Publication

Found 158 results
Author Title [ Type(Asc)] Year
Filters: Author is Tomaso Poggio  [Clear All Filters]
Conference Proceedings
Han, Y., Poggio, T. & Cheung, B. System Identification of Neural Systems: If We Got It Right, Would We Know?. Proceedings of the 40th International Conference on Machine Learning, PMLR 202, 12430-12444 (2023).PDF icon han23d.pdf (797.48 KB)
Conference Poster
Voinea, S., Zhang, C., Evangelopoulos, G., Rosasco, L. & Poggio, T. Speech Representations based on a Theory for Learning Invariances. (2014).
Lattman, E., Poggio, T. & Westervelt, R. NSF Science and Technology Centers – The Class of 2013. (2013).PDF icon NSFGender2013_poster.pdf (2.77 MB)
Gant, J., Banburski, A., Deza, A. & Poggio, T. Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN. NeurIPS 2021 (2021). at <https://nips.cc/Conferences/2021/Schedule?showEvent=21868>
Conference Paper
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>
Banburski, A. et al. Weight and Batch Normalization implement Classical Generalization Bounds . ICML (2019).
Tacchetti, A., Voinea, S., Evangelopoulos, G. & Poggio, T. Representation Learning from Orbit Sets for One-shot Classification. AAAI Spring Symposium Series, Science of Intelligence (2017). at <https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15357>
Han, Y., Roig, G., Geiger, G. & Poggio, T. Properties of invariant object recognition in human oneshot learning suggests a hierarchical architecture different from deep convolutional neural networks . Vision Science Society (2019). doi:10.1167/19.10.28d
Han, Y., Roig, G., Geiger, G. & Poggio, T. Properties of invariant object recognition in human one-shot learning suggests a hierarchical architecture different from deep convolutional neural networks. Vision Science Society (2019).
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>
Galanti, T., Xu, M., Galanti, L. & Poggio, T. Norm-based Generalization Bounds for Sparse Neural Networks. NeurIPS 2023 (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/file/8493e190ff1bbe3837eca821190b61ff-Paper-Conference.pdf>PDF icon NeurIPS-2023-norm-based-generalization-bounds-for-sparse-neural-networks-Paper-Conference.pdf (577.69 KB)
Mroueh, Y., Voinea, S. & Poggio, T. Learning with Group Invariant Features: A Kernel Perspective. NIPS 2015 (2015). at <https://papers.nips.cc/paper/5798-learning-with-group-invariant-features-a-kernel-perspective>PDF icon LearningInvarianceKernel_NIPS2015.pdf (292.18 KB)
Frogner, C., Zhang, C., Mobahi, H., Araya-Polo, M. & Poggio, T. Learning with a Wasserstein Loss. Advances in Neural Information Processing Systems (NIPS 2015) 28 (2015). at <http://arxiv.org/abs/1506.05439>PDF icon Learning with a Wasserstein Loss_1506.05439v2.pdf (2.57 MB)
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>
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)
Han, Y., Roig, G., Geiger, G. & Poggio, T. Is the Human Visual System Invariant to Translation and Scale?. AAAI Spring Symposium Series, Science of Intelligence (2017).
Liao, Q., Leibo, J. Z. & Poggio, T. How Important Is Weight Symmetry in Backpropagation?. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (2016). at <https://cbmm.mit.edu/sites/default/files/publications/liao-leibo-poggio.pdf>
Liao, Q., Leibo, J. Z. & Poggio, T. How Important Is Weight Symmetry in Backpropagation?. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (Association for the Advancement of Artificial Intelligence, 2016).PDF icon liao-leibo-poggio.pdf (191.91 KB)
Nickel, M., Rosasco, L. & Poggio, T. Holographic Embeddings of Knowledge Graphs. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (2016).PDF icon 1510.04935v2.pdf (360.65 KB)
Zhang, J., Han, Y., Poggio, T. & Roig, G. Eccentricity Dependent Neural Network with Recurrent Attention for Scale, Translation and Clutter Invariance . Vision Science Society (2019).
Chen, F., Roig, G., Isik, L., Boix, X. & Poggio, T. Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision. AAAI Spring Symposium Series, Science of Intelligence (2017). at <https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15360>PDF icon paper.pdf (963.87 KB)
Banburski, A. et al. Dynamics & Generalization in Deep Networks -Minimizing the Norm. NAS Sackler Colloquium on Science of Deep Learning (2019).
Zhang, C., Voinea, S., Evangelopoulos, G., Rosasco, L. & Poggio, T. Discriminative Template Learning in Group-Convolutional Networks for Invariant Speech Representations. INTERSPEECH-2015 (International Speech Communication Association (ISCA), 2015). at <http://www.isca-speech.org/archive/interspeech_2015/i15_3229.html>
Zhang, C., Evangelopoulos, G., Voinea, S., Rosasco, L. & Poggio, T. A Deep Representation for Invariance and Music Classification. ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 2014). doi:10.1109/ICASSP.2014.6854954
Adler, A., Araya-Polo, M. & Poggio, T. Deep Recurrent Architectures for Seismic Tomography. 81st EAGE Conference and Exhibition 2019 (2019).

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