Now showing items 57-76 of 99

    • 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 ...
    • Predicting Actions Before They Occur 

      Vaziri-Pashkam, Maryam; Cormiea, Sarah; Nakayama, Ken (Center for Brains, Minds and Machines (CBMM), 2015-10-26)
      Humans are experts at reading others’ actions in social contexts. They efficiently process others’ movements in real-time to predict intended goals. Here we designed a two-person reaching task to investigate real-time body ...
    • Probing the compositionality of intuitive functions 

      Schulz, Eric; Tenenbaum, Joshua B.; Duvenaud, David; Speekenbrink, Maarten; Gershman, Samuel J. (Center for Brains, Minds and Machines (CBMM), 2016-05-26)
      How do people learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is accomplished by harnessing compositionality: complex structure is decomposed into ...
    • 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 ...