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On Invariance and Selectivity in Representation Learning 

Anselmi, Fabio; Rosasco, Lorenzo; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2015-03-23)
We discuss data representation which can be learned automatically from data, are invariant to transformations, and at the same time selective, in the sense that two points have the same representation only if they are one ...
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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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Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results 

Arend, Luke; Han, Yena; Schrimpf, Martin; Bashivan, Pouya; Kar, Kohitij; e.a. (Center for Brains, Minds and Machines (CBMM), 2018-11-02)
Deep neural networks have been shown to predict neural responses in higher visual cortex. The mapping from the model to a neuron in the brain occurs through a linear combination of many units in the model, leaving open the ...
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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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Computational role of eccentricity dependent cortical magnification 

Poggio, Tomaso; Mutch, Jim; Isik, Leyla (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-06)
We develop a sampling extension of M-theory focused on invariance to scale and translation. Quite surprisingly, the theory predicts an architecture of early vision with increasing receptive field sizes and a high resolution ...
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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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I-theory on depth vs width: hierarchical function composition 

Poggio, Tomaso; Anselmi, Fabio; Rosasco, Lorenzo (Center for Brains, Minds and Machines (CBMM), 2015-12-29)
Deep learning networks with convolution, pooling and subsampling are a special case of hierar- chical architectures, which can be represented by trees (such as binary trees). Hierarchical as well as shallow networks can ...
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Group Invariant Deep Representations for Image Instance Retrieval 

Morère, Olivier; Veillard, Antoine; Lin, Jie; Petta, Julie; Chandrasekhar, Vijay; e.a. (Center for Brains, Minds and Machines (CBMM), 2016-01-11)
Most image instance retrieval pipelines are based on comparison of vectors known as global image descriptors between a query image and the database images. Due to their success in large scale image classification, ...
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Foveation-based Mechanisms Alleviate Adversarial Examples 

Lou, Yan; Boix, Xavier; Roig, Gemma; Poggio, Tomaso; Zhao, Qi (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-01-19)
We show that adversarial examples, i.e., the visually imperceptible perturbations that result in Convolutional Neural Networks (CNNs) fail, can be alleviated with a mechanism based on foveations---applying the CNN in ...
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Poggio, Tomaso (41)
Liao, Qianli (18)Rosasco, Lorenzo (10)Anselmi, Fabio (8)Miranda, Brando (6)Mhaskar, Hrushikesh (5)Zhang, Chiyuan (4)Boix, Xavier (3)Hidary, Jack (3)Leibo, Joel Z (3)... View MoreSubjectInvariance (9)Computer vision (8)Machine Learning (7)Hierarchy (5)i-theory (4)Artificial Intelligence (2)Batch Normalization (BN) (2)Convolutional Neural Networks (CNN) (2)Deep Convolutional Learning Networks (DCLNs) (2)Face recognition (2)... View MoreDate Issued2016 (9)2017 (9)2015 (8)2018 (6)2014 (5)2019 (3)Has File(s)Yes (40)No (1)

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