Search Results for author:
Found 7 papers, 6 papers with code
Unity by Diversity: Improved Representation Learning in Multimodal VAEs
Such architectures impose hard constraints on the model.
M(otion)-mode Based Prediction of Ejection Fraction using Echocardiograms
Early detection of cardiac dysfunction through routine screening is vital for diagnosing cardiovascular diseases.
Differentiable Random Partition Models
Partitioning a set of elements into an unknown number of mutually exclusive subsets is essential in many machine learning problems.
Learning Group Importance using the Differentiable Hypergeometric Distribution
We introduce reparameterizable gradients to learn the importance between groups and highlight the advantage of explicitly learning the size of subsets in two typical applications: weakly-supervised learning and clustering.
On the Limitations of Multimodal VAEs
Multimodal variational autoencoders (VAEs) have shown promise as efficient generative models for weakly-supervised data.
Generalized Multimodal ELBO
Multiple data types naturally co-occur when describing real-world phenomena and learning from them is a long-standing goal in machine learning research.
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
Learning from different data types is a long-standing goal in machine learning research, as multiple information sources co-occur when describing natural phenomena.
