Publication

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2014
Leibo, J. Z., Liao, Q., Anselmi, F. & Poggio, T. The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. (2014). doi:10.1101/004473PDF icon CBMM Memo 004_new.pdf (2.25 MB)
Evangelopoulos, G., Voinea, S., Zhang, C., Rosasco, L. & Poggio, T. Learning An Invariant Speech Representation. (2014).PDF icon CBMM-Memo-022-1406.3884v1.pdf (1.81 MB)
Liao, Q., Leibo, J. Z. & Poggio, T. Learning invariant representations and applications to face verification. NIPS 2013 (Advances in Neural Information Processing Systems 26, 2014). at <http://nips.cc/Conferences/2013/Program/event.php?ID=4074>PDF icon Liao_Leibo_Poggio_NIPS_2013.pdf (687.06 KB)
Tan, C. & Poggio, T. Neural tuning size is a key factor underlying holistic face processing. (2014).PDF icon CBMM-Memo-021-1406.3793.pdf (387.79 KB)
Zhang, C., Voinea, S., Evangelopoulos, G., Rosasco, L. & Poggio, T. Phone Classification by a Hierarchy of Invariant Representation Layers. INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association (International Speech Communication Association (ISCA), 2014). at <http://www.isca-speech.org/archive/interspeech_2014/i14_2346.html>
Anselmi, F. & Poggio, T. Representation Learning in Sensory Cortex: a theory. (2014).PDF icon CBMM-Memo-026_neuron_ver45.pdf (1.35 MB)
Poggio, T. Is Research in Intelligence an Existential Risk?. (2014).PDF icon Is Research in Intelligence an Existential Risk.pdf (571.42 KB)
Voinea, S., Zhang, C., Evangelopoulos, G., Rosasco, L. & Poggio, T. Speech Representations based on a Theory for Learning Invariances. (2014).
Leibo, J. Z., Liao, Q. & Poggio, T. Subtasks of unconstrained face recognition. (2014).
Leibo, J. Z., Liao, Q. & Poggio, T. Subtasks of Unconstrained Face Recognition. (2014).PDF icon Leibo_Liao_Poggio_subtasks_VISAPP_2014.pdf (268.69 KB)
Poggio, T. & Squire, L. R. The History of Neuroscience in Autobiography Volume 8 8, (Society for Neuroscience, 2014).PDF icon Volume Introduction and Preface (232.8 KB)PDF icon TomasoPoggio.pdf (1.43 MB)
Liao, Q., Leibo, J. Z. & Poggio, T. Unsupervised learning of clutter-resistant visual representations from natural videos. (2014).PDF icon 1409.3879v2.pdf (3.64 MB)
Anselmi, F. et al. Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?. (2014).PDF icon CBMM Memo No. 001 (940.36 KB)
Voinea, S., Zhang, C., Evangelopoulos, G., Rosasco, L. & Poggio, T. Word-level Invariant Representations From Acoustic Waveforms. INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association (International Speech Communication Association (ISCA), 2014). at <http://www.isca-speech.org/archive/interspeech_2014/i14_2385.html>
2013
Villa, S. et al. Empirical Inference 59 - 69 (Springer Berlin Heidelberg, 2013). doi:10.1007/978-3-642-41136-610.1007/978-3-642-41136-6_7PDF icon Author's Version (147.25 KB)
Lattman, E., Poggio, T. & Westervelt, R. NSF Science and Technology Centers – The Class of 2013. (2013).PDF icon NSFGender2013_poster.pdf (2.77 MB)
Anselmi, F. et al. Unsupervised Learning of Invariant Representations in Hierarchical Architectures. (2013).PDF icon 1311.4158v2.pdf (3.78 MB)
2012
Canas, G. D., Poggio, T. & Rosasco, L. Learning manifolds with k-means and k-flats. Advances in Neural Information Processing Systems 25 (NIPS 2012) (2012). at <https://papers.nips.cc/paper/2012/hash/b20bb95ab626d93fd976af958fbc61ba-Abstract.html>
2011
Garrote, E., Jhuang, H., Huehne, H., Poggio, T. & Serre, T. A Large Video Database for Human Motion Recognition. (2011).PDF icon Kuehne_etal_ICCV2011.pdf (433.27 KB)
2010
Mutch, J., Knoblich, U. & Poggio, T. CNS (“Cortical Network Simulator”): a GPU-based framework for simulating cortically-organized networks. (2010).File cns.tar (1.46 MB)PDF icon MIT-CSAIL-TR-2010-013.pdf (389.38 KB)File (last version before switch to classdef syntax)  (1.05 MB)
Garrote, E. et al. System for Mouse Behavior Recognition. (2010).

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