5 citations · 6 across the 2 of their papers we have counts for
5 papers
Interpretable Tensor Fusion
Saurabh Varshneya, Antoine Ledent, Philipp Liznerski +4
Conventional machine learning methods are predominantly designed to predict outcomes based on a single data type. However, practical applications may encompass data of diverse type…
Learning Interpretable Concept Groups in CNNs
Saurabh Varshneya, Antoine Ledent, Robert A. Vandermeulen +4
We propose a novel training methodology -- Concept Group Learning (CGL) -- that encourages training of interpretable CNN filters by partitioning filters in each layer into concept…
Fine-grained Generalization Analysis of Structured Output Prediction
Waleed Mustafa, Yunwen Lei, Antoine Ledent +1
In machine learning we often encounter structured output prediction problems (SOPPs), i.e. problems where the output space admits a rich internal structure. Application domains whe…
Fine-grained Generalization Analysis of Vector-valued Learning
Liang Wu, Antoine Ledent, Yunwen Lei +1
Many fundamental machine learning tasks can be formulated as a problem of learning with vector-valued functions, where we learn multiple scalar-valued functions together. Although…
Norm-based generalisation bounds for multi-class convolutional neural networks
Antoine Ledent, Waleed Mustafa, Yunwen Lei +1
We show generalisation error bounds for deep learning with two main improvements over the state of the art. (1) Our bounds have no explicit dependence on the number of classes exce…