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researcher

L. Tran

8 papers hereh-index 9696 citations21 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author3
  • last author2

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • stat.ML3
  • q-fin.ST1
same name
  • L. Tran — 17 papers, h 9
  • L. Tran — 6 papers, h 9
  • L. Tran — 6 papers, h 2
  • L. Tran — 5 papers, h 3
  • L. Tran — 4 papers, h 8
  • L. Tran — 4 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182021
most citedCauchy-Schwarz Regularized Autoencoder

1 citations · 1 across the 2 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2021

Group-disentangled Representation Learning with Weakly-Supervised Regularization

Linh Tran, Amir Hosein Khasahmadi, Aditya Sanghi +1

Learning interpretable and human-controllable representations that uncover factors of variation in data remains an ongoing key challenge in representation learning. We investigate…

cs.LG2021★ 1 cited

Cauchy-Schwarz Regularized Autoencoder

Linh Tran, Maja Pantic, Marc Peter Deisenroth

Recent work in unsupervised learning has focused on efficient inference and learning in latent variables models. Training these models by maximizing the evidence (marginal likeliho…

cs.LG2020

The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks

Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling +7

Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights. Recent work developing this class of methods…

cs.LG2020

Hydra: Preserving Ensemble Diversity for Model Distillation

Linh Tran, Bastiaan S. Veeling, Kevin Roth +7

Ensembles of models have been empirically shown to improve predictive performance and to yield robust measures of uncertainty. However, they are expensive in computation and memory…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.