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Browsing Center for Brains, Minds & Machines by Title

Research and Teaching Output of the MIT Community

Browsing Center for Brains, Minds & Machines by Title

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  • Amir, Nadav; Besold, Tarek R.; Camoriano, Rafaello; Erdogan, Goker; Flynn, Thomas; Gillary, Grant; Gomez, Jesse; Herbert-Voss, Ariel; Hotan, Gladia; Kadmon, Jonathan; Linderman, Scott W.; Liu, Tina T.; Marantan, Andrew; Olson, Joseph; Orchard, Garrick; Pal, Dipan K.; Pasquale, Giulia; Sanders, Honi; Silberer, Carina; Smith, Kevin A.; de Brito, Carols Stein N.; Suchow, Jordan W.; Tessler, M. H.; Viejo, Guillaume; Walker, Drew; Wehbe, Leila (Center for Brains, Minds and Machines (CBMM), 2014-09-26)
    A compilation of abstracts from the student projects of the 2014 Brains, Minds, and Machines Summer School, held at Woods Hole Marine Biological Lab, May 29 - June 12, 2014.
  • Berzak, Yevgeni; Huang, Yan; Barbu, Andrei; Korhonen, Anna; Katz, Boris (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-09-21)
    Published in the Proceedings of EMNLP 2016 We present a study on two key characteristics of human syntactic annotations: anchoring and agreement. Anchoring is a well-known cognitive bias in human decision making, where ...
  • Liao, Qianli; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-04-12)
    We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the primate visual cortex. We begin with the observation that a shallow RNN is exactly equivalent to a very deep ResNet with ...
  • Lake, Brenden M.; Ullman, Tomer D.; Tenenbaum, Joshua B.; Gershman, Samuel J. (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-04-01)
    Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object ...
  • 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 ...
  • Yuille, Alan L.; Mottaghi, Roozbeh (Center for Brains, Minds and Machines (CBMM), arXiv, 2015-05-05)
    This paper performs a complexity analysis of a class of serial and parallel compositional models of multiple objects and shows that they enable efficient representation and rapid inference. Compositional models are generative ...
  • Barbu, Andrei; Narayanaswamy, Siddharth; Xiong, Caiming; Corso, Jason J.; Fellbaum, Christiane D.; Hanson, Catherine; Hanson, Stephen Jose; Helie, Sebastien; Malaia, Evguenia; Pearlmutter, Barak A.; Siskind, Jeffrey Mark; Talavage, Thomas Michael; Wilbur, Ronnie B. (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-07-14)
    How does the human brain represent simple compositions of constituents: actors, verbs, objects, directions, and locations? Subjects viewed videos during neuroimaging (fMRI) sessions from which sentential descriptions of ...
  • 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 ...
  • Goodman, Noah D.; Tenenbaum, Joshua B.; Gerstenberg, Tobias (Center for Brains, Minds and Machines (CBMM), 2014-06-14)
    Knowledge organizes our understanding of the world, determining what we expect given what we have already seen. Our predictive representations have two key properties: they are productive, and they are graded. Productive ...
  • Berzak, Yevgeni; Reichart, Roi; Katz, Boris (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-06-05)
    This work examines the impact of crosslinguistic transfer on grammatical errors in English as Second Language (ESL) texts. Using a computational framework that formalizes the theory of Contrastive Analysis (CA), we demonstrate ...
  • Mao, Junhua; Xu, Wei; Yang, Yi; Wang, Jiang; Huang, Zhiheng; Yuille, Alan L. (Center for Brains, Minds and Machines (CBMM), arXiv, 2015-05-07)
    In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. ...
  • Anselmi, Fabio; Rosasco, Lorenzo; Tan, Cheston; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2015-08-05)
    We extend i-theory to incorporate not only pooling but also rectifying nonlinearities in an extended HW module (eHW) designed for supervised learning. The two operations roughly correspond to invariance and selectivity, ...
  • Lotter, William; Kreiman, Gabriel; Cox, David (Center for Brains, Minds and Machines (CBMM), arXiv, 2017-03-01)
    While great strides have been made in using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning—leveraging unlabeled examples to learn about the structure of a domain — remains ...
  • Zhang, Chiyuan; Evangelopoulos, Georgios; Voinea, Stephen; Rosasco, Lorenzo; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-17-03)
    Representations in the auditory cortex might be based on mechanisms similar to the visual ventral stream; modules for building invariance to transformations and multiple layers for compositionality and selectivity. In this ...
  • Mhaskar, Hrushikesh; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-08-12)
    The paper briefly reviews several recent results on hierarchical architectures for learning from examples, that may formally explain the conditions under which Deep Convolutional Neural Networks perform much better in ...
  • Chen, Xianjie; Mottaghi, Roozbeh; Liu, Xiaobai; Fidler, Sanja; Urtasun, Raquel; Yuille, Alan L. (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-10)
    Detecting objects becomes difficult when we need to deal with large shape deformation, occlusion and low resolution. We propose a novel approach to i) handle large deformations and partial occlusions in animals (as examples ...
  • Tachetti, Andrea; Voinea, Stephen; Evangelopoulos, Georgios (Center for Brains, Minds and Machines (CBMM), arXiv, 2017-03-13)
    The complexity of a learning task is increased by transformations in the input space that preserve class identity. Visual object recognition for example is affected by changes in viewpoint, scale, illumination or planar ...
  • Volokitin, Anna; Roig, Gemma; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2017-06-26)
    Crowding is a visual effect suffered by humans, in which an object that can be recognized in isolation can no longer be recognized when other objects, called flankers, are placed close to it. In this work, we study the ...
  • Berzak, Yevgeni; Barbu, Andrei; Harari, Daniel; Katz, Boris; Ullman, Shimon (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-06-10)
    Understanding language goes hand in hand with the ability to integrate complex contextual information obtained via perception. In this work, we present a novel task for grounded language understanding: disambiguating a ...
  • 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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