no code implementations • NeurIPS 2018 • Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos
Learning in small sample regimes is among the most remarkable features of the human perceptual system.
no code implementations • 14 Mar 2017 • Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos
We introduce a framework for weakly supervised learning of image embeddings that are robust to transformations and selective to the class distribution, using sets of transforming examples (orbit sets), deep parametrizations and a novel orbit-based loss.
no code implementations • 16 Jun 2014 • Georgios Evangelopoulos, Stephen Voinea, Chiyuan Zhang, Lorenzo Rosasco, Tomaso Poggio
Recognition of speech, and in particular the ability to generalize and learn from small sets of labelled examples like humans do, depends on an appropriate representation of the acoustic input.
no code implementations • 1 Apr 2014 • Chiyuan Zhang, Georgios Evangelopoulos, Stephen Voinea, Lorenzo Rosasco, Tomaso Poggio
We present the main theoretical and computational aspects of a framework for unsupervised learning of invariant audio representations, empirically evaluated on music genre classification.