Publication

Found 160 results
Author Title [ Type(Desc)] Year
Filters: Author is Tomaso Poggio  [Clear All Filters]
Conference Paper
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>
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).
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>
Banburski, A. et al. Weight and Batch Normalization implement Classical Generalization Bounds . ICML (2019).
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>
Conference Poster
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>
Lattman, E., Poggio, T. & Westervelt, R. NSF Science and Technology Centers – The Class of 2013. (2013).PDF icon NSFGender2013_poster.pdf (2.77 MB)
Voinea, S., Zhang, C., Evangelopoulos, G., Rosasco, L. & Poggio, T. Speech Representations based on a Theory for Learning Invariances. (2014).
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)
Anselmi, F. et al. Unsupervised Learning of Invariant Representations in Hierarchical Architectures. (2013).PDF icon 1311.4158v2.pdf (3.78 MB)
Mhaskar, H., Liao, Q. & Poggio, T. When and Why Are Deep Networks Better Than Shallow Ones?. AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence (2017).
Dataset
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)
Isik, L., Meyers, E., Leibo, J. Z. & Poggio, T. The dynamics of invariant object recognition in the human visual system. (2014). doi:http://dx.doi.org/10.7910/DVN/KRUPXZ
Leibo, J. Z., Liao, Q., Anselmi, F. & Poggio, T. The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. (2015).Binary Data modularity_dataset_ver1.tar.gz (36.14 MB)
Tacchetti, A., Isik, L. & Poggio, T. Invariant action recognition dataset. (2017). at <https://doi.org/10.7910/DVN/DMT0PG>
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)
Leibo, J. Z., Liao, Q. & Poggio, T. Subtasks of unconstrained face recognition. (2014).
Garrote, E. et al. System for Mouse Behavior Recognition. (2010).
Journal Article
Mhaskar, H. & Poggio, T. An analysis of training and generalization errors in shallow and deep networks. Neural Networks 121, 229 - 241 (2020).
Poggio, T., Liao, Q. & Banburski, A. Complexity Control by Gradient Descent in Deep Networks. Nature Communications 11, (2020).PDF icon s41467-020-14663-9.pdf (431.68 KB)
Poggio, T. & Fraser, M. Compositional sparsity of learnable functions. Bulletin of the American Mathematical Society 61, 438-456 (2024).
Chandrasekhar, V. et al. Compression of Deep Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1701.04923>PDF icon 1701.04923.pdf (614.33 KB)
Poggio, T. Deep Leaning: Mathematics and Neuroscience. A Sponsored Supplement to Science Brain-Inspired intelligent robotics: The intersection of robotics and neuroscience, 9-12 (2016).
Adler, A., Araya-Polo, M. & Poggio, T. Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows. IEEE Signal Processing Magazine 38, 89 - 119 (2021).

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