Sparsemax and Relaxed Wasserstein for Topic Sparsity.

Tianyi Lin|Zhiyue Hu|Xin Guo


Anthology ID:DBLP:conf/wsdm/LinHG19
Volume:Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, February 11-15, 2019
Year:2019
Venue:Web Search and Data Mining (WSDM)
Publisher:ACM
Pages:141-149
URL:https://doi.org/10.1145/3289600.3290957
DOI:https://doi.org/10.1145/3289600.3290957
DBLP:conf/wsdm/LinHG19
BibTeX:
@inproceedings{lin-2019-sparsemax, author = {Tianyi Lin and Zhiyue Hu and Xin Guo}, editor = {J. Shane Culpepper and Alistair Moffat and Paul N. Bennett and Kristina Lerman}, title = {{Sparsemax and Relaxed Wasserstein for Topic Sparsity}}, booktitle = {{Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, February 11-15, 2019}}, pages = {141--149}, publisher = {ACM}, year = {2019}, url = {https://doi.org/10.1145/3289600.3290957}, doi = {https://doi.org/10.1145/3289600.3290957} }