62 citations · 135 across the 5 of their papers we have counts for
5 papers
Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets
Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos
The complexity of a learning task is increased by transformations in the input space that preserve class identity. Visual object recognition for example is affected by changes in v…
Invariant recognition drives neural representations of action sequences
Andrea Tacchetti, Leyla Isik, Tomaso Poggio
Recognizing the actions of others from visual stimuli is a crucial aspect of human visual perception that allows individuals to respond to social cues. Humans are able to identify…
Learning with incremental iterative regularization
Lorenzo Rosasco, Silvia Villa
Within a statistical learning setting, we propose and study an iterative regularization algorithm for least squares defined by an incremental gradient method. In particular, we sho…
Unsupervised Learning of Invariant Representations in Hierarchical Architectures
Fabio Anselmi, Joel Z. Leibo, Lorenzo Rosasco +3
The present phase of Machine Learning is characterized by supervised learning algorithms relying on large sets of labeled examples (). The next phase is likely to foc…
GURLS: a Least Squares Library for Supervised Learning
Andrea Tacchetti, Pavan K Mallapragada, Matteo Santoro +1
We present GURLS, a least squares, modular, easy-to-extend software library for efficient supervised learning. GURLS is targeted to machine learning practitioners, as well as non-s…