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ORCIDTomaso A. Poggio
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- affiliation: Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory
- award (1992): Max Planck Research Award
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2020 – today
- 2025
[i63]Kushagra Tiwary, Aaron Young, Zaid Tasneem, Tzofi Klinghoffer, Akshat Dave, Tomaso A. Poggio, Dan Nilsson, Brian Cheung, Ramesh Raskar:
What if Eye...? Computationally Recreating Vision Evolution. CoRR abs/2501.15001 (2025)- 2024
- [c103]Yulu Gan, Tomer Galanti, Tomaso A. Poggio, Eran Malach:
On the Power of Decision Trees in Auto-Regressive Language Modeling. NeurIPS 2024 - [i62]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) - [i61]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) - [i60]Ziyin Liu, Isaac Chuang, Tomer Galanti, Tomaso A. Poggio:
Formation of Representations in Neural Networks. CoRR abs/2410.03006 (2024) - [i59]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) - [i58]Andrea Pinto, Akshay Rangamani, Tomaso A. Poggio:
On Generalization Bounds for Neural Networks with Low Rank Layers. CoRR abs/2411.13733 (2024) - [i57]Qianli Liao, Ziyin Liu, Yulu Gan, Brian Cheung, Mark Harnett, Tomaso A. Poggio:
Self-Assembly of a Biologically Plausible Learning Circuit. CoRR abs/2412.20018 (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]Tony Ezzat, Tomaso A. Poggio:
Discriminative word-spotting using ordered spectro-temporal patch features. SAPA@INTERSPEECH 2008: 35-40 - 2007
- [j54]Bernd Heisele, Thomas Serre, Tomaso A. Poggio:
A Component-based Framework for Face Detection and Identification. Int. J. Comput. Vis. 74(2): 167-181 (2007) - [j53]Thomas Serre, Lior Wolf, Stanley M. Bileschi, Maximilian Riesenhuber, Tomaso A. Poggio:
Robust Object Recognition with Cortex-Like Mechanisms. IEEE Trans. Pattern Anal. Mach. Intell. 29(3): 411-426 (2007) - [c59]Tony Ezzat, Jake V. Bouvrie, Tomaso A. Poggio:
AM-FM Demodulation of Spectrograms using Localized 2D Max-Gabor Analysis. ICASSP (4) 2007: 1061-1064 - [c58]Hueihan Jhuang, Thomas Serre, Lior Wolf, Tomaso A. Poggio:
A Biologically Inspired System for Action Recognition. ICCV 2007: 1-8 - [c57]Tony Ezzat, Jake V. Bouvrie, Tomaso A. Poggio:
Spectro-temporal analysis of speech using 2-d Gabor filters. INTERSPEECH 2007: 506-509 - 2006
- [j52]Sayan Mukherjee, Partha Niyogi, Tomaso A. Poggio, Ryan M. Rifkin:
Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization. Adv. Comput. Math. 25(1-3): 161-193 (2006) - [c56]Jerry Jun Yokono, Tomaso A. Poggio:
A Multiview Face Identification Model With No Geometric Constraints. FGR 2006: 493-498 - [c55]Tomaso A. Poggio:
Neuroscience: New Insights for AI? ICDM / Web Intelligence / IAT 2006: 3-8 - [c54]Tony Ezzat, Jake V. Bouvrie, Tomaso A. Poggio:
Max-Gabor analysis and synthesis of spectrograms. INTERSPEECH 2006 - [c53]Tomaso A. Poggio:
Neuroscience: New Insights for AI? WImBI 2006: 32-35 - [c52]Ulf Knoblich, Jake V. Bouvrie, Tomaso A. Poggio:
Biophysical Models of Neural Computation: Max and Tuning Circuits. WImBI 2006: 164-189 - 2005
- [j51]Lenore Blum, Felipe Cucker, Tomaso A. Poggio, James Renegar, Michael Shub:
Foreword. Found. Comput. Math. 5(4): 349 (2005) - [c51]Thomas Serre, Lior Wolf, Tomaso A. Poggio:
Object Recognition with Features Inspired by Visual Cortex. CVPR (2) 2005: 994-1000 - [c50]Rodrigo Sigala, Thomas Serre, Tomaso A. Poggio, Martin A. Giese:
Learning Features of Intermediate Complexity for the Recognition of Biological Motion. ICANN (1) 2005: 241-246 - [c49]Tony Ezzat, Ethan Meyers, James R. Glass, Tomaso A. Poggio:
Morphing spectral envelopes using audio flow. INTERSPEECH 2005: 2545-2548 - 2004
- [c48]Tony Ezzat, Gadi Geiger, Tomaso A. Poggio:
Trainable Videorealistic Speech Animation. FGR 2004: 57-66 - [c47]Jerry Jun Yokono, Tomaso A. Poggio:
Oriented Filters for Object Recognition: an Empirical Study. FGR 2004: 755-760 - 2003
- [j50]Volker Blanz, Curzio Basso, Tomaso A. Poggio, Thomas Vetter:
Reanimating Faces in Images and Video. Comput. Graph. Forum 22(3): 641-650 (2003) - [j49]Bernd Heisele, Purdy Ho, Jane Wu, Tomaso A. Poggio:
Face recognition: component-based versus global approaches. Comput. Vis. Image Underst. 91(1-2): 6-21 (2003) - [j48]Chikahito Nakajima, Massimiliano Pontil, Bernd Heisele, Tomaso A. Poggio:
Full-body person recognition system. Pattern Recognit. 36(9): 1997-2006 (2003) - [j47]Bernd Heisele, Thomas Serre, Sam Prentice, Tomaso A. Poggio:
Hierarchical classification and feature reduction for fast face detection with support vector machines. Pattern Recognit. 36(9): 2007-2017 (2003) - [j46]Ryan M. Rifkin, Sayan Mukherjee, Pablo Tamayo, Sridhar Ramaswamy, Chen-Hsiang Yeang, Michael Angelo, Michael Reich, Tomaso A. Poggio, Eric S. Lander, Todd R. Golub, Jill P. Mesirov:
An Analytical Method for Multiclass Molecular Cancer Classification. SIAM Rev. 45(4): 706-723 (2003) - [j45]Theodoros Evgeniou, Massimiliano Pontil, Constantine Papageorgiou, Tomaso A. Poggio:
Image Representations and Feature Selection for Multimedia Database Search. IEEE Trans. Knowl. Data Eng. 15(4): 911-920 (2003) - [c46]Tomaso A. Poggio:
Learning and Perceptual Interfaces. CVPR Workshops 2003: 45 - 2002
- [j44]Angela J. Yu, Martin A. Giese, Tomaso A. Poggio:
Biophysiologically Plausible Implementations of the Maximum Operation. Neural Comput. 14(12): 2857-2881 (2002) - [j43]Bernd Heisele, Alessandro Verri, Tomaso A. Poggio:
Learning and vision machines. Proc. IEEE 90(7): 1164-1177 (2002) - [j42]Tony Ezzat, Gadi Geiger, Tomaso A. Poggio:
Trainable videorealistic speech animation. ACM Trans. Graph. 21(3): 388-398 (2002) - [c45]Ulf Knoblich, Maximilian Riesenhuber, David J. Freedman, Earl K. Miller, Tomaso A. Poggio:
Visual Categorization: How the Monkey Brain Does It. Biologically Motivated Computer Vision 2002: 273-281 - [c44]Thomas Serre, Maximilian Riesenhuber, Jennifer Louie, Tomaso A. Poggio:
On the Role of Object-Specific Features for Real World Object Recognition in Biological Vision. Biologically Motivated Computer Vision 2002: 387-397 - [c43]Dirk Walther, Laurent Itti, Maximilian Riesenhuber, Tomaso A. Poggio, Christof Koch:
Attentional Selection for Object Recognition - A Gentle Way. Biologically Motivated Computer Vision 2002: 472-479 - [c42]Vinay P. Kumar, Tomaso A. Poggio:
Recognizing Expressions by Direct Estimation of the Parameters of a Pixel Morphable Model. Biologically Motivated Computer Vision 2002: 519-527 - [e2]Heinrich H. Bülthoff, Seong-Whan Lee, Tomaso A. Poggio, Christian Wallraven:
Biologically Motivated Computer Vision Second International Workshop, BMCV 2002, Tübingen, Germany, November 22-24, 2002, Proceedings. Lecture Notes in Computer Science 2525, Springer 2002, ISBN 3-540-00174-3 [contents] - 2001
- [j41]Anuj Mohan, Constantine Papageorgiou, Tomaso A. Poggio:
Example-Based Object Detection in Images by Components. IEEE Trans. Pattern Anal. Mach. Intell. 23(4): 349-361 (2001) - [c41]Bernd Heisele, Thomas Serre, Massimiliano Pontil, Tomaso A. Poggio:
Component-based Face Detection. CVPR (1) 2001: I:657-662 - [c40]Bernd Heisele, Thomas Serre, Sayan Mukherjee, Tomaso A. Poggio:
Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images. CVPR (2) 2001: II:18-24 - [c39]Bernd Heisele, Purdy Ho, Tomaso A. Poggio:
Face Recognition with Support Vector Machines: Global versus Component-based Approach. ICCV 2001: 688-694 - [c38]Bernd Heisele, Thomas Serre, Massimiliano Pontil, Thomas Vetter, Tomaso A. Poggio:
Categorization by Learning and Combining Object Parts. NIPS 2001: 1239-1245 - 2000
- [j40]Theodoros Evgeniou, Massimiliano Pontil, Tomaso A. Poggio:
Regularization Networks and Support Vector Machines. Adv. Comput. Math. 13(1): 1-50 (2000) - [j39]Tomaso A. Poggio, Alessandro Verri:
Introduction: Learning and Vision at CBCL. Int. J. Comput. Vis. 38(1): 5-7 (2000) - [j38]Theodoros Evgeniou, Massimiliano Pontil, Tomaso A. Poggio:
Statistical Learning Theory: A Primer. Int. J. Comput. Vis. 38(1): 9-13 (2000) - [j37]Constantine Papageorgiou, Tomaso A. Poggio:
A Trainable System for Object Detection. Int. J. Comput. Vis. 38(1): 15-33 (2000) - [j36]Tony Ezzat, Tomaso A. Poggio:
Visual Speech Synthesis by Morphing Visemes. Int. J. Comput. Vis. 38(1): 45-57 (2000) - [j35]Martin A. Giese, Tomaso A. Poggio:
Morphable Models for the Analysis and Synthesis of Complex Motion Patterns. Int. J. Comput. Vis. 38(1): 59-73 (2000) - [c37]Maximilian Riesenhuber, Tomaso A. Poggio:
CBF: A New Framework for Object Categorization in Cortex. Biologically Motivated Computer Vision 2000: 1-9 - [c36]Vinay P. Kumar, Tomaso A. Poggio:
Learning-Based Approach to Real Time Tracking and Analysis of Faces. FG 2000: 96-101 - [c35]Theodoros Evgeniou, Luis Pérez-Breva, Massimiliano Pontil, Tomaso A. Poggio:
Bounds on the Generalization Performance of Kernel Machine Ensembles. ICML 2000: 271-278 - [c34]Chikahito Nakajima, Norihiko Itoh, Massimiliano Pontil, Tomaso A. Poggio:
Object Recognition and Detection by a Combination of Support Vector Machine and Rotation Invariant Phase Only Correlation. ICPR 2000: 4787-4790 - [c33]Chikahito Nakajima, Massimiliano Pontil, Tomaso A. Poggio:
People Recognition and Pose Estimation in Image Sequences. IJCNN (4) 2000: 189-196 - [c32]Gert Cauwenberghs, Tomaso A. Poggio:
Incremental and Decremental Support Vector Machine Learning. NIPS 2000: 409-415 - [c31]Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso A. Poggio, Vladimir Vapnik:
Feature Selection for SVMs. NIPS 2000: 668-674 - [e1]Seong-Whan Lee, Heinrich H. Bülthoff, Tomaso A. Poggio:
Biologically Motivated Computer Vision, First IEEE International Workshop, BMVC 2000, Seoul, Korea, May 15-17, 2000, Proceedings. Lecture Notes in Computer Science 1811, Springer 2000, ISBN 3-540-67560-4 [contents]
1990 – 1999
- 1999
- [j34]Tomaso A. Poggio, Christian R. Shelton:
Machine Learning, Machine Vision, and the Brain. AI Mag. 20(3): 37-55 (1999) - [j33]Benny Rachlevsky-Reich, Israel Ben-Shaul, Nicholas Tung Chan, Andrew W. Lo, Tomaso A. Poggio:
GEM: A Global Electronic Market System. Inf. Syst. 24(6): 495-518 (1999) - [c30]Constantine Papageorgiou, Federico Girosi, Tomaso A. Poggio:
Sparse correlation kernel reconstruction. ICASSP 1999: 1633-1636 - [c29]Constantine Papageorgiou, Tomaso A. Poggio:
A Pattern Classification Approach to Dynamical Object Detection. ICCV 1999: 1223-1228 - [c28]Constantine Papageorgiou, Tomaso A. Poggio:
Trainable Pedestrian Detection. ICIP (4) 1999: 35-39 - 1998
- [j32]Michael J. Jones, Tomaso A. Poggio:
Multidimensional Morphable Models: A Framework for Representing and Matching Object Classes. Int. J. Comput. Vis. 29(2): 107-131 (1998) - [j31]Tomaso A. Poggio, Federico Girosi:
A Sparse Representation For Function Approximation. Neural Comput. 10(6): 1445-1454 (1998) - [j30]Kah Kay Sung, Tomaso A. Poggio:
Example-Based Learning for View-Based Human Face Detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1): 39-51 (1998) - [j29]Partha Niyogi, Federico Girosi, Tomaso A. Poggio:
Incorporating prior information in machine learning by creating virtual examples. Proc. IEEE 86(11): 2196-2209 (1998) - [j28]Rama Chellappa, Kunihiko Fukushima, Aggelos K. Katsaggelos, Sun-Yuan Kung, Yann LeCun, Nasser M. Nasrabadi, Tomaso A. Poggio:
Guest Editorial Applications Of Artificial Neural Networks To Image Processing. IEEE Trans. Image Process. 7(8): 1093-1096 (1998) - [c27]Tony Ezzat, Tomaso A. Poggio:
MikeTalk: A Talking Facial Display Based on Morphing Visemes. CA 1998: 96-102 - [c26]Michael J. Jones, Tomaso A. Poggio:
Hierarchical Morphable Models. CVPR 1998: 820-826 - [c25]Constantine Papageorgiou, Michael Oren, Tomaso A. Poggio:
A General Framework for Object Detection. ICCV 1998: 555-562 - [c24]Michael J. Jones, Tomaso A. Poggio:
Multidimensional Morphable Models. ICCV 1998: 683-688 - 1997
- [j27]Thomas Vetter, Tomaso A. Poggio:
Linear Object Classes and Image Synthesis From a Single Example Image. IEEE Trans. Pattern Anal. Mach. Intell. 19(7): 733-742 (1997) - [j26]Roberto Brunelli, Tomaso A. Poggio:
Template matching: matched spatial filters and beyond. Pattern Recognit. 30(5): 751-768 (1997) - [j25]Anthony G. Constantinides, Simon Haykin, Yu Hen Hu, Jenq-Neng Hwang, Shigeru Katagiri, Sun-Yuan Kung, Tomaso A. Poggio:
Guest Editors' Introduction: Neural Networks For Signal Processing. IEEE Trans. Signal Process. 45(11): 2637-2638 (1997) - [j24]Bernhard Schölkopf, Kah Kay Sung, Christopher J. C. Burges, Federico Girosi, Partha Niyogi, Tomaso A. Poggio, Vladimir Vapnik:
Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans. Signal Process. 45(11): 2758-2765 (1997) - [c23]Tomaso A. Poggio:
Image Representations for Visual Learning (Invited Paper). AVBPA 1997: 143 - [c22]Tony Ezzat, Tomaso A. Poggio:
Videorealistic talking faces: a morphing approach. AVSP 1997: 141-144 - [c21]Thomas Vetter, Michael J. Jones, Tomaso A. Poggio:
A bootstrapping algorithm for learning linear models of object classes. CVPR 1997: 40-46 - [c20]Michael Oren, Constantine Papageorgiou, Pawan Sinha, Edgar Osuna, Tomaso A. Poggio:
Pedestrian Detection Using Wavelet Templates. CVPR 1997: 193-199 - [c19]Maximilian Riesenhuber, Tomaso A. Poggio:
Just One View: Invariances in Inferotemporal Cell Tuning. NIPS 1997: 215-221 - [c18]Shai Avidan, Theodoros Evgeniou, Amnon Shashua, Tomaso A. Poggio:
Image-based view synthesis by combining trilinear tensors and learning techniques. VRST 1997: 103-110 - 1996
- [c17]Thomas Vetter, Tomaso A. Poggio:
Image Synthesis from a Single Example Image. ECCV (1) 1996: 652-659 - [c16]Tony Ezzat, Tomaso A. Poggio:
Facial Analysis and Synthesis Using Image-Based Models. FG 1996: 116-121 - [c15]Emanuela Bricolo, Tomaso A. Poggio, Nikos K. Logothetis:
3D Object Recognition: A Model of View-Tuned Neurons. NIPS 1996: 41-47 - 1995
- [j23]Nicola Ancona, Tomaso A. Poggio:
Optical flow from 1-D correlation: Application to a simple time-to-crash detector. Int. J. Comput. Vis. 14(2): 131-146 (1995) - [j22]Roberto Brunelli, Daniele Falavigna, Tomaso A. Poggio, Luigi Stringa:
Automatic person recognition by acoustic and geometric features. Mach. Vis. Appl. 8(5): 317-325 (1995) - [j21]Federico Girosi, Michael J. Jones, Tomaso A. Poggio:
Regularization Theory and Neural Networks Architectures. Neural Comput. 7(2): 219-269 (1995) - [c14]Tomaso A. Poggio, Kah Kay Sung:
Finding Human Faces with a Gaussian Mixture Distribution-Based Face Model. ACCV 1995: 437-446 - [c13]Kah Kay Sung, Tomaso A. Poggio:
Learning Human Face Detection in Cluttered Scenes. CAIP 1995: 432-439 - [c12]David Beymer, Tomaso A. Poggio:
Face Recognition from One Example View. ICCV 1995: 500-507 - [c11]Michael J. Jones, Tomaso A. Poggio:
Model-Based Matching of Line Drawings by Linear Combinations of Prototypes. ICCV 1995: 531-536 - 1994
- [j20]Bir Bhanu, Tomaso A. Poggio:
Introduction to the Special Section on Learning in Computer Vision. IEEE Trans. Pattern Anal. Mach. Intell. 16(9): 865 (1994) - 1993
- [j19]Tomaso A. Poggio:
Werner Reichardt 1924-1992. Biol. Cybern. 69(1): 1-3 (1993) - [j18]Roberto Brunelli, Tomaso A. Poggio:
Caricatural effects in automated face perception. Biol. Cybern. 69(3): 235-241 (1993) - [j17]Roberto Brunelli, Tomaso A. Poggio:
Face Recognition: Features Versus Templates. IEEE Trans. Pattern Anal. Mach. Intell. 15(10): 1042-1052 (1993) - [j16]Benjamin W. Wah, Thomas S. Huang, Aravind K. Joshi, Dan I. Moldovan, Yiannis Aloimonos, Ruzena Bajcsy, Dana H. Ballard, Doug DeGroot, Kenneth A. De Jong, Charles R. Dyer, Scott E. Fahlman, Ralph Grishman, Lynette Hirschman, Richard E. Korf, Stephen E. Levinson, Daniel P. Miranker, N. H. Morgan, Sergei Nirenburg, Tomaso A. Poggio, Edward M. Riseman, Craig Stanfil, Salvatore J. Stolfo, Steven L. Tanimoto, Charles C. Weems:
Report on Workshop on High Performance Computing and Communications for Grand Challenge Applications: Computer Vision, Speech and Natural Language Processing, and Artificial Intelligence. IEEE Trans. Knowl. Data Eng. 5(1): 138-154 (1993) - [c10]Nicola Ancona, Tomaso A. Poggio:
Optical flow from 1D correlation: Application to a simple time-to-crash detector. ICCV 1993: 209-214 - 1992
- [j15]Tomaso A. Poggio, Shimon Edelman, Manfred Fahle:
Learning of visual modules from examples: A framework for understanding adaptive visual performance. CVGIP Image Underst. 56(1): 22-30 (1992) - [j14]John L. Wyatt Jr., Craig L. Keast, Mark Seidel, David L. Standley, Berthold K. P. Horn, Tom Knight, Charles G. Sodini, Hae-Seung Lee, Tomaso A. Poggio:
Analog VLSI systems for image acquisition and fast early vision processing. Int. J. Comput. Vis. 8(3): 217-230 (1992) - [j13]Shimon Edelman, Tomaso A. Poggio:
Bringing the Grandmother back into the Picture: A Memory-Based View of Object Recognition. Int. J. Pattern Recognit. Artif. Intell. 6(1): 37-61 (1992) - [c9]Roberto Brunelli, Tomaso A. Poggio:
Face Recognition through Geometrical Features. ECCV 1992: 792-800 - 1991
- [j12]Charles C. Weems, Christopher M. Brown, Jon A. Webb, Tomaso A. Poggio, John R. Kender:
Parallel Processing in the DARPA Strategic Computing Vision Program. IEEE Expert 6(5): 23-38 (1991) - [c8]Roberto Brunelli, Tomaso A. Poggio:
HyperBF Networks for Real Object Recognition. IJCAI 1991: 1278-1285 - 1990
- [j11]Tomaso A. Poggio, Federico Girosi:
Networks for approximation and learning. Proc. IEEE 78(9): 1481-1497 (1990) - [c7]Federico Girosi, Tomaso A. Poggio, Bruno Caprile:
Extensions of a Theory of Networks for Approximation and Learning. NIPS 1990: 750-756
1980 – 1989
- 1989
- [j10]Federico Girosi, Tomaso A. Poggio:
Representation Properties of Networks: Kolmogorov's Theorem Is Irrelevant. Neural Comput. 1(4): 465-469 (1989) - [j9]Alessandro Verri, Tomaso A. Poggio:
Motion Field and Optical Flow: Qualitative Properties. IEEE Trans. Pattern Anal. Mach. Intell. 11(5): 490-498 (1989) - [j8]Edward B. Gamble, Davi Geiger, Tomaso A. Poggio, Daphna Weinshall:
Integration of vision modules and labeling of surface discontinuities. IEEE Trans. Syst. Man Cybern. 19(6): 1576-1581 (1989) - 1988
- [j7]Tomaso A. Poggio, Harry Voorhees, Alan L. Yuille:
A regularized solution to edge detection. J. Complex. 4(2): 106-123 (1988) - [j6]B. Moore, Tomaso A. Poggio:
Representation properties of multilayer feedforward networks. Neural Networks 1(Supplement-1): 203 (1988) - [j5]Tomaso A. Poggio:
Learning, regularization and splines. Neural Networks 1(Supplement-1): 211-212 (1988) - [j4]Mario Bertero, Tomaso A. Poggio, Vincent Torre:
Ill-posed problems in early vision. Proc. IEEE 76(8): 869-889 (1988) - [c6]James J. Little, Heinrich H. Bülthoff, Tomaso A. Poggio:
Parallel Optical Flow Using Local Voting. ICCV 1988: 454-459 - [c5]Anya C. Hurlbert, Tomaso A. Poggio:
A Network for Image Segmentation Using Color. NIPS 1988: 297-304 - 1987
- [c4]Davi Geiger, Tomaso A. Poggio:
An Optimal Scale for Edge Detection. IJCAI 1987: 745-748 - [c3]Tomaso A. Poggio, Anya C. Hurlbert:
Learning a Color Algorithm from Examples. NIPS 1987: 622-631 - 1986
- [j3]Alan L. Yuille, Tomaso A. Poggio:
Scaling Theorems for Zero Crossings. IEEE Trans. Pattern Anal. Mach. Intell. 8(1): 15-25 (1986) - [j2]Vincent Torre, Tomaso A. Poggio:
On Edge Detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(2): 147-163 (1986) - [c2]Michael Drumheller, Tomaso A. Poggio:
On parallel stereo. ICRA 1986: 1439-1448 - 1985
- [j1]Tomaso A. Poggio:
Early vision: From computational structure to algorithms and parallel hardware. Comput. Vis. Graph. Image Process. 31(2): 139-155 (1985) - 1984
- [c1]Alan L. Yuille, Tomaso A. Poggio:
Fingerprints Theorems. AAAI 1984: 362-365
Coauthor Index
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