Now showing items 61-80 of 143

    • 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 – ...
    • Image interpretation by iterative bottom-up top-down processing 

      Ullman, Shimon; Assif, Liav; Strugatski, Alona; Vatashsky, Ben-Zion; Levi, Hila; e.a. (Center for Brains, Minds and Machines (CBMM), 2021-11-01)
      Scene understanding requires the extraction and representation of scene components, such as objects and their parts, people, and places, together with their individual properties, as well as relations and interactions ...
    • Implicit dynamic regularization in deep networks 

      Poggio, Tomaso; Liao, Qianli (Center for Brains, Minds and Machines (CBMM), 2020-08-17)
      Square loss has been observed to perform well in classification tasks, at least as well as crossentropy. However, a theoretical justification is lacking. Here we develop a theoretical analysis for the square loss that also ...
    • Incorporating Rich Social Interactions Into MDPs 

      Tejwani, Ravi; Kuo, Yen-Ling; Shu, Tianmin; Stankovits, Bennett; Gutfreund, Dan; e.a. (Center for Brains, Minds and Machines (CBMM), International Conference on Robotics and Automation (ICRA), 2022-02-07)
      Much of what we do as humans is engage socially with other agents, a skill that robots must also eventually possess. We demonstrate that a rich theory of social interactions originating from microso- ciology and economics ...
    • 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 ...
    • The Language of Fake News: Opening the Black-Box of Deep Learning Based Detectors 

      O'Brien, Nicole; Latessa, Sophia; Evangelopoulos, Georgios; Boix, Xavier (Center for Brains, Minds and Machines (CBMM), 2018-11-01)
      The digital information age has generated new outlets for content creators to publish so-called “fake news”, a new form of propaganda that is intentionally designed to mislead the reader. With the widespread effects of the ...
    • Learning a natural-language to LTL executable semantic parser for grounded robotics 

      Wang, Christopher; Ross, Candace; Kuo, Yen-Ling; Katz, Boris; Barbu, Andrei (Center for Brains, Minds and Machines (CBMM), Conference on Robot Learning (CoRL), 2020-11-16)
      Children acquire their native language with apparent ease by observing how language is used in context and attempting to use it themselves. They do so without laborious annotations, negative examples, or even direct ...
    • 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 ...
    • Loss landscape: SGD can have a better view than GD 

      Poggio, Tomaso; Cooper, Yaim (Center for Brains, Minds and Machines (CBMM), 2020-07-01)
      Consider a loss function L = 􏰀ni=1 l2i with li = f(xi) − yi, where f(x) is a deep feedforward network with R layers, no bias terms and scalar output. Assume the network is overparametrized that is, d >> n, where d is the ...
    • 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 ...
    • Measuring Social Biases in Grounded Vision and Language Embeddings 

      Ross, Candace; Barbu, Andrei; Katz, Boris (Center for Brains, Minds and Machines (CBMM), Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL), 2021-06-06)
      We generalize the notion of measuring social biases in word embeddings to visually grounded word embeddings. Biases are present in grounded embeddings, and indeed seem to be equally or more significant than for ungrounded ...
    • 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 Regression, Representational Similarity, Model Zoology Neural Taskonomy at Scale in Rodent Visual Cortex 

      Conwell, Colin; Mayo, David; Buice, Michael A.; Katz, Boris; Alvarez, George A.; e.a. (Center for Brains, Minds and Machines (CBMM), The Thirty-fifth Annual Conference on Neural Information Processing Systems (NeurIPS), 2021-12-06)
      How well do deep neural networks fare as models of mouse visual cortex? A majority of research to date suggests results far more mixed than those produced in the modeling of primate visual cortex. Here, we perform a ...
    • 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 ...