Now showing items 43-62 of 99

    • 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 ...
    • Image interpretation above and below the object level 

      Ben-Yosef, Guy; Ullman, Shimon (Center for Brains, Minds and Machines (CBMM), 2018-05-10)
      Computational models of vision have advanced in recent years at a rapid rate, rivaling in some areas human- level performance. Much of the progress to date has focused on analyzing the visual scene at the object level – ...
    • The infancy of the human brain 

      Dehaene-Lambertz, G.; Spelke, Elizabeth S. (Center for Brains, Minds and Machines (CBMM), Neuron, 2015-10-07)
      The human infant brain is the only known machine able to master a natural language and develop explicit, symbolic, and communicable systems of knowledge that deliver rich representations of the external world. With the ...
    • 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 ...
    • Learning An Invariant Speech Representation 

      Evangelopoulos, Georgios; Voinea, Stephen; Zhang, Chiyuan; Rosasco, Lorenzo; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-15)
      Recognition of speech, and in particular the ability to generalize and learn from small sets of labelled examples like humans do, depends on an appropriate representation of the acoustic input. We formulate the problem of ...
    • Learning Mid-Level Auditory Codes from Natural Sound Statistics 

      Mlynarski, Wiktor; McDermott, Josh (Center for Brains, Minds and Machines (CBMM), arXiv, 2017-01-25)
      Interaction with the world requires an organism to transform sensory signals into representations in which behaviorally meaningful properties of the environment are made explicit. These representations are derived through ...
    • 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 ...
    • Measuring and modeling the perception of natural and unconstrained gaze in humans and machines 

      Harari, Daniel; Gao, Tao; Kanwisher, Nancy; Tenenbaum, Joshua; Ullman, Shimon (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-11-28)
      Humans are remarkably adept at interpreting the gaze direction of other individuals in their surroundings. This skill is at the core of the ability to engage in joint visual attention, which is essential for establishing ...
    • Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection 

      Shen, Wei; Wang, Bin; Jiang, Yuan; Wang, Yan; Yuille, Alan L. (Center for Brains, Minds and Machines (CBMM), 2017-10-01)
      In the field of connectomics, neuroscientists seek to identify cortical connectivity comprehensively. Neuronal boundary detection from the Electron Microscopy (EM) images is often done to assist the automatic reconstruction ...
    • Musings on Deep Learning: Properties of SGD 

      Zhang, Chiyuan; Liao, Qianli; Rakhlin, Alexander; Sridharan, Karthik; Miranda, Brando; e.a. (Center for Brains, Minds and Machines (CBMM), 2017-04-04)
      [previously titled "Theory of Deep Learning III: Generalization Properties of SGD"] In Theory III we characterize with a mix of theory and experiments the generalization properties of Stochastic Gradient Descent in ...
    • 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 ...
    • A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation 

      Linderman, Scott W.; Johnson, Matthew J.; Wilson, Matthew A.; Chen, Zhe (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-12-01)
      Rodent hippocampal population codes represent important spatial information about the environment during navigation. Several computational methods have been developed to uncover the neural representation of spatial topology ...
    • A normalization model of visual search predicts single trial human fixations in an object search task. 

      Miconi, Thomas; Groomes, Laura; Kreiman, Gabriel (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-04-25)
      When searching for an object in a scene, how does the brain decide where to look next? Theories of visual search suggest the existence of a global attentional map, computed by integrating bottom-up visual information with ...
    • Notes on Hierarchical Splines, DCLNs and i-theory 

      Poggio, Tomaso; Rosasco, Lorenzo; Shashua, Amnon; Cohen, Nadav; Anselmi, Fabio (Center for Brains, Minds and Machines (CBMM), 2015-09-29)
      We define an extension of classical additive splines for multivariate function approximation that we call hierarchical splines. We show that the case of hierarchical, additive, piece-wise linear splines includes present-day ...
    • Object-Oriented Deep Learning 

      Liao, Qianli; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), 2017-10-31)
      We investigate an unconventional direction of research that aims at converting neural networks, a class of distributed, connectionist, sub-symbolic models into a symbolic level with the ultimate goal of achieving AI ...
    • 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 ...
    • On the Forgetting of College Academice: at "Ebbinghaus Speed"? 

      Subirana, Brian; Bagiati, Aikaterini; Sarma, Sanjay (Center for Brains, Minds and Machines (CBMM), 2017-06-20)
      How important are Undergraduate College Academics after graduation? How much do we actually remember after we leave the college classroom, and for how long? Taking a look at major University ranking methodologies one can ...
    • On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations 

      Cheney, Nicholas; Schrimpf, Martin; Kreiman, Gabriel (Center for Brains, Minds and Machines (CBMM), arXiv, 2017-04-03)
      Deep convolutional neural networks are generally regarded as robust function approximators. So far, this intuition is based on perturbations to external stimuli such as the images to be classified. Here we explore the ...
    • Parsing Occluded People by Flexible Compositions 

      Chen, Xianjie; Yuille, Alan L. (Center for Brains, Minds and Machines (CBMM), arXiv, 2015-06-01)
      This paper presents an approach to parsing humans when there is significant occlusion. We model humans using a graphical model which has a tree structure building on recent work [32, 6] and exploit the connectivity prior ...
    • Parsing Semantic Parts of Cars Using Graphical Models and Segment Appearance Consistency 

      Lu, Wenhao; Lian, Xiaochen; Yuille, Alan L. (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-13)
      This paper addresses the problem of semantic part parsing (segmentation) of cars, i.e.assigning every pixel within the car to one of the parts (e.g.body, window, lights, license plates and wheels). We formulate this as a ...