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The word mover's distance (WMD) is a fundamental technique for measuring the similarity of two documents.
Earth mover’s distance minimization for unsupervised bilingual lexicon induction
Zhang, M., Liu, Y., Luan, H., and Sun, M · 1945
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The speed of mean glivenko-cantelli convergence
Dudley, R. M · 1969
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NewsWeeder: Learning to filter netnews
Lang, K · 1995
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Text categorization with support vector machines: Learning with many relevant features
Joachims, T · 1998
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Fast and robust comparison of probability measures in heterogeneous spaces
Sato, R., Cuturi, M., Yamada, M., and Kashima, H · 2002
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Machine learning in automated text categorization
Sebastiani, F · 2002
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UniFrac: a new phylogenetic method for comparing microbial communities
Lozupone, C. and Knight, R · 2005
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Practical solutions to the problem of diagonal dominance in kernel document clustering
Greene, D. and Cunningham, P · 2006
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Improvements that don’t add up: ad-hoc retrieval results since 1998
Armstrong, T. G., Moffat, A., Webber, W., and Zobel, J · 2009
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Local histogram based segmentation using the wasserstein distance
Ni, K., Bresson, X., Chan, T. F., and Esedoglu, S · 2009
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Wasserstein barycenter and its application to texture mixing
Rabin, J., Peyré, G., Delon, J., and Bernot, M · 2011
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Sanders-twitter sentiment corpus
Sanders, N. J · 2011
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Blue noise through optimal transport
De Goes, F., Breeden, K., Ostromoukhov, V., and Desbrun, M · 2012
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The phylogenetic kantorovich–rubinstein metric for environmental sequence samples
Evans, S. N. and Matsen, F. A · 2012
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Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M · 2013
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Policy: NIH plans to enhance reproducibility
Collins, F. S. and Tabak, L. A · 2014
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GloVe: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. D · 2014
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Summarization based on embedding distributions
Kobayashi, H., Noguchi, M., and Yatsuka, T · 2015
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From word embeddings to document distances
Kusner, M. J., Sun, Y., Kolkin, N. I., and Weinberger, K. Q · 2015
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Optimal transport for applied mathematicians
Santambrogio, F · 2015
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Stochastic optimization for large-scale optimal transport
Genevay, A., Cuturi, M., Peyré, G., and Bach, F. R · 2016
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What does research reproducibility mean?
Goodman, S. N., Fanelli, D., and Ioannidis, J. P · 2016
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Supervised word mover’s distance
Huang, G., Guo, C., Kusner, M. J., Sun, Y., Sha, F., and Weinberger, K. Q · 2016
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Sliced wasserstein kernels for probability distributions
Kolouri, S., Zou, Y., and Rohde, G. K · 2016
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Fast dictionary learning with a smoothed wasserstein loss
Rolet, A., Cuturi, M., and Peyré, G · 2016
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Building earth mover’s distance on bilingual word embeddings for machine translation
Zhang, M., Liu, Y., Luan, H., Sun, M., Izuha, T., and Hao, J · 2016
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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A simple but tough-to-beat baseline for sentence embeddings
Arora, S., Liang, Y., and Ma, T · 2017
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Re-evaluating automatic metrics for image captioning
Kilickaya, M., Erdem, A., Ikizler-Cinbis, N., and Erdem, E · 2017
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Earth mover’s distance pooling over siamese lstms for automatic short answer grading
Kumar, S., Chakrabarti, S., and Roy, S · 2017
Cited alongside, same era.
Does the geometry of word embeddings help document classification? A case study on persistent homology-based representations
Michel, P., Ravichander, A., and Rijhwani, S · 2017
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Regularising non-linear models using feature side-information
Mollaysa, A., Strasser, P., and Kalousis, A · 2017
Cited alongside, same era.
A manifesto for reproducible science
Munafò, M. R., Nosek, B. A., Bishop, D. V., Button, K. S., Chambers, C. D., Du Sert, N. P., Simonsohn, U., Wagenmakers, E.-J., Ware, J. J., and Ioannidis, J. P · 2017
Cited alongside, same era.
Multivariate gaussian document representation from word embeddings for text categorization
Nikolentzos, G., Meladianos, P., Rousseau, F., Stavrakas, Y., and Vazirgiannis, M · 2017
Cited alongside, same era.
Performance comparison of neural and non-neural approaches to session-based recommendation
Ludewig, M., Mauro, N., Latifi, S., and Jannach, D · 2019
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Subspace robust wasserstein distances
Paty, F. and Cuturi, M · 2019
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Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming
Schiebinger, G., Shu, J., Tabaka, M., Cleary, B., Subramanian, V., Solomon, A., Gould, J., Liu, S., Lin, S., Berube, P., et al · 2019
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Sharp asymptotic and finite-sample rates of convergence of empirical measures in wasserstein distance
Weed, J., Bach, F., et al · 2019
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Balanced word clusters for interpretable document representation
Wrzalik, M. and Krechel, D · 2019
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Hierarchical optimal transport for document representation
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Retrieving compositional documents using position-sensitive word mover’s distance
Trapp, M., Skowron, M., and Schabus, D · 2017
Cited alongside, same era.
Structured optimal transport
Alvarez-Melis, D., Jaakkola, T. S., and Jegelka, S · 2018
Cited alongside, same era.
Learning semantic similarity in a continuous space
Deudon, M · 2018
Cited alongside, same era.
Learning generative models with sinkhorn divergences
Genevay, A., Peyré, G., and Cuturi, M · 2018
Cited alongside, same era.
The neural hype and comparisons against weak baselines
Lin, J · 2018
Cited alongside, same era.
Generalizing point embeddings using the wasserstein space of elliptical distributions
Muzellec, B. and Cuturi, M · 2018
Cited alongside, same era.
Improving gans using optimal transport
Salimans, T., Zhang, H., Radford, A., and Metaxas, D. N · 2018
Cited alongside, same era.
Yurochkin, M., Claici, S., Chien, E., Mirzazadeh, F., and Solomon, J. M · 2019
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Moverscore: Text generation evaluating with contextualized embeddings and earth mover distance
Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C. M., and Eger, S · 2019
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Scalable nearest neighbor search for optimal transport
Backurs, A., Dong, Y., Indyk, P., Razenshteyn, I. P., and Wagner, T · 2020
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A swiss army knife for minimax optimal transport
Dhouib, S., Redko, I., Kerdoncuff, T., Emonet, R., and Sebban, M · 2020
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A study of performance of optimal transport
Dong, Y., Gao, Y., Peng, R., Razenshteyn, I. P., and Sawlani, S · 2020
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A fair comparison of graph neural networks for graph classification
Errica, F., Podda, M., Bacciu, D., and Micheli, A · 2020
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SUPERT: towards new frontiers in unsupervised evaluation metrics for multi-document summarization
Gao, Y., Zhao, W., and Eger, S · 2020
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P-SIF: document embeddings using partition averaging
Gupta, V., Saw, A., Nokhiz, P., Netrapalli, P., Rai, P., and Talukdar, P. P · 2020
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Dense passage retrieval for open-domain question answering
Karpukhin, V., Oguz, B., Min, S., Lewis, P. S. H., Wu, L., Edunov, S., Chen, D., and Yih, W · 2020
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Colbert: Efficient and effective passage search via contextualized late interaction over BERT
Khattab, O. and Zaharia, M · 2020
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Improving text generation with student-forcing optimal transport
Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., and Carin, L · 2020
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Projection robust wasserstein distance and riemannian optimization
Lin, T., Fan, C., Ho, N., Cuturi, M., and Jordan, M. I · 2020
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Message passing attention networks for document understanding
Nikolentzos, G., Tixier, A. J., and Vazirgiannis, M · 2020
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Feature robust optimal transport for high-dimensional data
Petrovich, M., Liang, C., Sato, R., Liu, Y., Tsai, Y. H., Zhu, L., Yang, Y., Salakhutdinov, R., and Yamada, M · 2020
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Fast unbalanced optimal transport on a tree
Sato, R., Yamada, M., and Kashima, H · 2020
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Context mover’s distance & barycenters: Optimal transport of contexts for building representations
Singh, S. P., Hug, A., Dieuleveut, A., and Jaggi, M · 2020
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Speeding up word mover’s distance and its variants via properties of distances between embeddings
Werner, M. and Laber, E · 2020
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Word rotator’s distance
Yokoi, S., Takahashi, R., Akama, R., Suzuki, J., and Inui, K · 2020
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Semantic matching via optimal partial transport
Zhang, R., Chen, C., Zhang, X., Bai, K., and Carin, L · 2020
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Zhao, W., Glavas, G., Peyrard, M., Gao, Y., West, R., and Eger, S · 2020
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Word alignment by fine-tuning embeddings on parallel corpora
Dou, Z. and Neubig, G · 2021
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A riemannian block coordinate descent method for computing the projection robust wasserstein distance
Huang, M., Ma, S., and Lai, L · 2021
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Rocketqa: An optimized training approach to dense passage retrieval for open-domain question answering
Qu, Y., Ding, Y., Liu, J., Liu, K., Ren, R., Zhao, W. X., Dong, D., Wu, H., and Wang, H · 2021
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Supervised tree-wasserstein distance
Takezawa, Y., Sato, R., and Yamada, M · 2021
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