6 papers · 1 filter
Bridging Functional and Representational Similarity via Usable Information
Antonio Almudévar, Alfonso Ortega
We present a unified framework for quantifying the similarity between representations through the lens of \textit{usable} information, offering a rigorous theoretical and empirical…
Representation Unlearning: Forgetting through Information Compression
Antonio Almudévar, Alfonso Ortega
Machine unlearning seeks to remove the influence of specific training data from a model, a need driven by privacy regulations and robustness concerns. Existing approaches typically…
Aligning Multimodal Representations through an Information Bottleneck
Antonio Almudévar, José Miguel Hernández-Lobato, Sameer Khurana +2
Contrastive losses have been extensively used as a tool for multimodal representation learning. However, it has been empirically observed that their use is not effective to learn a…
There Was Never a Bottleneck in Concept Bottleneck Models
Antonio Almudévar, José Miguel Hernández-Lobato, Alfonso Ortega
Deep learning representations are often difficult to interpret, which can hinder their deployment in sensitive applications. Concept Bottleneck Models (CBMs) have emerged as a prom…
Predefined Prototypes for Intra-Class Separation and Disentanglement
Antonio Almudévar, Théo Mariotte, Alfonso Ortega +4
Prototypical Learning is based on the idea that there is a point (which we call prototype) around which the embeddings of a class are clustered. It has shown promising results in s…
Unsupervised Multiple Domain Translation through Controlled Disentanglement in Variational Autoencoder
Antonio Almudévar, Théo Mariotte, Alfonso Ortega +1
Unsupervised Multiple Domain Translation is the task of transforming data from one domain to other domains without having paired data to train the systems. Typically, methods based…