Poggio, Tomaso - Center for Biological and Computational Learning, Massachusetts Institute of Technology (MIT)

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Poggio, Tomaso - Center for Biological and Computational Learning, Massachusetts Institute of Technology (MIT)

An Order Flow Model and a Liquidity Measure of Financial Markets
Computer Science and Artificial Intelligence Laboratory Technical Report
Stability of K-Means Clustering Alexander Rakhlin
Trainable Videorealistic Speech Animation Tony Ezzat Gadi Geiger Tomaso Poggio
Feature Selection for SVMs J. Weston y , S. Mukherjee yy , O. Chapelle , M. Pontil yy
PRIOR KNOWLEDGE AND THE CREATION OF ``VIRTUAL'' EXAMPLES
To Appear in Recent Progress in Computer Vision, SpringerVerlag LNCS Series, 1995 Finding Human Faces with a Gaussian Mixture
AgentBased Models of Financial Markets: A Comparison with Experimental Markets \Lambda
Responses of single neurons in the human brain during flash suppression
Max-Gabor Analysis and Synthesis of Spectrograms Tony Ezzat, Jake Bouvrie, Tomaso Poggio
Appears in Proceedings of ICASSP, Phoenix, March 1999 SPARSE CORRELATION KERNEL RECONSTRUCTION
`United' we stand A little before Bill Clinton and Al Gore's re-election for a second term in office we had
letters to nature 436 NATURE |VOL 415 |24 JANUARY 2002 |www.nature.com
Component-based Face Recognition with 3D Morphable Models B. Weyrauch
Oriented Filters for Object Recognition: an empirical study Jerry Jun Yokono Tomaso Poggio
On the Noise Model of Support Vector Machines Massimiliano Pontil, Sayan Mukherjee, and Federico Girosi
Bounds on sample size for policy evaluation in Markov environments
A Sparse Representation for Function Approximation 1 Tomaso Poggio and Federico Girosi
To appear in Advances in Image Understanding: A Festschrift for Azriel Rosenfeld. IEEE Computer Society Press, June 1996
e-publishing Face processing in
MIT Progress Report Summary -2004 1. Specific Aims
MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
T. Poggio, S. Mukherjee, R. Rifkin, A. Rakhlin, and A. Verri Center for Biological and Computational Learning
Components for Face Recognition Bernd Heisele
Incremental and Decremental Support Vector Machine Learning
STORY TEXT Annan Sees All Sides Ready to Resume
Computer Science and Artificial Intelligence Laboratory Technical Report
Error Weighted Classifier Combination for Multi-modal
Conte Center 2006 Report Summary In the enclosed progress reports we describe the progress so far 36 months after the beginning of the
Categorization by Learning and Combining Object Parts
wavelets in 2D vertical horizontal diagonal
Face Recognition with Support Vector Machines: Global versus Component-based Approach
Gene expression changes and molecular pathways mediating activity-dependent plasticity in visual cortex
A Contour-Based Moving Object Detection and Masayuki Yokoyama
R E V I E W S NATURE REVIEWS | NEUROSCIENCE VOLUME 4 | MARCH 2003 | 179
Object Recognition with Partially Labeled Examples Andrew S. Crane
Maximum Entropy Modeling of Short Sequence Motifs with Applications to RNA Splicing Signals
MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
Appears in Proceedings of International Conference on Computer Vision, Corfu, Greece, September 1999 A Pattern Classification Approach to Dynamical Object Detection
A Bootstrapping Algorithm for Learning Linear Models of Object Classes
ModelBased Matching by Linear Combinations of Prototypes Michael J. Jones and Tomaso Poggio
Computer Science and Artificial Intelligence Laboratory Technical Report
Intracellular Measurements of Spatial Integration and the MAX Operation in Complex Cells of the Cat Primary Visual Cortex
Asymptotics of Gaussian Regularized Least-Squares
On the Relationship Between Generalization Error, Hypothesis Complexity, and Sample Complexity for Radial Basis Functions 1
Trainable Pedestrian Detection Constantine Papageorgiou Tomaso Poggio
Extra-label information: experiments with view-based classification
Training Support Vector Machines: an Application to Face (To appear in the Proceedings of CVPR'97, June 1719, 1997, Puerto Rico.)
This is page 107 Printer: Opaque this
MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
International Journal of Computer Vision, ??, 1--28 (1998) fl 1998 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
Multi-Source Human Identification Brian A. Kim
Wider recognition in peripheral vision common to different subtypes of dyslexia
Quantification and classification of locomotion patterns by spatiotemporal morphable models
Experiments in Expression Recognition James P. Skelley
apse formation and reveal a striking effect of leptin on the synaptic inputs to two key pep-
MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
Neural model for the recognition of biological Center for Biological and Computational Learning
Journal of Machine Learning Research 7 (2006) 2565-2583 Submitted 8/05; Revised 9/06; Published 12/06 Stability Properties of Empirical Risk Minimization over Donsker
Artificial Markets and Intelligent Agents S.B. Massachusetts Institute of Technology (1995)
Direction Estimation of Pedestrian from Multiple Still Images Hiroaki Shimizu
Advances in Component-based Face Detection Stanley M. Bileschi1
Categorization Training Results in Shape-and Category-Selective Human Neural Plasticity
REGULARIZED ALGORITHMS FOR RANKING, AND MANIFOLD LEARNING FOR RELATED TASKS
An Equivalence Between Sparse Approximation and Support Vector Machines 1
Synthesis and recognition of biological motion patterns based on linear superposition of
Handheld Face Identification Technology in a Pervasive Computing Environment
Analysis and Applications, Vol. 3, No. 4 (2005) 397417 c World Scientific Publishing Company
Statistical Learning : stability is sufficient for generalization and necessary and sufficient for consistency of Empirical Risk
Object recognition in cortex: Neural mechanisms, and possible roles for attention
Object Recognition and Detection by a Combination of Support Vector Machine and Rotation Invariant Phase Only Correlation
People Recognition and Pose Estimation in Image Sequences Chikahito Nakajima
Properties of Support Vector Machines Massimiliano Pontil and Alessandro Verri
MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
Partha Niyogi was born July 31, 1967, in Calcutta (Kolkata), India. As a child he lived in Bombay (Mumbai), where he
Using Component Features for Face Recognition Yuri Ivanov Bernd Heisele Thomas Serre
Identification, Improved Modeling and Integration of Signals to Predict Constitutive and Alternative Splicing
Robust Boosting for Learning from Few Examples Lior Wolf & Ian Martin
arXiv:1105.4701v2[cs.LG]25May2011 Online Learning, Stability, and Stochastic Gradient Descent
LETTER Communicated by Bartlett Mel A Canonical Neural Circuit for Cortical Nonlinear Operations
ModelBased Matching by Linear Combinations of Prototypes Michael J. Jones
LearningBased Approach to Real Time Tracking and Analysis of Faces Vinay P. Kumar and Tomaso Poggio
MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
Face recognition: component-based versus global approaches
Project Title: Detection and Recognition of Objects in Visual Cortex In response to program announcement for Silvio O. Conte Feasibility Centers for
Component-based Car Detection in Street Scene Images
Advances in Component Based Face Detection S. M. Bileschi B. Heisele
A Bayesian Approach to Transcript Estimation from Gene Array Data: The BEAM Technique
38 1094-7167/03/$17.00 2003 IEEE IEEE INTELLIGENT SYSTEMS Published by the IEEE Computer Society
Visual Search and Dual Tasks Reveal Two Distinct Attentional Resources
Using neural population decoding to understand high level visual processing
Regularization Theory and Neural Networks Architectures 1 Federico Girosi, Michael Jones and Tomaso Poggio
APPROXIMATION ERROR BOUNDS THAT USE VCBOUNDS 1
International Journal of Computer Vision 38(1), 1533, 2000 c 2000 Kluwer Academic Publishers. Manufactured in The Netherlands.
Face Recognition with Support Vector Machines and 3D Head Models
Journal of Machine Learning Research 6 (2005) 18551887 Submitted 9/03; Revised 4/05; Published 11/05 Feature Selection for Unsupervised and Supervised Inference
Local Field Potentials and Spikes in the Human Medial Temporal Lobe are Selective to
ESAIM: PS ESAIM: Probability and Statistics June 2005, Vol. 9, p. 220229
Computer Science and Artificial Intelligence Laboratory Technical Report
Hierarchical Morphable Models Michael J. Jones Tomaso Poggio
Example-Based Object Detection in Images by Components
BIOINFORMATICS DISCOVERY NOTE Vol. 1 no. 1 2001
A NOTE ON THE ROLE OF SQUARED LOSS IN REGRESSION ANDREA CAPONNETTO
Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines
ModelBased Matching of Line Drawings by Linear Combinations of Prototypes
Multidimensional Morphable Models Michael J. Jones Tomaso Poggio
Computer Science and Artificial Intelligence Laboratory Technical Report
What and Where: A Bayesian Inference Theory of Visual Sharat Chikkerur
Choosing Multiple Parameters for Support Vector Machines
Neural field model for the recognition of biological motion patterns
MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
Boosting a Biologically Inspired Local Descriptor for
International Journal of Computer Vision 38(1), 57, 2000 c 2000 Kluwer Academic Publishers. Manufactured in The Netherlands.
A Biological Model of Object Recognition with Feature Learning
Learning and Vision Machines BERND HEISELE, ALESSANDRO VERRI, AND TOMASO POGGIO
Biophysical Models of Neural Computation: Max and Tuning Circuits
Evidence for multi-functional interactions in early visual
NIH-Conte Center Report Summary -2005 In the enclosed progress reports we describe the progress so far 20 months after the beginning of the
Computer Science and Artificial Intelligence Laboratory Technical Report
Computer Science and Artificial Intelligence Laboratory Technical Report
Computer Science and Artificial Intelligence Laboratory Technical Report
Computer Science and Artificial Intelligence Laboratory Technical Report
International Journal of Computer Vision c 2006 Springer Science + Business Media, LLC. Manufactured in the United States.
A Biologically Inspired System for Action Recognition H. Jhuang T. Serre L. Wolf
StreetScenes: Towards Scene Understanding in Still Images
Computer Science and Artificial Intelligence Laboratory Technical Report
Applications of Empirical Processes in Learning Theory: Algorithmic Stability and Generalization
Learning a Dictionary of Shape-Components in Visual Cortex: Comparison with Neurons, Humans and Machines
Modeling Shape Representation in Visual Cortex Charles Fredrick Cadieu
A Combinatorial Code for Splicing Silencing: UAGG and GGGG Motifs
Robust Learning and Segmentation for Scene Understanding
Object Recognition with Features Inspired by Visual Cortex Thomas Serre Lior Wolf Tomaso Poggio
W. Duch et al. (Eds.): ICANN 2005, LNCS 3696, pp. 241 246, 2005. Springer-Verlag Berlin Heidelberg 2005
mit computer science and artificial intelligence laboratory Comparing Visual Features for
Identification and analysis of alternative splicing events conserved in human and mouse
ARTICLE IN PRESS UNCORRECTEDPROOF
Stereovision based vehicle classification using support vector machines
Cell, Vol. 119, 831845, December 17, 2004, Copyright 2004 by Cell Press Systematic Identification and
Advances in Component-based Face Detection Stanley Michael Bileschi
Estimating Dataset Size Requirements for Classifying DNA Microarray Data
Pattern Recognition 36 (2003) 19972006 www.elsevier.com/locate/patcog
Notices of the American Mathematical Society (AMS), Vol. 50, No. 5, 537-544, 2003.
SIAM REVIEW c 2003 Society for Industrial and Applied Mathematics Vol. 45, No. 4, pp. 706723
Regularized Least-Squares Classification
Visual Categorization: How the Monkey Brain Does It
Single-unit recordings from behaving monkeys and human functional magnetic resonance imaging studies have continued
On the Role of Object-specific Features for Real World Object Recognition in Biological Vision
Learning from Partially Labeled Data Marcin Olof Szummer
Component-based Face Detection Bernd Heisele
Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images
Application of Statistical Learning Theory to DNA Microarray Analysis
Balancing Multiple Sources of Reward in Reinforcement Learning
Learning with kernel machine architectures Theodoros Evgeniou
International Journal of Computer Vision 38(1), 913, 2000 c 2000 Kluwer Academic Publishers. Manufactured in The Netherlands.
Advances in Computational Mathematics x (1999) x-x 1 Regularization Networks and Support Vector Machines
How a Part of the Brain Might or Might Not Work: A New Hierarchical Model of Object Recognition
International Journal of Computer Vision 38(1), 7591, 2000 c 2000 Kluwer Academic Publishers. Manufactured in The Netherlands.
MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
www.ghedini.it/medea Developmental dyslexia,
Towards the Prevention of Gadi Geiger & Domenic G. Amara
Hierarchical Learning: Theory with Applications in Speech and Vision
Toward a More Biologically Plausible Model of Object Recognition
The Informational Complexity of Learning from Partha Niyogi
Neuron 50, 159172, April 6, 2006 2006 Elsevier Inc. DOI 10.1016/j.neuron.2006.03.012 Evaluation of a Shape-Based Model
Innovative Methodology Using Neuronal Latency to Determine SensoryMotor Processing Pathways in
NIH-Conte Center Report Summary -2004 In the enclosed progress reports we describe the progress so far after the first 8 months after starting
MIT 2006 Progress Report Summary 1. Specific Aims
MIT Progress Report Summary -2005 1. Specific Aims
Melting the Iceberg: Contrast Invariance in Visual Cortex
Brief Communications Sparse Representation in the Human Medial Temporal Lobe
Computer Science and Artificial Intelligence Laboratory Technical Report
Finding Human Faces with a Gaussian Mixture Distributionbased Face Model
A feedforward theory of visual cortex accounts for human performance in rapid categorization
Trainable Videorealistic Speech Animation Tony Ezzat Gadi Geiger
Multi-Source Contingency Clustering Jacob V. Bouvrie
The duration of the attentional blink in natural scenes depends on stimulus category
Tomaso Poggio Tomaso Poggio is Eugene
Support Vector Machines for 3D Object Recognition
TOMASO A. POGGIO Department of Brain & Cognitive Sciences
Component-based Face Recognition with 3D Morphable Models
Automatic Facial Expression Analysis and Emotional Classification
On Stability and Concentration of Measure Alexander Rakhlin, Sayan Mukherjee and Tomaso Poggio
Name: Tomaso A. Poggio Professional Title and Affiliation: The Eugene McDermott Professor
-3 -2 -1 0 1 2 3 0.5 1 1.5 2 2.5 3 3.5
Sequence Motifs Predictive of Tissue-Specific B.S. Engineering and Applied Science
Pattern Recognition 36 (2003) 20072017 www.elsevier.com/locate/patcog
Computer Science and Artificial Intelligence Laboratory Technical Report
A Computational Theory of Human Stereo Vision D. Marr; T. Poggio
Computer Science and Artificial Intelligence Laboratory Technical Report
BioI. Cybernetics 19,201-209 (1975) @ by Springer-Verlag 1975
BioI. Cybernetics 18, 69-80 (1975) @ by Springer-Verlag 1975
Ill-Posed Problems in Early Vision: From Computational Theory to Analogue T. Poggio; C. Koch
Kybernetik 12, 185-203 (1973) @ by Springer-Verlag 1973
A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks
View-dependent object recognition by monkeys N.K. Logothetis*, J.Pauls*, H.H. Bilthofft and T.Poggio*
Visual Hyperacuity: Spatiotemporal Interpolation in Human Vision M. Fahle; T. Poggio
How can cells in the anterior medial face patch be viewpoint invariant? Joel Z Leibo, Jim Mutch, Tomaso Poggio
Fast Perceptual Learning in Visual Hyperacuity Tomaso Poggio; Manfred Fahle; Shimon Edelman
Computer Science and Artificial Intelligence Laboratory Technical Report
Biol.Cybern.45,123-130(1982) Biological Cybernetics
Synthesizing a Color Algorithm from Examples Anya C. Hurlbert; Tomaso A. Poggio
BioI. Cybernetics 19,39-54 (1975) @ by Springer-Verlag (1975)
Kybernetik 13, 113-122(1973) @ by Springer-Verlag 1973
The Mller-Lyer Figure and the Fly Gad Geiger; Tomaso Poggio
A Synaptic Mechanism Possibly Underlying Directional Selectivity to Motion V. Torre; T. Poggio
A Theoretical Analysis of Electrical Properties of Spines C. Koch; T. Poggio
Probabilistic Solution of Ill-Posed Problems in Computational Vision J. Marroquin; S. Mitter; T. Poggio
Retinal Ganglion Cells: A Functional Interpretation of Dendritic Morphology C. Koch; T. Poggio; V. Torres
Computer Science and Artificial Intelligence Laboratory Technical Report
Regularization Algorithms for Learning that are Equivalent to Multilayer T. Poggio; F. Girosi
?~ . .~(:$'-,:,:;,. ,~" ~'"" '0# rirst European
Kybcrnel il; 13, 223-227 (1973) (0 by Springer-Vl'rlag 1973
Kybernetik 12,237-238 (1973) @ by Springer-Verlag 1973
Parallel Integration of Vision Modules T. Poggio; E. B. Gamble; J. J. Little
Caricatural E ects in Automated Face Perception R. Brunelli
Kybemetik 10, 58-60 (1972) @ by Springer-Verlag 1972
Computer Science and Artificial Intelligence Laboratory Technical Report
arXiv:1105.4701v3[cs.LG]8Sep2011 Online Learning, Stability, and Stochastic Gradient Descent
THE COMPUTATIONAL MAGIC OF THE VENTRAL STREAM Online archived report: usage instructions. This is version 2 (September 16, 2011) of a report pub-
Why The Brain Separates Face Recognition From Object Recognition
Computer Science and Artificial Intelligence Laboratory Technical Report