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Found 7 papers, 4 papers with code
Self Supervised Correlation-based Permutations for Multi-View Clustering
Fusing information from different modalities can enhance data analysis tasks, including clustering.
Interpretable Deep Clustering
Furthermore, we verify that our model leads to interpretable results at a sample and cluster level.
SG-VAD: Stochastic Gates Based Speech Activity Detection
Our key idea is to model VAD as a denoising task, and construct a network that is designed to identify nuisance features for a speech classification task.
Ranked #3 on
Activity Detection
on AVA-Speech
(ROC-AUC metric)
Discovery of Single Independent Latent Variable
Latent variable discovery is a central problem in data analysis with a broad range of applications in applied science.
Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features
Experimenting on several real-world datasets, we demonstrate that our proposed approach outperforms similar approaches designed to avoid only correlated or nuisance features, but not both.
Deep Ordinal Regression using Optimal Transport Loss and Unimodal Output Probabilities
We empirically analyze the different components of our proposed approach and demonstrate their contribution to the performance of the model.
Ranked #8 on
Age And Gender Classification
on Adience Age
Differentiable Unsupervised Feature Selection based on a Gated Laplacian
In this paper, we present a method for unsupervised feature selection, and we demonstrate its use for the task of clustering.
