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A new algorithmic framework is proposed for learning autoencoders of data distributions.
Scalable gromov-wasserstein learning for graph partitioning and matching
Xu, H., Luo, D., and Carin, L · 1905
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Theory of maxima and the method of lagrange
Afriat, S · 1971
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Fei-Fei, L., Fergus, R., and Perona, P · 2004
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A co-regularization approach to semi-supervised learning with multiple views
Sindhwani, V., Niyogi, P., and Belkin, M · 2005
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A learning algorithm for adaptive canonical correlation analysis of several data sets
Vía, J., Santamaría, I., and Pérez, J · 2007
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Spectral Gromov-Wasserstein distances for shape matching
Mémoli, F · 2009
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A co-training approach for multi-view spectral clustering
Kumar, A. and Daumé, H · 2011
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Gromov–wasserstein distances and the metric approach to object matching
Mémoli, F · 2011
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Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Large-scale multi-view spectral clustering via bipartite graph
Li, Y., Nie, F., Huang, H., and Huang, J · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
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On deep multi-view representation learning
Wang, W., Arora, R., Livescu, K., and Bilmes, J · 2015
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Generating sentences from a continuous space
Bowman, S., Vilnis, L., Vinyals, O., Dai, A., Jozefowicz, R., and Bengio, S · 2016
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Deep unsupervised clustering with gaussian mixture variational autoencoders
Optimal transport for gaussian mixture models
Chen, Y., Georgiou, T. T., and Tannenbaum, A · 2018
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The Gromov-Wasserstein distance between networks and stable network invariants
Chowdhury, S. and Mémoli, F · 2018
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Sliced-wasserstein autoencoder: an embarrassingly simple generative model
Kolouri, S., Pope, P. E., Martin, C. E., and Rohde, G. K · 2018
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Wasserstein auto-encoders
Tolstikhin, I., Bousquet, O., Gelly, S., and Schölkopf, B · 2018
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Vae with a vampprior
Tomczak, J. and Welling, M · 2018
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Learning generative models across incomparable spaces
Bunne, C., Alvarez-Melis, D., Krause, A., and Jegelka, S · 2019
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Sliced Wasserstein kernels for probability distributions
Kolouri, S., Zou, Y., and Rohde, G. K · 2016
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Gromov-wasserstein averaging of kernel and distance matrices
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Coupled marginalized auto-encoders for cross-domain multi-view learning
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Learning multiple views with orthogonal denoising autoencoders
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Multi-view generative adversarial networks
Chen, M. and Denoyer, L · 2017
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Gromov-wasserstein alignment of word embedding spaces
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Hierarchical optimal transport for multimodal distribution alignment
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Variational autoencoder with implicit optimal priors
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Sliced gromov-wasserstein
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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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Gromov-wasserstein factorization models for graph clustering
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Hierarchical optimal transport for document representation
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