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ORCIDTomaso A. Poggio
Person information
- affiliation: Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory
- award (1992): Max Planck Research Award
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2020 – today
- 2024
[i60]Pierfrancesco Beneventano, Andrea Pinto, Tomaso A. Poggio:
How Neural Networks Learn the Support is an Implicit Regularization Effect of SGD. CoRR abs/2406.11110 (2024)- [i59]Yulu Gan, Tomer Galanti, Tomaso A. Poggio, Eran Malach:
On the Power of Decision Trees in Auto-Regressive Language Modeling. CoRR abs/2409.19150 (2024) - [i58]Ziyin Liu, Isaac Chuang, Tomer Galanti, Tomaso A. Poggio:
Formation of Representations in Neural Networks. CoRR abs/2410.03006 (2024) - [i57]Vighnesh Subramaniam, David Mayo, Colin Conwell, Tomaso A. Poggio, Boris Katz, Brian Cheung, Andrei Barbu:
Training the Untrainable: Introducing Inductive Bias via Representational Alignment. CoRR abs/2410.20035 (2024) - 2023
- [c102]Yena Han, Tomaso A. Poggio, Brian Cheung:
System Identification of Neural Systems: If We Got It Right, Would We Know? ICML 2023: 12430-12444 - [c101]Akshay Rangamani, Marius Lindegaard, Tomer Galanti, Tomaso A. Poggio:
Feature learning in deep classifiers through Intermediate Neural Collapse. ICML 2023: 28729-28745 - [c100]Tomer Galanti, Mengjia Xu, Liane Galanti, Tomaso A. Poggio:
Norm-based Generalization Bounds for Sparse Neural Networks. NeurIPS 2023 - [i56]Tomer Galanti, Mengjia Xu, Liane Galanti, Tomaso A. Poggio:
Norm-based Generalization Bounds for Compositionally Sparse Neural Networks. CoRR abs/2301.12033 (2023) - [i55]Yena Han, Tomaso A. Poggio, Brian Cheung:
System identification of neural systems: If we got it right, would we know? CoRR abs/2302.06677 (2023) - [i54]Utkarsh Singhal, Brian Cheung, Kartik Chandra, Jonathan Ragan-Kelley, Joshua B. Tenenbaum, Tomaso A. Poggio, Stella X. Yu:
How to guess a gradient. CoRR abs/2312.04709 (2023) - 2022
- [j68]Fabio Anselmi
, Tomaso A. Poggio:
Representation Learning in Sensory Cortex: A Theory. IEEE Access 10: 102475-102491 (2022) - [c99]Dagen Braun, Matthew D. Reisman, Larry Dewell, Andrzej Banburski-Fahey, Arturo Deza, Tomaso A. Poggio:
Achieving Adversarial Robustness in Deep Learning-Based Overhead Imaging. AIPR 2022: 1-7 - [i53]Tomer Galanti, Tomaso A. Poggio:
SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks. CoRR abs/2206.05794 (2022) - [i52]Vassilis Apidopoulos, Tomaso A. Poggio, Lorenzo Rosasco, Silvia Villa:
Iterative regularization in classification via hinge loss diagonal descent. CoRR abs/2212.12675 (2022) - 2021
- [j67]Amir Adler, Mauricio Araya-Polo, Tomaso A. Poggio:
Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows. IEEE Signal Process. Mag. 38(2): 89-119 (2021) - [c98]Simon Alford, Anshula Gandhi, Akshay Rangamani, Andrzej Banburski, Tony Wang, Sylee Dandekar, John Chin, Tomaso A. Poggio, Peter Chin:
Neural-Guided, Bidirectional Program Search for Abstraction and Reasoning. COMPLEX NETWORKS 2021: 657-668 - [i51]Tomaso A. Poggio, Qianli Liao:
Explicit regularization and implicit bias in deep network classifiers trained with the square loss. CoRR abs/2101.00072 (2021) - [i50]Owen Kunhardt, Arturo Deza, Tomaso A. Poggio:
The Effects of Image Distribution and Task on Adversarial Robustness. CoRR abs/2102.10534 (2021) - [i49]Andrzej Banburski, Fernanda De La Torre, Nishka Pant, Ishana Shastri, Tomaso A. Poggio:
Distribution of Classification Margins: Are All Data Equal? CoRR abs/2107.10199 (2021) - [i48]Simon Alford, Anshula Gandhi, Akshay Rangamani, Andrzej Banburski, Tony Wang, Sylee Dandekar, John Chin, Tomaso A. Poggio, Peter Chin:
Neural-guided, Bidirectional Program Search for Abstraction and Reasoning. CoRR abs/2110.11536 (2021) - 2020
- [j66]Hrushikesh N. Mhaskar, Tomaso A. Poggio:
An analysis of training and generalization errors in shallow and deep networks. Neural Networks 121: 229-241 (2020) - [c97]Charlie Frogner, Tomaso A. Poggio:
Approximate Inference with Wasserstein Gradient Flows. AISTATS 2020: 2581-2590 - [c96]Alexandra Proca, Andrzej Banburski, Tomaso A. Poggio:
Cross-Domain Adversarial Reprogramming of a Recurrent Neural Network. CogSci 2020 - [c95]Manish V. Reddy, Andrzej Banburski, Nishka Pant, Tomaso A. Poggio:
Biologically Inspired Mechanisms for Adversarial Robustness. NeurIPS 2020 - [i47]Arturo Deza, Qianli Liao, Andrzej Banburski, Tomaso A. Poggio:
Hierarchically Local Tasks and Deep Convolutional Networks. CoRR abs/2006.13915 (2020) - [i46]Akshay Rangamani, Lorenzo Rosasco, Tomaso A. Poggio:
For interpolating kernel machines, the minimum norm ERM solution is the most stable. CoRR abs/2006.15522 (2020) - [i45]Manish V. Reddy, Andrzej Banburski, Nishka Pant, Tomaso A. Poggio:
Biologically Inspired Mechanisms for Adversarial Robustness. CoRR abs/2006.16427 (2020) - [i44]Elian Malkin, Arturo Deza, Tomaso A. Poggio:
CUDA-Optimized real-time rendering of a Foveated Visual System. CoRR abs/2012.08655 (2020)
2010 – 2019
- 2019
- [j65]Fabio Anselmi, Georgios Evangelopoulos, Lorenzo Rosasco, Tomaso A. Poggio:
Symmetry-adapted representation learning. Pattern Recognit. 86: 201-208 (2019) - [c94]Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes:
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. AISTATS 2019: 888-896 - [c93]Will Xiao, Honglin Chen, Qianli Liao, Tomaso A. Poggio:
Biologically-Plausible Learning Algorithms Can Scale to Large Datasets. ICLR (Poster) 2019 - [c92]Charlie Frogner, Tomaso A. Poggio:
Fast and Flexible Inference of Joint Distributions from their Marginals. ICML 2019: 2002-2011 - [i43]Andrzej Banburski, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Bob Liang, Jack Hidary, Tomaso A. Poggio:
Theory III: Dynamics and Generalization in Deep Networks. CoRR abs/1903.04991 (2019) - [i42]Hrushikesh N. Mhaskar, Tomaso A. Poggio:
Function approximation by deep networks. CoRR abs/1905.12882 (2019) - [i41]Tomaso A. Poggio, Andrzej Banburski, Qianli Liao:
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization. CoRR abs/1908.09375 (2019) - [i40]Tomaso A. Poggio, Gil Kur, Andrzej Banburski:
Double descent in the condition number. CoRR abs/1912.06190 (2019) - 2018
- [i39]Tomaso A. Poggio, Kenji Kawaguchi, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Xavier Boix, Jack Hidary, Hrushikesh N. Mhaskar:
Theory of Deep Learning III: explaining the non-overfitting puzzle. CoRR abs/1801.00173 (2018) - [i38]Chiyuan Zhang, Qianli Liao, Alexander Rakhlin, Brando Miranda, Noah Golowich, Tomaso A. Poggio:
Theory of Deep Learning IIb: Optimization Properties of SGD. CoRR abs/1801.02254 (2018) - [i37]Hrushikesh N. Mhaskar, Tomaso A. Poggio:
An analysis of training and generalization errors in shallow and deep networks. CoRR abs/1802.06266 (2018) - [i36]Charlie Frogner, Tomaso A. Poggio:
Approximate inference with Wasserstein gradient flows. CoRR abs/1806.04542 (2018) - [i35]Tomaso A. Poggio, Qianli Liao, Brando Miranda, Andrzej Banburski, Xavier Boix, Jack Hidary:
Theory IIIb: Generalization in Deep Networks. CoRR abs/1806.11379 (2018) - [i34]Qianli Liao, Brando Miranda, Andrzej Banburski, Jack Hidary, Tomaso A. Poggio:
A Surprising Linear Relationship Predicts Test Performance in Deep Networks. CoRR abs/1807.09659 (2018) - [i33]Will Xiao, Honglin Chen, Qianli Liao, Tomaso A. Poggio:
Biologically-plausible learning algorithms can scale to large datasets. CoRR abs/1811.03567 (2018) - 2017
- [j64]Tomaso A. Poggio, Hrushikesh N. Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao:
Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. Int. J. Autom. Comput. 14(5): 503-519 (2017) - [j63]Andrea Tacchetti, Leyla Isik, Tomaso A. Poggio:
Invariant recognition drives neural representations of action sequences. PLoS Comput. Biol. 13(12) (2017) - [c91]Hrushikesh N. Mhaskar, Qianli Liao, Tomaso A. Poggio:
When and Why Are Deep Networks Better Than Shallow Ones? AAAI 2017: 2343-2349 - [c90]Francis X. Chen, Gemma Roig, Leyla Isik, Xavier Boix, Tomaso A. Poggio:
Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision. AAAI Spring Symposia 2017 - [c89]Yena Han, Gemma Roig, Gadi Geiger, Tomaso A. Poggio:
Is the Human Visual System Invariant to Translation and Scale? AAAI Spring Symposia 2017 - [c88]Olivier Morère, Antoine Veillard, Jie Lin, Julie Petta, Vijay Chandrasekhar, Tomaso A. Poggio:
Group Invariant Deep Representations for Image Instance Retrieval. AAAI Spring Symposia 2017 - [c87]Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos, Tomaso A. Poggio:
Representation Learning from Orbit Sets for One-Shot Classification. AAAI Spring Symposia 2017 - [c86]Vijay Chandrasekhar, Jie Lin, Qianli Liao, Olivier Morère, Antoine Veillard, Ling-Yu Duan, Tomaso A. Poggio:
Compression of Deep Neural Networks for Image Instance Retrieval. DCC 2017: 300-309 - [c85]Olivier Morère, Jie Lin, Antoine Veillard, Ling-Yu Duan, Vijay Chandrasekhar, Tomaso A. Poggio:
Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval. ICMR 2017: 260-268 - [c84]Anna Volokitin, Gemma Roig, Tomaso A. Poggio:
Do Deep Neural Networks Suffer from Crowding? NIPS 2017: 5628-5638 - [i32]Vijay Chandrasekhar, Jie Lin, Qianli Liao, Olivier Morère, Antoine Veillard, Ling-Yu Duan, Tomaso A. Poggio:
Compression of Deep Neural Networks for Image Instance Retrieval. CoRR abs/1701.04923 (2017) - [i31]Tomaso A. Poggio, Qianli Liao:
Theory II: Landscape of the Empirical Risk in Deep Learning. CoRR abs/1703.09833 (2017) - [i30]Anna Volokitin, Gemma Roig, Tomaso A. Poggio:
Do Deep Neural Networks Suffer from Crowding? CoRR abs/1706.08616 (2017) - [i29]Gaurav Manek, Jie Lin, Vijay Chandrasekhar, Lingyu Duan, Sateesh Giduthuri, Xiaoli Li, Tomaso A. Poggio:
Pruning Convolutional Neural Networks for Image Instance Retrieval. CoRR abs/1707.05455 (2017) - [i28]Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes:
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. CoRR abs/1711.01530 (2017) - 2016
- [j62]Tomaso A. Poggio, Ethan Meyers:
Turing++ Questions: A Test for the Science of (Human) Intelligence. AI Mag. 37(1): 73-77 (2016) - [j61]Fabio Anselmi, Joel Z. Leibo, Lorenzo Rosasco, Jim Mutch, Andrea Tacchetti, Tomaso A. Poggio:
Unsupervised learning of invariant representations. Theor. Comput. Sci. 633: 112-121 (2016) - [c83]Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio:
How Important Is Weight Symmetry in Backpropagation? AAAI 2016: 1837-1844 - [c82]Maximilian Nickel, Lorenzo Rosasco, Tomaso A. Poggio:
Holographic Embeddings of Knowledge Graphs. AAAI 2016: 1955-1961 - [i27]Olivier Morère, Antoine Veillard, Jie Lin, Julie Petta, Vijay Chandrasekhar, Tomaso A. Poggio:
Group Invariant Deep Representations for Image Instance Retrieval. CoRR abs/1601.02093 (2016) - [i26]Hrushikesh N. Mhaskar, Qianli Liao, Tomaso A. Poggio:
Learning Real and Boolean Functions: When Is Deep Better Than Shallow. CoRR abs/1603.00988 (2016) - [i25]Qianli Liao, Tomaso A. Poggio:
Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. CoRR abs/1604.03640 (2016) - [i24]Joel Z. Leibo, Qianli Liao, Winrich Freiwald, Fabio Anselmi, Tomaso A. Poggio:
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation. CoRR abs/1606.01552 (2016) - [i23]Hrushikesh N. Mhaskar, Tomaso A. Poggio:
Deep vs. shallow networks : An approximation theory perspective. CoRR abs/1608.03287 (2016) - [i22]Qianli Liao, Kenji Kawaguchi, Tomaso A. Poggio:
Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning. CoRR abs/1610.06160 (2016) - [i21]Tomaso A. Poggio, Hrushikesh N. Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao:
Why and When Can Deep - but Not Shallow - Networks Avoid the Curse of Dimensionality: a Review. CoRR abs/1611.00740 (2016) - 2015
- [j60]Joel Z. Leibo, Qianli Liao, Fabio Anselmi, Tomaso A. Poggio:
The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. PLoS Comput. Biol. 11(10) (2015) - [c81]Carlo Ciliberto, Youssef Mroueh, Tomaso A. Poggio, Lorenzo Rosasco:
Convex Learning of Multiple Tasks and their Structure. ICML 2015: 1548-1557 - [c80]Chiyuan Zhang, Stephen Voinea, Georgios Evangelopoulos, Lorenzo Rosasco, Tomaso A. Poggio:
Discriminative template learning in group-convolutional networks for invariant speech representations. INTERSPEECH 2015: 3229-3233 - [c79]Youssef Mroueh, Stephen Voinea, Tomaso A. Poggio:
Learning with Group Invariant Features: A Kernel Perspective. NIPS 2015: 1558-1566 - [c78]Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya-Polo, Tomaso A. Poggio:
Learning with a Wasserstein Loss. NIPS 2015: 2053-2061 - [i20]Fabio Anselmi, Lorenzo Rosasco, Tomaso A. Poggio:
On Invariance and Selectivity in Representation Learning. CoRR abs/1503.05938 (2015) - [i19]Carlo Ciliberto, Youssef Mroueh, Tomaso A. Poggio, Lorenzo Rosasco:
Convex Learning of Multiple Tasks and their Structure. CoRR abs/1504.03101 (2015) - [i18]Youssef Mroueh, Stephen Voinea, Tomaso A. Poggio:
Learning with Group Invariant Features: A Kernel Perspective. CoRR abs/1506.02544 (2015) - [i17]Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya-Polo, Tomaso A. Poggio:
Learning with a Wasserstein Loss. CoRR abs/1506.05439 (2015) - [i16]Fabio Anselmi, Lorenzo Rosasco, Cheston Tan, Tomaso A. Poggio:
Deep Convolutional Networks are Hierarchical Kernel Machines. CoRR abs/1508.01084 (2015) - [i15]Maximilian Nickel, Lorenzo Rosasco, Tomaso A. Poggio:
Holographic Embeddings of Knowledge Graphs. CoRR abs/1510.04935 (2015) - [i14]Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio:
How Important is Weight Symmetry in Backpropagation? CoRR abs/1510.05067 (2015) - [i13]Yan Luo, Xavier Boix, Gemma Roig, Tomaso A. Poggio, Qi Zhao:
Foveation-based Mechanisms Alleviate Adversarial Examples. CoRR abs/1511.06292 (2015) - 2014
- [c77]Chiyuan Zhang, Georgios Evangelopoulos, Stephen Voinea, Lorenzo Rosasco, Tomaso A. Poggio:
A deep representation for invariance and music classification. ICASSP 2014: 6984-6988 - [c76]Chiyuan Zhang, Stephen Voinea, Georgios Evangelopoulos, Lorenzo Rosasco, Tomaso A. Poggio:
Phone classification by a hierarchy of invariant representation layers. INTERSPEECH 2014: 2346-2350 - [c75]Stephen Voinea, Chiyuan Zhang, Georgios Evangelopoulos, Lorenzo Rosasco, Tomaso A. Poggio:
Word-level invariant representations from acoustic waveforms. INTERSPEECH 2014: 2385-2389 - [c74]Joel Z. Leibo, Qianli Liao, Tomaso A. Poggio:
Subtasks of Unconstrained Face Recognition. VISAPP (2) 2014: 113-121 - [r2]Tomaso A. Poggio, Shimon Ullman:
Machine Recognition of Objects. Computer Vision, A Reference Guide 2014: 469-472 - [r1]Tomaso A. Poggio, Shimon Ullman:
Visual Cortex Models for Object Recognition. Computer Vision, A Reference Guide 2014: 862-866 - [i12]Chiyuan Zhang, Georgios Evangelopoulos, Stephen Voinea, Lorenzo Rosasco, Tomaso A. Poggio:
A Deep Representation for Invariance And Music Classification. CoRR abs/1404.0400 (2014) - [i11]Tomaso A. Poggio, Jim Mutch, Leyla Isik:
Computational role of eccentricity dependent cortical magnification. CoRR abs/1406.1770 (2014) - [i10]Cheston Tan, Tomaso A. Poggio:
Neural tuning size is a key factor underlying holistic face processing. CoRR abs/1406.3793 (2014) - [i9]Georgios Evangelopoulos, Stephen Voinea, Chiyuan Zhang, Lorenzo Rosasco, Tomaso A. Poggio:
Learning An Invariant Speech Representation. CoRR abs/1406.3884 (2014) - [i8]Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio:
Unsupervised learning of clutter-resistant visual representations from natural videos. CoRR abs/1409.3879 (2014) - [i7]Pierre Baldi, Kenji Fukumizu, Tomaso A. Poggio:
Deep Learning: Theory, Algorithms, and Applications (NII Shonan Meeting 2014-5). NII Shonan Meet. Rep. 2014 (2014) - 2013
- [j59]Tomaso A. Poggio, Thomas Serre:
Models of visual cortex. Scholarpedia 8(4): 3516 (2013) - [c73]Silvia Villa, Lorenzo Rosasco, Tomaso A. Poggio:
On Learnability, Complexity and Stability. Empirical Inference 2013: 59-69 - [c72]Tomaso A. Poggio:
The Computational Magic of Pattern Recognition in Cortex: A Theory of Selectivity and Invariance. ICPRAM 2013: IS-7 - [c71]Cheston Tan, Jedediah M. Singer, Thomas Serre, David L. Sheinberg, Tomaso A. Poggio:
Neural representation of action sequences: how far can a simple snippet-matching model take us? NIPS 2013: 593-601 - [c70]Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio:
Learning invariant representations and applications to face verification. NIPS 2013: 3057-3065 - [p1]Cheston Tan, Joel Z. Leibo, Tomaso A. Poggio:
Throwing Down the Visual Intelligence Gauntlet. Machine Learning for Computer Vision 2013: 1-15 - [i6]Silvia Villa, Lorenzo Rosasco, Tomaso A. Poggio:
On Learnability, Complexity and Stability. CoRR abs/1303.5976 (2013) - [i5]Qianli Liao, Joel Z. Leibo, Youssef Mroueh, Tomaso A. Poggio:
Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines? CoRR abs/1311.4082 (2013) - [i4]Fabio Anselmi, Joel Z. Leibo, Lorenzo Rosasco, Jim Mutch, Andrea Tacchetti, Tomaso A. Poggio:
Unsupervised Learning of Invariant Representations in Hierarchical Architectures. CoRR abs/1311.4158 (2013) - 2012
- [j58]Leyla Isik, Joel Z. Leibo, Tomaso A. Poggio:
Learning and disrupting invariance in visual recognition with a temporal association rule. Frontiers Comput. Neurosci. 6: 37 (2012) - [c69]Guillermo D. Cañas, Tomaso A. Poggio, Lorenzo Rosasco:
Learning Manifolds with K-Means and K-Flats. NIPS 2012: 2474-2482 - [c68]Youssef Mroueh, Tomaso A. Poggio, Lorenzo Rosasco, Jean-Jacques E. Slotine:
Multiclass Learning with Simplex Coding. NIPS 2012: 2798-2806 - [i3]Guillermo D. Cañas, Tomaso A. Poggio, Lorenzo Rosasco:
Learning Manifolds with K-Means and K-Flats. CoRR abs/1209.1121 (2012) - [i2]Youssef Mroueh, Tomaso A. Poggio, Lorenzo Rosasco, Jean-Jacques E. Slotine:
Multiclass Learning with Simplex Coding. CoRR abs/1209.1360 (2012) - 2011
- [c67]Sharat Chikkerur, Thomas Serre, Cheston Tan, Tomaso A. Poggio:
Attention as a Bayesian inference process. Human Vision and Electronic Imaging 2011: 786511 - [c66]Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso A. Poggio, Thomas Serre:
HMDB: A large video database for human motion recognition. ICCV 2011: 2556-2563 - [c65]Joel Z. Leibo, Jim Mutch, Tomaso A. Poggio:
Why The Brain Separates Face Recognition From Object Recognition. NIPS 2011: 711-719 - [i1]Tomaso A. Poggio, Stephen Voinea, Lorenzo Rosasco:
Online Learning, Stability, and Stochastic Gradient Descent. CoRR abs/1105.4701 (2011) - 2010
- [j57]Thomas Serre, Tomaso A. Poggio:
A neuromorphic approach to computer vision. Commun. ACM 53(10): 54-61 (2010) - [j56]Steve Smale, Lorenzo Rosasco, Jake V. Bouvrie, Andrea Caponnetto, Tomaso A. Poggio:
Mathematics of the Neural Response. Found. Comput. Math. 10(1): 67-91 (2010) - [c64]Hueihan Jhuang, Estíbaliz Garrote, Nicholas Edelman, Tomaso A. Poggio, Andrew Steele, Thomas Serre:
Trainable, vision-based automated home cage behavioral phenotyping. MB 2010: 33:1-33:4 - [c63]Tomaso A. Poggio:
Hierarchical Learning Machines and Neuroscience of Visual Cortex. ECML/PKDD (1) 2010: 5 - [e3]Yiyu Yao, Ron Sun, Tomaso A. Poggio, Jiming Liu, Ning Zhong, Jimmy X. Huang:
Brain Informatics, International Conference, BI 2010, Toronto, ON, Canada, August 28-30, 2010. Proceedings. Lecture Notes in Computer Science 6334, Springer 2010, ISBN 978-3-642-15313-6 [contents]
2000 – 2009
- 2009
- [c62]Jake V. Bouvrie, Lorenzo Rosasco, Tomaso A. Poggio:
On Invariance in Hierarchical Models. NIPS 2009: 162-170 - 2008
- [j55]Minjoon Kouh, Tomaso A. Poggio:
A Canonical Neural Circuit for Cortical Nonlinear Operations. Neural Comput. 20(6): 1427-1451 (2008) - [c61]Jake V. Bouvrie, Tony Ezzat, Tomaso A. Poggio:
Localized spectro-temporal cepstral analysis of speech. ICASSP 2008: 4733-4736 - [c60]