4 papers
Neural Field Tokenizations with Hierarchy and Spatial Locality Priors
Alonso Urbano, David W. Romero, Max Zimmer +1
Neural fields parameterize data as functions from coordinates to values, providing a unified framework for representation learning across modalities. Existing approaches are domina…
RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization
Alonso Urbano, David W. Romero, Max Zimmer +1
Real world data often exhibits unknown, instance-specific symmetries that rarely exactly match a transformation group fixed a priori. Class-pose decompositions aim to create di…
The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases
David W. Romero
The emergence of Deep Learning has marked a profound shift in machine learning, driven by numerous breakthroughs achieved in recent years. However, as Deep Learning becomes increas…
Self-Supervised Detection of Perfect and Partial Input-Dependent Symmetries
Alonso Urbano, David W. Romero
Group equivariance can overly constrain models if the symmetries in the group differ from those observed in data. While common methods address this by determining the appropriate l…