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Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in generative modeling.
The mathematics of statistical machine translation: Parameter estimation
Brown, P. F., Della Pietra, S. A., Della Pietra, V. J., and Mercer, R. L · 1993
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Building a large annotated corpus of english: the penn treebank
Marcus, M. P., Marcinkiewicz, M. A., and Santorini, B · 1993
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The information bottleneck method
Tishby, N., Pereira, F. C., and Bialek, W · 2000
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Bleu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J · 2002
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Vector-based models of semantic composition
Mitchell, J. and Lapata, M · 2008
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Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Cernockỳ, J., and Khudanpur, S · 2010
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Learning latent space energy-based prior model for molecule generation
Pang, B., Han, T., and Wu, Y. N · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.-A., and Bottou, L · 2010
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A connection between score matching and denoising autoencoders
Vincent, P · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W · 2011
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An optimal assessment of natural language student input using word-to-word similarity metrics
Rus, V. and Lintean, M · 2012
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Generalized denoising auto-encoders as generative models
Bengio, Y., Yao, L., Alain, G., and Vincent, P · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Pomdp-based statistical spoken dialog systems: A review
Young, S., Gašić, M., Thomson, B., and Williams, J. D · 2013
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Cho, K., van Merrienboer, B., Gülçehre, Ç., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y · 2014
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Bootstrapping dialog systems with word embeddings
Forgues, G., Pineau, J., Larchevêque, J.-M., and Tremblay, R · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Semi-supervised learning with deep generative models
Kingma, D. P., Mohamed, S., Rezende, D. J., and Welling, M · 2014
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Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. D · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Character-level convolutional networks for text classification
Zhang, X., Zhao, J., and LeCun, Y · 2015
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Generating sentences from a continuous space
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A. M., Jozefowicz, R., and Bengio, S · 2016
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Importance weighted autoencoders
Burda, Y., Grosse, R. B., and Salakhutdinov, R · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2016
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Neural variational inference for text processing
Miao, Y., Yu, L., and Blunsom, P · 2016
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Building end-to-end dialogue systems using generative hierarchical neural network models
Serban, I., Sordoni, A., Bengio, Y., Courville, A., and Pineau, J · 2016
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A theory of generative convnet
Xie, J., Lu, Y., Zhu, S.-C., and Wu, Y · 2016
Cited alongside, same era.
Variational neural machine translation
Zhang, B., Xiong, D., Su, J., Duan, H., and Zhang, M · 2016
Cited alongside, same era.
Key-value retrieval networks for task-oriented dialogue
Eric, M., Krishnan, L., Charette, F., and Manning, C. D · 2017
A surprisingly effective fix for deep latent variable modeling of text
Li, B., He, J., Neubig, G., Berg-Kirkpatrick, T., and Yang, Y · 2019
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Learning non-convergent non-persistent short-run mcmc toward energy-based model
Nijkamp, E., Hill, M., Zhu, S.-C., and Wu, Y. N · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Topic-guided variational auto-encoder for text generation
Wang, W., Gan, Z., Xu, H., Zhang, R., Wang, G., Shen, D., Chen, C., and Carin, L · 2019
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Learning energy-based models by diffusion recovery likelihood
Gao, R., Song, Y., Poole, B., Wu, Y. N., and Kingma, D. P · 2020
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Joint training of variational auto-encoder and latent energy-based model
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Cited alongside, same era.
A hierarchical latent variable encoder-decoder model for generating dialogues
Serban, I., Sordoni, A., Lowe, R., Charlin, L., Pineau, J., Courville, A., and Bengio, Y · 2017
Cited alongside, same era.
Latent intention dialogue models
Wen, T.-H., Miao, Y., Blunsom, P., and Young, S · 2017
Cited alongside, same era.
Learning discourse-level diversity for neural dialog models using conditional variational autoencoders
Zhao, T., Zhao, R., and Eskenazi, M · 2017
Cited alongside, same era.
Neural models for documents with metadata
Card, D., Tan, C., and Smith, N. A · 2018
Cited alongside, same era.
A deep generative framework for paraphrase generation
Gupta, A., Agarwal, A., Singh, P., and Rai, P · 2018
Cited alongside, same era.
Lagging inference networks and posterior collapse in variational autoencoders
He, J., Spokoyny, D., Neubig, G., and Berg-Kirkpatrick, T · 2018
Cited alongside, same era.
Han, T., Nijkamp, E., Zhou, L., Pang, B., Zhu, S.-C., and Wu, Y. N · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Discrete latent variable representations for low-resource text classification
Jin, S., Wiseman, S., Stratos, K., and Livescu, K · 2020
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On the anatomy of mcmc-based maximum likelihood learning of energy-based models
Nijkamp, E., Hill, M., Han, T., Zhu, S.-C., and Wu, Y. N · 2020
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Dispersed exponential family mixture vaes for interpretable text generation
Shi, W., Zhou, H., Miao, N., and Li, L · 2020
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Improved techniques for training score-based generative models
Song, Y. and Ermon, S · 2020
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020
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NVAE: A deep hierarchical variational autoencoder
Vahdat, A. and Kautz, J · 2020
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Modeling worlds in text
Ammanabrolu, P. and Riedl, M. O · 2021
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Structured denoising diffusion models in discrete state-spaces
Austin, J., Johnson, D. D., Ho, J., Tarlow, D., and van den Berg, R · 2021
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Improved contrastive divergence training of energy based models
Du, Y., Li, S., Tenenbaum, J., and Mordatch, I · 2021
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Controllable and compositional generation with latent-space energy-based models
Nie, W., Vahdat, A., and Anandkumar, A · 2021
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Latent space energy-based model of symbol-vector coupling for text generation and classification
Pang, B. and Wu, Y. N · 2021
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Trajectory prediction with latent belief energy-based model
Pang, B., Zhao, T., Xie, X., and Wu, Y. N · 2021
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D2c: Diffusion-denoising models for few-shot conditional generation
Sinha, A., Song, J., Meng, C., and Ermon, S · 2021
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Score-based generative modeling in latent space
Vahdat, A., Kreis, K., and Kautz, J · 2021
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Diffusion priors in variational autoencoders
Wehenkel, A. and Louppe, G · 2021
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Unsupervised foreground extraction via deep region competition
Yu, P., Xie, S., Ma, X., Zhu, Y., Wu, Y. N., and Zhu, S.-C · 2021
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