Poggio, Tomaso
Tomaso Poggio fisico italiano
Poggio, Tomaso 1947-
Poggio, Tomaso, 19..-....
![Sudoc [ABES], France](/viaf/images/flags/SUDOC.png "Sudoc [ABES], France")
Tomaso Poggio physicien italien
VIAF ID: 13031602 (Personal)
Permalink: http://viaf.org/viaf/13031602
Preferred Forms
-
200 _ 1
‡a
Poggio
‡b
, Tomaso
-
200 _ |
‡a
Poggio
‡b
Tomaso
-
100 1 _ ‡a Poggio, Tomaso
-
100 1 _ ‡a Poggio, Tomaso
-
100 1 _ ‡a Poggio, Tomaso (sparse)
-
100 1 _ ‡a Poggio, Tomaso
-
100 1 _ ‡a Poggio, Tomaso
-
100 1 _ ‡a Poggio, Tomaso
-
100 1 _ ‡a Poggio, Tomaso (sparse)
-
100 1 _
‡a
Poggio, Tomaso
-
100 1 _ ‡a Poggio, Tomaso
-
100 1 _
‡a
Poggio, Tomaso
-
100 1 _ ‡a Poggio, Tomaso ‡d (1947- )
-
100 1 _ ‡a Poggio, Tomaso ‡d (1947- ).
-
100 1 _
‡a
Poggio, Tomaso
‡d
1947-
-
100 1 _
‡a
Poggio, Tomaso,
‡d
19..-....
-
100 0 _
‡a
Tomaso Poggio
‡c
fisico italiano
-
100 0 _
‡a
Tomaso Poggio
‡c
physicien italien
4xx's: Alternate Name Forms (30)
5xx's: Related Names (1)
Works
| Title | Sources |
|---|---|
| Biologically motivated computer vision : First IEEE International Workshop BMCV 2000, Seoul, Korea, May 2000 : proceedings |
|
| The biophysical properties of spines as a basis for their electrical function: a comment on Kawato & Tsukahara |
|
| Cervello: manuale dell'utente : guida semplificata alla macchina più complessa del mondo |
|
| Computational neuroscience |
|
| Dynamic faces : insights from experiments and computation |
|
| Early visual learning |
|
| Exploring brain functions : models in neuroscience : report of the Dahlem Workshop on Exploring Brain Functions: Models in Neuroscience, Berlin, 1991 September 29-October 4 |
|
| A fast, invariant representation for human action in the visual system |
|
| Fast perceptual learning in hyperacuity. |
|
| Feature Selection for SVMs |
|
| A feedforward architecture accounts for rapid categorization |
|
| Figure-ground discrimination by relative movement in the visual system of the fly. Part II: Towards the neural circuitry 1 |
|
| Fingerprints theorems for zero-crossings, c1983 |
|
| Generalization in vision and motor control |
|
| Holographic Embeddings of Knowledge Graphs |
|
| Identification and analysis of alternative splicing events conserved in human and mouse |
|
| The importance of symmetry and virtual views in three-dimensional object recognition |
|
| Intracellular measurements of spatial integration and the MAX operation in complex cells of the cat primary visual cortex |
|
| The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex |
|
| Just One View: Invariances in Inferotemporal Cell Tuning |
|
| Learning a Color Algorithm from Examples |
|
| Learning in brains and machines |
|
| Learning Manifolds with K-Means and K-Flats |
|
| Learning with a Wasserstein Loss |
|
| The Levels of Understanding framework, revised |
|
| L'occhio e il cervello / Tomaso Poggio |
|
| Marr's approach to vision |
|
| Mózg : podręcznik użytkownika |
|
| The Müller-Lyer figure and the fly |
|
| A Network for Image Segmentation Using Color |
|
| Neural mechanisms for the recognition of biological movements |
|
| Neural representation of action sequences: how far can a simple snippet-matching model take us? |
|
| Neural Tuning Size in a Model of Primate Visual Processing Accounts for Three Key Markers of Holistic Face Processing |
|
| Nonlinear interactions in a dendritic tree: localization, timing, and role in information processing |
|
| On holographic models of memory |
|
| On Invariance in Hierarchical Models |
|
| On optimal nonlinear associative recall |
|
| On the respresentation of multi-input systems: computational properties of polynomial algorithms. |
|
| Parallel integration of vision modules. |
|
| Predicting the visual world: silence is golden |
|
| Real-time delayed tracking in flies |
|
| Regularization algorithms for learning that are equivalent to multilayer networks. |
|
| Regularization Theory and Neural Networks Architectures |
|
| Retinal ganglion cells: a functional interpretation of dendritic morphology. |
|
| Robust object recognition with cortex-like mechanisms |
|
| Role of learning in three-dimensional form perception |
|
| Scale and translation-invariance for novel objects in human vision |
|
| Shape representation in the inferior temporal cortex of monkeys |
|
| A simple algorithm for solving the cable equation in dendritic trees of arbitrary geometry. |
|
| Smallest channel in early human vision |
|
| Symmetric 3D objects are an easy case for 2D object recognition. |
|
| Synapses that compute motion |
|
| Theoretical approaches in neurobiology, c1980 (a.e.) |
|
| Theoretical issues in deep networks |
|
| A theory of how the brain might work. |
|
| A theory of the pattern induced flight orientation of the fly Musca domestica II |
|
| Top-down learning of low-level vision tasks. |
|
| Tracking and chasing in houseflies (Musca) |
|
| Trade-off between object selectivity and tolerance in monkey inferotemporal cortex. |
|
| Vertical image registration in stereopsis |
|
| View-based models of 3D object recognition: invariance to imaging transformations. |
|
| View-dependent object recognition by monkeys. |
|
| View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation |
|
| Vision : a computational investigation into the human representation and processing of visual information |
|
| Vision: are models of object recognition catching up with the brain? |
|
| Vision by man and machine. |
|
| Visual categorization and the primate prefrontal cortex: neurophysiology and behavior |
|
| Visual control of orientation behaviour in the fly. Part II. Towards the underlying neural interactions |
|
| Visual cortex and deep networks, 2016: |
|
| Visual hyperacuity: spatiotemporal interpolation in human vision |
|
| What and where: a Bayesian inference theory of attention |
|
| Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review |
|
| Why The Brain Separates Face Recognition From Object Recognition |
|
| Wiener-like system identification in physiology |
|
