35 citations · 98 across the 11 of their papers we have counts for
12 papers
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…
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…
Noise-robust classification with hypergraph neural network
Nguyen Trinh Vu Dang, Loc Tran, Linh Tran
This paper presents a novel version of the hypergraph neural network method. This method is utilized to solve the noisy label learning problem. First, we apply the PCA dimensional…
Directed hypergraph neural network
Loc Hoang Tran, Linh Hoang Tran
To deal with irregular data structure, graph convolution neural networks have been developed by a lot of data scientists. However, data scientists just have concentrated primarily…
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…
How Good is the Bayes Posterior in Deep Neural Networks Really?
Florian Wenzel, Kevin Roth, Bastiaan S. Veeling +7
During the past five years the Bayesian deep learning community has developed increasingly accurate and efficient approximate inference procedures that allow for Bayesian inference…