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Polar factorization and monotone rearrangement of vector-valued functions
Brenier, Y · 1991
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Optimal transport: old and new , volume 338
Villani, C · 2008
Earlier work this paper cites.
Unidimensional and evolution methods for optimal transportation
Bonnotte, N · 2013
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M · 2013
Earlier work this paper cites.
From word embeddings to document distances
Kusner, M., Sun, Y., Kolkin, N., and Weinberger, K · 2015
Earlier work this paper cites.
An efficient linear programming method for optimal transportation
Oberman, A. M. and Ruan, Y · 2015
Earlier work this paper cites.
Stochastic optimization for large-scale optimal transport
Genevay, A., Cuturi, M., Peyré, G., and Bach, F · 2016
Earlier work this paper cites.
Supervised word mover's distance
Huang, G., Guo, C., Kusner, M. J., Sun, Y., Sha, F., and Weinberger, K. Q · 2016
Earlier work this paper cites.
Learning representations for automatic colorization
Larsson, G., Maire, M., and Shakhnarovich, G · 2016
Earlier work this paper cites.
A sparse multiscale algorithm for dense optimal transport
Schmitzer, B · 2016
Earlier work this paper cites.
Colorful image colorization
Zhang, R., Isola, P., and Efros, A. A · 2016
Earlier work this paper cites.
Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Earlier work this paper cites.
Optimal transport for domain adaptation
Courty, N., Flamary, R., Tuia, D., and Rakotomamonjy, A · 2017
Earlier work this paper cites.
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
Earlier work this paper cites.
Optimal mass transport: Signal processing and machine-learning applications
Kolouri, S., Park, S. R., Thorpe, M., Slepcev, D., and Rohde, G. K · 2017
Cited alongside, same era.
Representation learning by learning to count
Noroozi, M., Pirsiavash, H., and Favaro, P · 2017
Cited alongside, same era.
Deep sets
Zaheer, M., Kottur, S., Ravanbhakhsh, S., Póczos, B., Salakhutdinov, R., and Smola, A. J · 2017
Cited alongside, same era.
Towards sparse hierarchical graph classifiers
Cangea, C., Veličković, P., Jovanović, N., Kipf, T., and Liò, P · 2018
Cited alongside, same era.
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Damodaran, B. B., Kellenberger, B., Flamary, R., Tuia, D., and Courty, N · 2018
Cited alongside, same era.
Generative modeling using the sliced Wasserstein distance
Subspace robust wasserstein distances
Paty, F.-P. and Cuturi, M · 2019
Later among the works it cites.
Wasserstein weisfeiler-lehman graph kernels
Togninalli, M., Ghisu, M. E., Llinares-López, F., Rieck, B., and Borgwardt, K. M · 2019
Later among the works it cites.
Selfie: Self-supervised pretraining for image embedding
Trinh, T. H., Luong, M.-T., and Le, Q. V · 2019
Later among the works it cites.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Later among the works it cites.
Exploring simple siamese representation learning
Chen, X. and He, K · 2020
Later among the works it cites.
Bootstrap your own latent: A new approach to self-supervised learning
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Deshpande, I., Zhang, Z., and Schwing, A · 2018
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., and Komodakis, N · 2018
Cited alongside, same era.
Sliced Wasserstein distance for learning gaussian mixture models
Kolouri, S., Rohde, G. K., and Hoffmann, H · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
Cited alongside, same era.
The cumulative distribution transform and linear pattern classification
Park, S. R., Kolouri, S., Kundu, S., and Rohde, G. K · 2018
Cited alongside, same era.
Wasserstein auto-encoders
Tolstikhin, I., Bousquet, O., Gelly, S., and Schoelkopf, B · 2018
Cited alongside, same era.
Unsupervised feature learning via non-parametric instance discrimination
Wu, Z., Xiong, Y., Yu, S. X., and Lin, D · 2018
Cited alongside, same era.
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., et al · 2020
Later among the works it cites.
Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
Later among the works it cites.
Conditional set generation with transformers
Kosiorek, A. R., Kim, H., and Rezende, D. J · 2020
Later among the works it cites.
Contrastive representation learning: A framework and review
Le-Khac, P. H., Healy, G., and Smeaton, A. F · 2020
Later among the works it cites.
Self-supervised learning of pretext-invariant representations
Misra, I. and Maaten, L. v. d · 2020
Later among the works it cites.
Statistical and topological properties of sliced probability divergences
Nadjahi, K., Durmus, A., Chizat, L., Kolouri, S., Shahrampour, S., and Şimşekli, U · 2020
Later among the works it cites.
Distributional sliced-wasserstein and applications to generative modeling
Nguyen, K., Ho, N., Pham, T., and Bui, H · 2020
Later among the works it cites.
Rep the set: Neural networks for learning set representations
Skianis, K., Nikolentzos, G., Limnios, S., and Vazirgiannis, M · 2020
Later among the works it cites.
Wasserstein embedding for graph learning
Kolouri, S., Naderializadeh, N., Rohde, G. K., and Hoffmann, H · 2021
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