Fetching the paper…
Reading the bibliography…
Optimal transport offers an alternative to maximum likelihood for learning generative autoencoding models.
Ranking via Sinkhorn Propagation
Adams, R. P. and Zemel, R. S. (2011) · 1925
Earlier work this paper cites.
The Hungarian method for the assignment problem
Kuhn, H. W. (1955) · 1955
Earlier work this paper cites.
A relationship between arbitrary positive matrices and doubly stochastic matrices
Sinkhorn, R. (1964) · 1964
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Hornik, K. (1991) · 1991
Earlier work this paper cites.
Asymptotic analysis of the exponential penalty trajectory in linear programming
Cominetti, R. and San Martín, J. (1994) · 1994
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
Hinton, G. E. and Salakhutdinov, R. R. (2006) · 2006
Earlier work this paper cites.
A kernel method for the two-sample-problem
Gretton, A., Borgwardt, K. M., Rasch, M., Schölkopf, B., and Smola, A. J. (2007) · 2007
Earlier work this paper cites.
Visualizing data using t t -SNE
Maaten, L. v. d. and Hinton, G. (2008) · 2008
Earlier work this paper cites.
Optimal Transport: Old and New
Villani, C. (2008) · 2008
Earlier work this paper cites.
Hilbert space embeddings and metrics on probability measures
Sriperumbudur, B. K., Gretton, A., Fukumizu, K., Schölkopf, B., and Lanckriet, G. R. G. (2010) · 2010
Earlier work this paper cites.
Universality, characteristic kernels and RKHS embedding of measures
Sriperumbudur, B. K., Fukumizu, K., and Lanckriet, G. R. G. (2011) · 2011
Earlier work this paper cites.
A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A. (2012) · 2012
Earlier work this paper cites.
Sinkhorn Distances: Lightspeed Computation of Optimal Transport
Cuturi, M. (2013) · 2013
Earlier work this paper cites.
Auto-encoding variational Bayes
Kingma, D. P. and Welling, M. (2013) · 2013
Earlier work this paper cites.
Domain adaptation with regularized optimal transport
Courty, N., Flamary, R., and Tuia, D. (2014) · 2014
Earlier work this paper cites.
Generative Adversarial Nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D. (2014) · 2014
Earlier work this paper cites.
Learning with a Wasserstein Loss
Frogner, C., Zhang, C., Mobahi, H., Araya, M., and Poggio, T. A. (2015) · 2015
Earlier work this paper cites.
Adversarial autoencoders
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B. (2015) · 2015
Cited alongside, same era.
Scaling Algorithms for Unbalanced Transport Problems
Chizat, L., Peyré, G., Schmitzer, B., and Vialard, F.-X. (2016) · 2016
Cited alongside, same era.
ELBO surgery: yet another way to carve up the variational evidence lower bound
Hoffman, M. D. and Johnson, M. J. (2016) · 2016
Cited alongside, same era.
Supervised word mover’s distance
Huang, G., Guo, C., Kusner, M. J., Sun, Y., Sha, F., and Weinberger, K. Q. (2016) · 2016
Cited alongside, same era.
Learning in implicit generative models
Mohamed, S. and Lakshminarayanan, B. (2016) · 2016
Cited alongside, same era.
Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M. and Favaro, P. (2016) · 2016
VAE with a VampPrior
Tomczak, J. M. and Welling, M. (2017) · 2017
Later among the works it cites.
Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance
Weed, J. and Bach, F. (2017) · 2017
Later among the works it cites.
Fixing a Broken ELBO
Alemi, A., Poole, B., Fischer, I., Dillon, J., Saurous, R. A., and Murphy, K. (2018) · 2018
Closest in time.
Wasserstein Variational Inference
Ambrogioni, L., Güçlü, U., Güçlütürk, Y., Hinne, M., van Gerven, M. A., and Maris, E. (2018) · 2018
Closest in time.
Bińkowski, M., Sutherland, D. J., Arbel, M., and Gretton, A. (2018) · 2018
Closest in time.
Hyperspherical Variational Auto-Encoders
Davidson, T. R., Falorsi, L., De Cao, N., Kipf, T., and Tomczak, J. M. (2018) · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Stabilized sparse scaling algorithms for entropy regularized transport problems
Schmitzer, B. (2016) · 2016
Cited alongside, same era.
Deep variational information bottleneck
Alemi, A. A., Fischer, I., Dillon, J. V., and Murphy, K. (2017) · 2017
Cited alongside, same era.
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
Altschuler, J., Weed, J., and Rigollet, P. (2017) · 2017
Cited alongside, same era.
Lipschitz properties for deep convolutional networks
Balan, R., Singh, M., and Zou, D. (2017) · 2017
Cited alongside, same era.
Unsupervised Learning by Predicting Noise
Bojanowski, P. and Joulin, A. (2017) · 2017
Cited alongside, same era.
From optimal transport to generative modeling: the VEGAN cookbook
Bousquet, O., Gelly, S., Tolstikhin, I., Simon-Gabriel, C.-J., and Schoelkopf, B. (2017) · 2017
Cited alongside, same era.
Closest in time.
Interpolating between Optimal Transport and MMD using Sinkhorn Divergences
Feydy, J., Séjourné, T., Vialard, F.-X., Amari, S.-i., Trouvé, A., and Peyré, G. (2018) · 2018
Closest in time.
Implicit Reparameterization Gradients
Figurnov, M., Mohamed, S., and Mnih, A. (2018) · 2018
Closest in time.
Learning Generative Models with Sinkhorn Divergences
Genevay, A., Peyré, G., Cuturi, M., et al. (2018) · 2018
Closest in time.
Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model
Kolouri, S., Martin, C. E., and Rohde, G. K. (2018) · 2018
Closest in time.
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
Linderman, S. W., Mena, G. E., Cooper, H., Paninski, L., and Cunningham, J. P. (2018) · 2018
Closest in time.
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance
Luise, G., Rudi, A., Pontil, M., and Ciliberto, C. (2018) · 2018
Closest in time.
Computational Optimal Transport
Peyré, G. and Cuturi, M. (2018) · 2018
Closest in time.
Distribution Matching in Variational Inference
Rosca, M., Lakshminarayanan, B., and Mohamed, S. (2018) · 2018
Closest in time.
Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders
Rubenstein, P. K., Schoelkopf, B., and Tolstikhin, I. (2018) · 2018
Closest in time.
Wasserstein Auto-Encoders
Tolstikhin, I., Bousquet, O., Gelly, S., and Schoelkopf, B. (2018) · 2018
Closest in time.
An explicit analysis of the entropic penalty in linear programming
Weed, J. (2018) · 2018
Closest in time.
Sample Complexity of Sinkhorn Divergences
Genevay, A., Chizat, L., Bach, F., Cuturi, M., Peyré, G., et al. (2019) · 2019
Closest in time.