91 citations · 103 across the 4 of their papers we have counts for
4 papers
Self-Supervised Siamese Autoencoders
Friederike Baier, Sebastian Mair, Samuel G. Fadel
In contrast to fully-supervised models, self-supervised representation learning only needs a fraction of data to be labeled and often achieves the same or even higher downstream pe…
Principled Interpolation in Normalizing Flows
Samuel G. Fadel, Sebastian Mair, Ricardo da S. Torres +1
Generative models based on normalizing flows are very successful in modeling complex data distributions using simpler ones. However, straightforward linear interpolations show unex…
Link Prediction in Dynamic Graphs for Recommendation
Samuel G. Fadel, Ricardo da S. Torres
Recent advances in employing neural networks on graph domains helped push the state of the art in link prediction tasks, particularly in recommendation services. However, the use o…
Exploiting ConvNet Diversity for Flooding Identification
Keiller Nogueira, Samuel G. Fadel, Ícaro C. Dourado +7
Flooding is the world's most costly type of natural disaster in terms of both economic losses and human causalities. A first and essential procedure towards flood monitoring is bas…