scopus 로고    scopus 로고
    • Neural variational inference for text processing
    • Miao, Yishu, Yu, Lei, and Blunsom, Phil. Neural Variational Inference for Text Processing. In Proceedings of ICML, 2016.
    • (2016) Proceedings of ICML
    • Miao, Y.1    Yu, L.2    Blunsom, P.3
  • 42
    • 85048492740 scopus 로고    scopus 로고
    • Discovering discrete latent topics with neural variational inference
    • Miao, Yishu, Grefenstette, Edward, and Blunsom, Phil. Discovering Discrete Latent Topics with Neural Variational Inference. In Proceedings of ICML, 2017.
    • (2017) Proceedings of ICML
    • Miao, Y.1    Grefenstette, E.2    Blunsom, P.3
  • 43
    • 84919786239 scopus 로고    scopus 로고
    • Neural variational inference and learning in belief networks
    • Mnih, Andryi and Gregor, Karol. Neural Variational Inference and Learning in Belief Networks. In Proceedings of ICML, 2014.
    • (2014) Proceedings of ICML
    • Mnih, A.1    Gregor, K.2
  • 44
    • 85047006547 scopus 로고    scopus 로고
    • Sequence to better sequence: Continuous revision of combinatorial structures
    • Mueller, Jonas, Gifford, David, and Jaakkola, Tommi. Sequence to Better Sequence: Continuous Revision of Combinatorial Structures. In Proceedings of ICML, 2017.
    • (2017) Proceedings of ICML
    • Mueller, J.1    Gifford, D.2    Jaakkola, T.3
  • 45
    • 0000255539 scopus 로고
    • Fast exact multiplication by the hessian
    • Pearlmutter, Barak A. Fast exact multiplication by the hessian. Neural Computation, 6(1):147-160, 1994.
    • (1994) Neural Computation , vol.6 , Issue.1 , pp. 147-160
    • Pearlmutter, B.A.1
  • 49
    • 84969776493 scopus 로고    scopus 로고
    • Variational inference with normalizing flows
    • Rezende, Danilo J. and Mohamed, Shakir. Variational Inference with Normalizing Flows. In Proceedings of ICML, 2015.
    • (2015) Proceedings of ICML
    • Rezende, D.J.1    Mohamed, S.2
  • 50
    • 84919796093 scopus 로고    scopus 로고
    • Stochastic backpropagation and approximate inference in deep generative models
    • Rezende, Danilo Jimenez, Mohamed, Shakir, and Wierstra, Daan. Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In Proceedings of ICML, 2014.
    • (2014) Proceedings of ICML
    • Rezende, D.J.1    Mohamed, S.2    Wierstra, D.3
  • 51
    • 85064803481 scopus 로고    scopus 로고
    • Discrete variational autoencoders
    • Rolfe, Jason Tyler. Discrete Variational Autoencoders. In Proceedings of ICLR, 2017.
    • (2017) Proceedings of ICLR
    • Rolfe, J.T.1
  • 52
    • 85031097119 scopus 로고    scopus 로고
    • Importance sampled stochastic optimization for variational inference
    • Sakaya, Joseph and Klami, Arto. Importance Sampled Stochastic Optimization for Variational Inference. In Proceedings of UAI, 2017.
    • (2017) Proceedings of UAI
    • Sakaya, J.1    Klami, A.2
  • 54
    • 84969835291 scopus 로고    scopus 로고
    • Markov chain monte carlo and variational inference: Bridging the gap
    • Salimans, Tim, Kingma, Diederik, and Welling, Max. Markov Chain Monte Carlo and Variational Inference: Bridging the Gap. In Proceedings of ICML, 2015.
    • (2015) Proceedings of ICML
    • Salimans, T.1    Kingma, D.2    Welling, M.3
  • 56
    • 85073165882 scopus 로고    scopus 로고
    • A hybrid convolutional variational autoencoder for text generation
    • Semeniuta, Stanislau, Severyn, Aliaksei, and Barth, Erhardt. A Hybrid Convolutional Variational Autoencoder for Text Generation. In Proceedings of EMNLP, 2017.
    • (2017) Proceedings of EMNLP
    • Semeniuta, S.1    Severyn, A.2    Barth, E.3
  • 59
    • 84883148756 scopus 로고    scopus 로고
    • Empirical risk minimization of graphical model parameters given approximate inference, decoding, and model structure
    • Stoyanov, Veselin, Ropson, Alexander, and Eisner, Jason. Empirical Risk Minimization of Graphical Model Parameters Given Approximate Inference, Decoding, and Model Structure. In Proceedings of AISTATS, 2011.
    • (2011) Proceedings of AISTATS
    • Stoyanov, V.1    Ropson, A.2    Eisner, J.3
  • 65
    • 85048398320 scopus 로고    scopus 로고
    • Improved variational autoencoders for text modeling using dilated convolutions
    • Yang, Zichao, Hu, Zhiting, Salakhutdinov, Ruslan, and Berg-Kirkpatrick, Taylor. Improved Variational Autoencoders for Text Modeling using Dilated Convolutions. In Proceedings of ICML, 2017.
    • (2017) Proceedings of ICML
    • Yang, Z.1    Hu, Z.2    Salakhutdinov, R.3    Berg-Kirkpatrick, T.4
  • 67
    • 85044572828 scopus 로고    scopus 로고
    • Towards deeper understanding of variational autoencoding models
    • Zhao, Shengjia, Song, Jiaming, and Ermon, Stefano. Towards Deeper Understanding of Variational Autoencoding Models. In Proceedings of ICML, 2017.
    • (2017) Proceedings of ICML
    • Zhao, S.1    Song, J.2    Ermon, S.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.