15 citations · 19 across the 4 of their papers we have counts for
5 papers
Homomorphic Self-Supervised Learning
T. Anderson Keller, Xavier Suau, Luca Zappella
In this work, we observe that many existing self-supervised learning algorithms can be both unified and generalized when seen through the lens of equivariant representations. Speci…
Contrastive Self-Supervised Learning for Skeleton Representations
Nico Lingg, Miguel Sarabia, Luca Zappella +1
Human skeleton point clouds are commonly used to automatically classify and predict the behaviour of others. In this paper, we use a contrastive self-supervised learning method, Si…
Fair SA: Sensitivity Analysis for Fairness in Face Recognition
Aparna R. Joshi, Xavier Suau, Nivedha Sivakumar +2
As the use of deep learning in high impact domains becomes ubiquitous, it is increasingly important to assess the resilience of models. One such high impact domain is that of face…
Finding Experts in Transformer Models
Xavier Suau, Luca Zappella, Nicholas Apostoloff
In this work we study the presence of expert units in pre-trained Transformer Models (TM), and how they impact a model's performance. We define expert units to be neurons that are…
Filter Distillation for Network Compression
Xavier Suau, Luca Zappella, Nicholas Apostoloff
In this paper we introduce Principal Filter Analysis (PFA), an easy to use and effective method for neural network compression. PFA exploits the correlation between filter response…