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Comparing data defined over space and time is notoriously hard, because it involves quantifying both spatial and temporal variability, while at the same time taking into account the chronological structure of data.
Tracking cortical activity from M/EEG using graph cuts with spatiotemporal constraints
Gramfort, A., Papadopoulo, T., Baillet, S., and Clerc, M. (2011) · 1941
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On the translocation of masses
Kantorovic, L. (1942) · 1942
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On the theory of dynamic programming
Bellman, R. (1952) · 1952
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Dynamic programming algorithm optimization for spoken word recognition
Sakoe, H. and Chiba, S. (1978) · 1978
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Abnormal evoked potential latencies in amblyopia
Sokol, S. (1983) · 1983
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Particle filtering for multi-target tracking and sensor management
Doucet, A., Vo, B. ., Andrieu, C., and Davy, M. (2002) · 2002
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Protein homology detection using string alignment kernels
Saigo, H., Jean-Philippe, Vert, Ueda, N., and Akutsu, T. (2004) · 2004
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Extracting motion primitives from natural handwriting data
Williams, B., M.Toussaint, and Storkey., A. (2006) · 2006
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Visualizing high-dimensional data using t-sne
Maaten, L. and Hinton, G. (2008) · 2008
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A high-density erp study reveals latency, amplitude, and topographical differences in multiple sclerosis patients versus controls
Whelan, R., Lonergan, R., Kiiski, H., Nolan, H., Kinsella, K., Bramham, J., O’Brien, M., Reilly, R., Hutchinson, M., and Tubridy, N. (2010) · 2010
Cited alongside, same era.
Fast global alignment kernels
Cuturi, M. (2011) · 2011
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011) · 2011
Cited alongside, same era.
Enumerative Combinatorics: Volume 1
Stanley, R. P. (2011) · 2011
Cited alongside, same era.
Sinkhorn Distances: Lightspeed Computation of Optimal Transport
Soft-dtw: a differentiable loss function for time-series
Cuturi, M. and Blondel, M. (2017) · 2017
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Optimal transport for diffeomorphic registration
Feydy, J., Charlier, B., Vialard, F.-X., and Peyré, G. (2017) · 2017
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A transportation l(p) distance for signal analysis
Thorpe, M., Park, S., Kolouri, S., Rohde, G. K., and Slepčev, D. (2017) · 2017
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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
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Learning generative models with sinkhorn divergences
Genevay, A., Peyre, G., and Cuturi, M. (2018) · 2018
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Computational Optimal Transport
Peyré, G. and Cuturi, M. (2018) · 2018
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Cuturi, M. (2013) · 2013
Cited alongside, same era.
Convolutional Wasserstein distances: Efficient optimal transportation on geometric domains
Solomon, J., de Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A., Du, T., and Guibas, L. (2015) · 2015
Cited alongside, same era.
Scaling Algorithms for Unbalanced Transport Problems
Chizat, L., Peyré, G., Schmitzer, B., and Vialard, F.-X. (2017) · 2017
Cited alongside, same era.
New york city taxi trip data, 2009-2018
Taxi, N. Y. N. Y. . and Commission, L. (2019) · 2018
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Wasserstein regularization for sparse multi-task regression
Janati, H., Cuturi, M., and Gramfort, A. (2019) · 2019
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