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
    Neural Tuning Size in a Model of Primate Visual Processing Accounts for Three Key Markers of Holistic Face Processing.
    by Cheston Tan, Tomaso Poggio
    Published 2016-01-01
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  2. 2
    Invariant recognition drives neural representations of action sequences.
    by Andrea Tacchetti, Leyla Isik, Tomaso Poggio
    Published 2017-12-01
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  3. 3
    Complexity control by gradient descent in deep networks
    by Tomaso Poggio, Qianli Liao, Andrzej Banburski
    Published 2020-02-01
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  4. 4
    The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex.
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  5. 5
    On foveation of deep neural networks
    by Srivastava, Sanjana.
    Published 2019
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  6. 6
    Detecting basal cell carcinoma in skin histopathological images using deep learning
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  7. 7
    Structured learning and inference with neural networks and generative models
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  8. 8
    Pose-invariant face recognition using real and virtual views
    by Beymer, David James
    Published 2007
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  9. 9
    Advances in component-based face detection
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  10. 10
    Automated phenotyping of mouse social behavior
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  11. 11
    Rotation invariant real-time face detection and recognition system
    Other Authors: “...Tomaso Poggio....”
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  12. 12
    Importance sampling for reinforcement learning with multiple objectives
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  13. 13
    Machine learning for detection of fake news
    by O'Brien, Nicole (Nicole J.)
    Published 2018
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  14. 14
    Effects of neuronal correlations on population decoding and encoding models
    by Patel, Ami (Ami M.)
    Published 2017
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  15. 15
    Deep learning and structured data
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  16. 16
    Multi-source contingency clustering
    by Bouvrie, Jacob V
    Published 2006
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  17. 17
    Dorsal stream : from algorithm to neuroscience
    by Jhuang, Hueihan
    Published 2011
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  18. 18
    Component-based face recognition with 3D morphable models
    by Huang, Jennifer, 1980-
    Published 2014
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  19. 19
    Learning and inference with Wasserstein metrics
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  20. 20
    Mouse behavior recognition with the wisdom of crowd
    by Ni, Yuzhao
    Published 2013
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