42 citations · 46 across the 8 of their papers we have counts for
11 papers
Rethinking Robustness in Machine Learning: A Posterior Agreement Approach
João Borges S. Carvalho, Victor Jimenez Rodriguez, Alessandro Torcinovich +4
The robustness of algorithms against covariate shifts is a fundamental problem with critical implications for the deployment of machine learning algorithms in the real world. Curre…
Boosting CNN-based Handwriting Recognition Systems with Learnable Relaxation Labeling
Sara Ferro, Alessandro Torcinovich, Arianna Traviglia +1
The primary challenge for handwriting recognition systems lies in managing long-range contextual dependencies, an issue that traditional models often struggle with. To mitigate it,…
Measuring Orthogonality in Representations of Generative Models
Robin C. Geyer, Alessandro Torcinovich, João B. Carvalho +2
In unsupervised representation learning, models aim to distill essential features from high-dimensional data into lower-dimensional learned representations, guided by inductive bia…
On generalized KKT points for the Motzkin-Straus program
G. Beretta, A. Torcinovich, M. Pelillo
In 1965, T. S. Motzkin and E. G. Straus established an elegant connection between the clique number of a graph and the global maxima of a quadratic program defined on the standard…
Multi-Phase Relaxation Labeling for Square Jigsaw Puzzle Solving
Ben Vardi, Alessandro Torcinovich, Marina Khoroshiltseva +2
We present a novel method for solving square jigsaw puzzles based on global optimization. The method is fully automatic, assumes no prior information, and can handle puzzles with k…
The Group Loss++: A deeper look into group loss for deep metric learning
Ismail Elezi, Jenny Seidenschwarz, Laurin Wagner +4
Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings…