Now showing items 43-62 of 118

    • Full interpretation of minimal images 

      Ben-Yosef, Guy; Assif, Liav; Ullman, Shimon (Center for Brains, Minds and Machines (CBMM), 2017-02-08)
      The goal in this work is to model the process of ‘full interpretation’ of object images, which is the ability to identify and localize all semantic features and parts that are recognized by human observers. The task is ...
    • The Genesis Story Understanding and Story Telling System A 21st Century Step toward Artificial Intelligence 

      Winston, Patrick Henry (Center for Brains, Minds and Machines (CBMM), 2014-06-10)
      Story understanding is an important differentiator of human intelligence, perhaps the most important differentiator. The Genesis system was built to model and explore aspects of story understanding using simply expressed, ...
    • Group Invariant Deep Representations for Image Instance Retrieval 

      Morère, Olivier; Veillard, Antoine; Lin, Jie; Petta, Julie; Chandrasekhar, Vijay; e.a. (Center for Brains, Minds and Machines (CBMM), 2016-01-11)
      Most image instance retrieval pipelines are based on comparison of vectors known as global image descriptors between a query image and the database images. Due to their success in large scale image classification, ...
    • Hierarchically Local Tasks and Deep Convolutional Networks 

      Deza, Arturo; Liao, Qianli; Banburski, Andrzej; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), 2020-06-24)
      The main success stories of deep learning, starting with ImageNet, depend on convolutional networks, which on certain tasks perform significantly better than traditional shallow classifiers, such as support vector machines. ...
    • Hippocampal Remapping as Hidden State Inference 

      Sanders, Honi; Wilson, Matthew A.; Gershman, Samueal J. (Center for Brains, Minds and Machines (CBMM), bioRxiv, 2019-08-22)
      Cells in the hippocampus tuned to spatial location (place cells) typically change their tuning when an animal changes context, a phenomenon known as remapping. A fundamental challenge to understanding remapping is the fact ...
    • Holographic Embeddings of Knowledge Graphs 

      Nickel, Maximilian; Rosasco, Lorenzo; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2015-11-16)
      Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HolE) to learn ...
    • How Important is Weight Symmetry in Backpropagation? 

      Liao, Qianli; Leibo, Joel Z.; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2015-11-29)
      Gradient backpropagation (BP) requires symmetric feedforward and feedback connections—the same weights must be used for forward and backward passes. This “weight transport problem” [1] is thought to be one of the main ...
    • Human-like Learning: A Research Proposal 

      Liao, Qianli; Poggio, Tomaso (2017-09-28)
      We propose Human-like Learning, a new machine learning paradigm aiming at training generalist AI systems in a human-like manner with a focus on human-unique skills.
    • Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding 

      Mottaghi, Roozbeh; Fidler, Sanja; Yuille, Alan L.; Urtasun, Raquel; Parikh, Devi (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-15)
      Recent trends in image understanding have pushed for holistic scene understanding models that jointly reason about various tasks such as object detection, scene recognition, shape analysis, contextual reasoning, and local ...
    • 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 ...
    • 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 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 ...
    • 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 ...