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Variational autoencoders (VAEs) are powerful tools for learning latent representations of data used in a wide range of applications.
Adaptive mixtures of local experts
Jacobs, R. A., Jordan, M. I., Nowlan, S. J., and Hinton, G. E · 1991
Earlier work this paper cites.
A primer in game theory
Gibbons, R. et al · 1992
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
Earlier work this paper cites.
An introduction to variational methods for graphical models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., and Saul, L. K · 1999
Earlier work this paper cites.
Probabilistic principal component analysis
Tipping, M. E. and Bishop, C. M · 1999
Earlier work this paper cites.
The information bottleneck method
Tishby, N., Pereira, F. C., and Bialek, W · 2000
Earlier work this paper cites.
Elements of information theory
Thomas, M. and Joy, A. T · 2006
Earlier work this paper cites.
An overview of bilevel optimization
Colson, B., Marcotte, P., and Savard, G · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Robust principal component analysis?
Candès, E. J., Li, X., Ma, Y., and Wright, J · 2011
Earlier work this paper cites.
Rank-sparsity incoherence for matrix decomposition
Chandrasekaran, V., Sanghavi, S., Parrilo, P. A., and Willsky, A. S · 2011
Earlier work this paper cites.
The neural autoregressive distribution estimator
Larochelle, H. and Murray, I · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
Earlier work this paper cites.
The mnist database of handwritten digit images for machine learning research
Deng, L · 2012
Earlier work this paper cites.
Efficient backprop
LeCun, Y. A., Bottou, L., Orr, G. B., and Müller, K.-R · 2012
Earlier work this paper cites.
Deep boltzmann machines and the centering trick
Montavon, G. and Müller, K.-R · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
Earlier work this paper cites.
Variational autoencoder based anomaly detection using reconstruction probability
An, J. and Cho, S · 2015
Earlier work this paper cites.
Generating sentences from a continuous space
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A. M., Jozefowicz, R., and Bengio, S · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
Zhang, X., Zhao, J., and LeCun, Y · 2015
Earlier work this paper cites.
Deep variational information bottleneck
Alemi, A. A., Fischer, I., Dillon, J. V., and Murphy, K · 2016
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Importance weighted autoencoders
Burda, Y., Grosse, R., and Salakhutdinov, R · 2016
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Chen, X., Kingma, D. P., Salimans, T., Duan, Y., Dhariwal, P., Schulman, J., Sutskever, I., and Abbeel, P · 2016
Cited alongside, same era.
Ha, D., Dai, A., and Le, Q. V · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Texar: A modularized, versatile, and extensible toolkit for text generation
Hu, Z., Shi, H., Tan, B., Wang, W., Yang, Z., Zhao, T., He, J., Qin, L., Wang, D., et al · 2019
Later among the works it cites.
Don’t blame the elbo! a linear vae perspective on posterior collapse
Lucas, J., Tucker, G., Grosse, R. B., and Norouzi, M · 2019
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MacKay, M., Vicol, P., Lorraine, J., Duvenaud, D., and Grosse, R · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Later among the works it cites.
Skew-fit: State-covering self-supervised reinforcement learning
Pong, V. H., Dalal, M., Lin, S., Nair, A., Bahl, S., and Levine, S · 2019
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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
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Cited alongside, same era.
dsprites: Disentanglement testing sprites dataset
Matthey, L., Higgins, I., Hassabis, D., and Lerchner, A · 2017
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Learning visual reasoning without strong priors
Perez, E., De Vries, H., Strub, F., Dumoulin, V., and Courville, A · 2017
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Fixing a broken elbo
Alemi, A., Poole, B., Fischer, I., Dillon, J., Saurous, R. A., and Murphy, K · 2018
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs, 2018
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q · 2018
Cited alongside, same era.
Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
Cited alongside, same era.
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Preventing posterior collapse with delta-vaes
Razavi, A., Oord, A. v. d., Poole, B., and Vinyals, O · 2019
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On solving minimax optimization locally: A follow-the-ridge approach
Wang, Y., Zhang, G., and Ba, J · 2019
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Variational autoencoder for low bit-rate image compression
Zhou, L., Cai, C., Gao, Y., Su, S., and Wu, J · 2019
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Δ \Delta -stn: Efficient bilevel optimization for neural networks using structured response jacobians
Bae, J. and Grosse, R. B · 2020
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The usual suspects? reassessing blame for vae posterior collapse
Dai, B., Wang, Z., and Wipf, D · 2020
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Evaluating lossy compression rates of deep generative models
Huang, S., Makhzani, A., Cao, Y., and Grosse, R · 2020
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Multiplicative interactions and where to find them
Jayakumar, S. M., Czarnecki, W. M., Menick, J., Schwarz, J., Rae, J., Osindero, S., Teh, Y. W., Harley, T., and Pascanu, R · 2020
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On implicit regularization in β \beta -vaes
Kumar, A. and Poole, B · 2020
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Learning the pareto front with hypernetworks
Navon, A., Shamsian, A., Chechik, G., and Fetaya, E · 2020
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Exemplar vae: Linking generative models, nearest neighbor retrieval, and data augmentation
Norouzi, S., Fleet, D. J., and Norouzi, M · 2020
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Simple and effective vae training with calibrated decoders, 2020
Rybkin, O., Daniilidis, K., and Levine, S · 2020
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NVAE: A deep hierarchical variational autoencoder
Vahdat, A. and Kautz, J · 2020
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Variable rate deep image compression with modulated autoencoder
Yang, F., Herranz, L., Van De Weijer, J., Guitián, J. A. I., López, A. M., and Mozerov, M. G · 2020
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Rate-regularization and generalization in variational autoencoders
Bozkurt, A., Esmaeili, B., Tristan, J.-B., Brooks, D., Dy, J., and van de Meent, J.-W · 2021
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Quantitative understanding of vae as a non-linearly scaled isometric embedding
Nakagawa, A., Kato, K., and Suzuki, T · 2021
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A generalised linear model framework for β \beta -variational autoencoders based on exponential dispersion families
Sicks, R., Korn, R., and Schwaar, S · 2021
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If influence functions are the answer, then what is the question?
Bae, J., Ng, N., Lo, A., Ghassemi, M., and Grosse, R · 2022
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Pythae: Unifying generative autoencoders in python – a benchmarking use case
Chadebec, C., Vincent, L. J., and Allassonnière, S · 2022
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Interpreting rate-distortion of variational autoencoder and using model uncertainty for anomaly detection
Park, S., Adosoglou, G., and Pardalos, P. M · 2022
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Posterior collapse of a linear latent variable model
Wang, Z. and Ziyin, L · 2022
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