Now showing items 65-84 of 99

    • Reconstructing Native Language Typology from Foreign Language Usage 

      Berzak, Yevgeni; Reichart, Roi; Katz, Boris (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-04-25)
      Linguists and psychologists have long been studying cross-linguistic transfer, the influence of native language properties on linguistic performance in a foreign language. In this work we provide empirical evidence for ...
    • Recurrent Multimodal Interaction for Referring Image Segmentation 

      Liu, Chenxi; Lin, Zhe; Shen, Xiaohui; Yang, Jimei; Lu, Xin; e.a. (Center for Brains, Minds and Machines (CBMM), 2018-05-10)
      In this paper we are interested in the problem of image segmentation given natural language descriptions, i.e. referring expressions. Existing works tackle this problem by first modeling images and sentences independently ...
    • 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 ...
    • A Review of Relational Machine Learning for Knowledge Graphs 

      Nickel, Maximilian; Murphy, Kevin; Tresp, Volker; Gabrilovich, Evgeniy (Center for Brains, Minds and Machines (CBMM), arXiv, 2015-03-23)
      Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be “trained” on large knowledge graphs, ...
    • Robust Estimation of 3D Human Poses from a Single Image 

      Wang, Chunyu; Wang, Yizhou; Lin, Zhouchen; Yuille, Alan L.; Gao, Wen (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-10)
      Human pose estimation is a key step to action recognition. We propose a method of estimating 3D human poses from a single image, which works in conjunction with an existing 2D pose/joint detector. 3D pose estimation is ...
    • A role for recurrent processing in object completion: neurophysiological, psychophysical and computational evidence. 

      Tang, Hanlin; Buia, Calin; Madsen, Joseph R.; Anderson, William S.; Kreiman, Gabriel (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-04-26)
      Recognition of objects from partial information presents a significant challenge for theories of vision because it requires spatial integration and extrapolation from prior knowledge. We combined neurophysiological recordings ...
    • Scene Graph Parsing as Dependency Parsing 

      Wang, Yu-Siang; Liu, Chenxi; Zeng, Xiaohui; Yuille, Alan L. (Center for Brains, Minds and Machines (CBMM), 2018-05-10)
      In this paper, we study the problem of parsing structured knowledge graphs from textual descrip- tions. In particular, we consider the scene graph representation that considers objects together with their attributes and ...
    • The Secrets of Salient Object Segmentation 

      Li, Yin; Hou, Xiaodi; Koch, Christof; Rehg, James M.; Yuille, Alan L. (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-13)
      In this paper we provide an extensive evaluation of fixation prediction and salient object segmentation algorithms as well as statistics of major datasets. Our analysis identifies serious design flaws of existing salient ...
    • Seeing is Worse than Believing: Reading People’s Minds Better than Computer-Vision Methods Recognize Actions 

      Barbu, Andrei; Barrett, Daniel P.; Chen, Wei; Narayanaswamy, Siddharth; Xiong, Caiming; e.a. (2015-12-10)
      We had human subjects perform a one-out-of-six class action recognition task from video stimuli while undergoing functional magnetic resonance imaging (fMRI). Support-vector machines (SVMs) were trained on the recovered ...
    • Seeing What You’re Told: Sentence-Guided Activity Recognition In Video 

      Siddharth, Narayanaswamy; Barbu, Andrei; Siskind, Jeffrey Mark (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-05-29)
      We present a system that demonstrates how the compositional structure of events, in concert with the compositional structure of language, can interplay with the underlying focusing mechanisms in video action recognition, ...
    • Semantic Part Segmentation using Compositional Model combining Shape and Appearance 

      Wang, Jianyu; Yuille, Alan L. (Center for Brains, Minds and Machines (CBMM), arXiv, 2015-06-08)
      In this paper, we study the problem of semantic part segmentation for animals. This is more challenging than standard object detection, object segmentation and pose estimation tasks because semantic parts of animals often ...
    • Sensitivity to Timing and Order in Human Visual Cortex. 

      Singer, Jedediah M.; Madsen, Joseph R.; Anderson, William S.; Kreiman, Gabriel (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-04-25)
      Visual recognition takes a small fraction of a second and relies on the cascade of signals along the ventral visual stream. Given the rapid path through multiple processing steps between photoreceptors and higher visual ...
    • Simultaneous whole‐animal 3D imaging of neuronal activity using light‐field microscopy 

      Prevedel, Robert; Yoon, Young-Gyu; Hoffman, Maximilian; Pak, Nikita; Wetzstein, Gordon; e.a. (Center for Brains, Minds and Machines (CBMM), 2014-05-18)
      High-speed, large-scale three-dimensional (3D) imaging of neuronal activity poses a major challenge in neuroscience. Here we demonstrate simultaneous functional imaging of neuronal activity at single-neuron resolution in ...
    • 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 ...
    • Single-Shot Object Detection with Enriched Semantics 

      Zhang, Zhishuai; Qiao, Siyuan; Xie, Cihang; Shen, Wei; Wang, Bo; e.a. (Center for Brains, Minds and Machines (CBMM), 2018-06-19)
      We propose a novel single shot object detection network named Detection with Enriched Semantics (DES). Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic ...
    • Spatiotemporal interpretation features in the recognition of dynamic images 

      Ben-Yosef, Guy; Kreiman, Gabriel; Ullman, Shimon (Center for Brains, Minds and Machines (CBMM), 2018-11-21)
      Objects and their parts can be visually recognized and localized from purely spatial information in static images and also from purely temporal information as in the perception of biological motion. Cortical regions have ...
    • Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning 

      Liao, Qianli; Kawaguchi, Kenji; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-10-19)
      We systematically explored a spectrum of normalization algorithms related to Batch Normalization (BN) and propose a generalized formulation that simultaneously solves two major limitations of BN: (1) online learning and ...
    • Symmetry Regularization 

      Anselmi, Fabio; Evangelopoulos, Georgios; Rosasco, Lorenzo; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), 2017-05-26)
      The properties of a representation, such as smoothness, adaptability, generality, equivari- ance/invariance, depend on restrictions imposed during learning. In this paper, we propose using data symmetries, in the sense of ...
    • Theoretical Issues in Deep Networks 

      Poggio, Tomaso; Banburski, Andrzej; Liao, Qianli (Center for Brains, Minds and Machines (CBMM), 2019-08-17)
      While deep learning is successful in a number of applications, it is not yet well understood theoretically. A theoretical characterization of deep learning should answer questions about their approximation power, the ...
    • Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality? 

      Poggio, Tomaso; Mhaskar, Hrushikesh; Rosasco, Lorenzo; Miranda, Brando; Liao, Qianli (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-11-23)
      [formerly titled "Why and When Can Deep – but Not Shallow – Networks Avoid the Curse of Dimensionality: a Review"] The paper reviews and extends an emerging body of theoretical results on deep learning including the ...