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Unsupervised learning of clutter-resistant visual representations from natural videos 

Liao, Qianli; Leibo, Joel Z; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2015-04-27)
Populations of neurons in inferotemporal cortex (IT) maintain an explicit code for object identity that also tolerates transformations of object appearance e.g., position, scale, viewing angle [1, 2, 3]. Though the learning ...
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The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex 

Leibo, Joel Z; Liao, Qianli; Anselmi, Fabio; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), bioRxiv, 2015-04-26)
Is visual cortex made up of general-purpose information processing machinery, or does it consist of a collection of specialized modules? If prior knowledge, acquired from learning a set of objects is only transferable to ...
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Neural tuning size is a key factor underlying holistic face processing 

Tan, Cheston; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-14)
Faces are a class of visual stimuli with unique significance, for a variety of reasons. They are ubiquitous throughout the course of a person’s life, and face recognition is crucial for daily social interaction. Faces are ...
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Can a biologically-plausible hierarchy e ectively replace face detection, alignment, and recognition pipelines? 

Liao, Qianli; Leibo, Joel Z; Mroueh, Youssef; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-03-27)
The standard approach to unconstrained face recognition in natural photographs is via a detection, alignment, recognition pipeline. While that approach has achieved impressive results, there are several reasons to be ...
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Learning Real and Boolean Functions: When Is Deep Better Than Shallow 

Mhaskar, Hrushikesh; Liao, Qianli; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-03-08)
We describe computational tasks - especially in vision - that correspond to compositional/hierarchical functions. While the universal approximation property holds both for hierarchical and shallow networks, we prove that ...
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Representation Learning in Sensory Cortex: a theory 

Anselmi, Fabio; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), 2014-11-14)
We review and apply a computational theory of the feedforward path of the ventral stream in visual cortex based on the hypothesis that its main function is the encoding of invariant representations of images. A key ...
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Fast, invariant representation for human action in the visual system 

Isik, Leyla; Tacchetti, Andrea; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-01-06)
The ability to recognize the actions of others from visual input is essential to humans' daily lives. The neural computations underlying action recognition, however, are still poorly understood. We use magnetoencephalography ...
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View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation 

Leibo, Joel Z.; Liao, Qianli; Freiwald, Winrich; Anselmi, Fabio; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-06-03)
The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and relatively robust against identity-preserving ...

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Poggio, Tomaso (8)
Liao, Qianli (4)Anselmi, Fabio (3)Leibo, Joel Z (3)Freiwald, Winrich (1)Isik, Leyla (1)Leibo, Joel Z. (1)Liao, Qianli (1)Mhaskar, Hrushikesh (1)Mroueh, Youssef (1)... View MoreSubject
Computer vision (8)
Invariance (4)Artificial Intelligence (2)Face recognition (2)Hierarchy (2)Machine Learning (2)computational tasks (1)Computational Theory (1)Magnetoencephalography (MEG) (1)Object Recognition (1)... View MoreDate Issued2014 (3)2016 (3)2015 (2)Has File(s)Yes (8)

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