Distilled Wasserstein Learning for Word Embedding and Topic Modeling

Distilled Wasserstein Learning for Word Embedding and Topic Modeling

12 September 2018

Lawrence Carin

Papers citing "Distilled Wasserstein Learning for Word Embedding and Topic Modeling"

8 / 8 papers shown

Title
Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs Nicolas Boizard Kevin El Haddad C´eline Hudelot Pierre Colombo 68 14 0 28 Jan 2025
Linearized Wasserstein Barycenters: Synthesis, Analysis, Representational Capacity, and Applications Matthew Werenski Brendan Mallery Shuchin Aeron James M. Murphy 38 0 0 31 Oct 2024
Knowledge-Aware Bayesian Deep Topic Model Dongsheng Wang Yishi Xu Miaoge Li Zhibin Duan Chaojie Wang Bo Chen Mingyuan Zhou BDL 28 15 0 20 Sep 2022
Hybrid Gromov-Wasserstein Embedding for Capsule Learning Pourya Shamsolmoali Masoumeh Zareapoor Swagatam Das Eric Granger Salvador García MedIm 24 2 0 01 Sep 2022
Measure Estimation in the Barycentric Coding Model Matthew Werenski Ruijie Jiang Abiy Tasissa Shuchin Aeron James M. Murphy 40 14 0 28 Jan 2022
Gaussian Hierarchical Latent Dirichlet Allocation: Bringing Polysemy Back Takahiro Yoshida Ryohei Hisano T. Ohnishi 22 6 0 25 Feb 2020
Hierarchical Optimal Transport for Document Representation Mikhail Yurochkin Sebastian Claici Edward Chien F. Mirzazadeh Justin Solomon OT 11 90 0 26 Jun 2019
Wasserstein Barycenter Model Ensembling Pierre L. Dognin Igor Melnyk Youssef Mroueh Jerret Ross Cicero Nogueira dos Santos Tom Sercu 22 24 0 13 Feb 2019