-
Ranking Deep Learning Generalization using Label Variation in Latent Geometry Graphs
Measuring the generalization performance of a Deep Neural Network (DNN) ...
11/25/2020 ∙ by Carlos Lassance, et al. ∙
0
∙
share
read it
-
Few-shot Learning for Decoding Brain Signals
Few-shot learning consists in addressing data-thrifty (inductive few-sho...
10/23/2020 ∙ by Myriam Bontonou, et al. ∙
0
∙
share
read it
-
Predicting the Accuracy of a Few-Shot Classifier
In the context of few-shot learning, one cannot measure the generalizati...
07/08/2020 ∙ by Myriam Bontonou, et al. ∙
0
∙
share
read it
-
Deep geometric knowledge distillation with graphs
In most cases deep learning architectures are trained disregarding the a...
11/08/2019 ∙ by Carlos Lassance, et al. ∙
26
∙
share
read it
-
Comparing linear structure-based and data-driven latent spatial representations for sequence prediction
Predicting the future of Graph-supported Time Series (GTS) is a key chal...
08/19/2019 ∙ by Myriam Bontonou, et al. ∙
0
∙
share
read it
-
A Unified Deep Learning Formalism For Processing Graph Signals
Convolutional Neural Networks are very efficient at processing signals d...
05/01/2019 ∙ by Myriam Bontonou, et al. ∙
0
∙
share
read it
-
Introducing Graph Smoothness Loss for Training Deep Learning Architectures
We introduce a novel loss function for training deep learning architectu...
05/01/2019 ∙ by Myriam Bontonou, et al. ∙
20
∙
share
read it
