3 citations · 5 across the 10 of their papers we have counts for
4 papers · 1 filter
Enhancing Hyperspectral Image Prediction with Contrastive Learning in Low-Label Regime
Salma Haidar, José Oramas
Self-supervised contrastive learning is an effective approach for addressing the challenge of limited labelled data. This study builds upon the previously established two-stage pat…
Deep Model Interpretation with Limited Data : A Coreset-based Approach
Hamed Behzadi-Khormouji, José Oramas
Model Interpretation aims at the extraction of insights from the internals of a trained model. A common approach to address this task is the characterization of relevant features i…
Bilinear MLPs enable weight-based mechanistic interpretability
Michael T. Pearce, Thomas Dooms, Alice Rigg +2
A mechanistic understanding of how MLPs do computation in deep neural networks remains elusive. Current interpretability work can extract features from hidden activations over an i…
Smooth InfoMax -- Towards Easier Post-Hoc Interpretability
Fabian Denoodt, Bart de Boer, José Oramas
We introduce Smooth InfoMax (SIM), a self-supervised representation learning method that incorporates interpretability constraints into the latent representations at different dept…