Tomaso Poggio

Tomaso Armando Poggio (born 11 September 1947 in Genoa, Italy), is the Eugene McDermott professor in the Department of Brain and Cognitive Sciences, an investigator at the McGovern Institute for Brain Research, a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and director of both the Center for Biological and Computational Learning at MIT and the [http://cbmm.mit.edu/ Center for Brains, Minds, and Machines], a multi-institutional collaboration headquartered at the McGovern Institute since 2013. Provided by Wikipedia
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  1. 1
    The Levels of Understanding framework, revised
    by Poggio, Tomaso
    Published 2021
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  2. 2
    The Levels of Understanding framework, revised
    by Poggio, Tomaso A
    Published 2021
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  3. 3
    An Overview of Some Issues in the Theory of Deep Networks
    by Poggio, Tomaso, Banburski, Andrzej
    Published 2022
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  4. 4
    Do deep neural networks suffer from crowding?
    by Poggio, Tomaso, Roig, Gemma
    Published 2021
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  5. 5
    An Overview of Some Issues in the Theory of Deep Networks
    by Poggio, Tomaso, Banburski, Andrzej
    Published 2021
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  6. 6
    Neural Tuning Size in a Model of Primate Visual Processing Accounts for Three Key Markers of Holistic Face Processing
    by Tan, Cheston, Poggio, Tomaso A
    Published 2017
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  7. 7
    A Neuromorphic Approach to Computer Vision
    by Serre, Thomas, Poggio, Tomaso A.
    Published 2011
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  8. 8
    Turing++ Questions: A Test for the Science of (Human) Intelligence
    by Poggio, Tomaso A, Meyers, Ethan M
    Published 2017
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  9. 9
    Function approximation by deep networks
    by Mhaskar, H. N., Poggio, Tomaso A
    Published 2021
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  10. 10
    Do deep neural networks suffer from crowding?
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  11. 11
    Invariant recognition drives neural representations of action sequences
    Other Authors: “...Poggio, Tomaso A...”
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  12. 12
    On invariance and selectivity in representation learning
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  13. 13
    Holographic embeddings of knowledge graphs
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  14. 14
    Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows
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  15. 15
    Subtasks of unconstrained face recognition
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  16. 16
    Learning and disrupting invariance in visual recognition with a temporal association rule
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  17. 17
    Learning with group invariant features: A Kernel perspective
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  18. 18
    Learning invariant representations and applications to face verification
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  19. 19
    How important is weight symmetry in backpropagation?
    Other Authors: “...Poggio, Tomaso A...”
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  20. 20
    Learning manifolds with k-means and k-flats
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