Now showing items 21-40 of 99

    • Deep Nets: What have they ever done for Vision? 

      Yuille, Alan L.; Liu, Chenxi (Center for Brains, Minds and Machines (CBMM), 2018-05-10)
      This is an opinion paper about the strengths and weaknesses of Deep Nets. They are at the center of recent progress on Artificial Intelligence and are of growing importance in Cognitive Science and Neuroscience since they ...
    • Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning 

      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 ...
    • Deep Regression Forests for Age Estimation 

      Shen, Wei; Guo, Yilu; Wang, Yan; Zhao, Kai; Wang, Bo; e.a. (Center for Brains, Minds and Machines (CBMM), 2018-06-01)
      Age estimation from facial images is typically cast as a nonlinear regression problem. The main challenge of this problem is the facial feature space w.r.t. ages is inhomogeneous, due to the large variation in facial ...
    • A Deep Representation for Invariance And Music Classification 

      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 ...
    • Deep vs. shallow networks : An approximation theory perspective 

      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 ...
    • DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion 

      Zhang, Zhishuai; Xie, Cihang; Wang, Jianyu; Xie, Lingxi; Yuille, Alan L. (Center for Brains, Minds and Machines (CBMM), 2018-06-19)
      In this paper, we study the task of detecting semantic parts of an object, e.g., a wheel of a car, under partial occlusion. We propose that all models should be trained without seeing occlusions while being able to transfer ...
    • Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts 

      Chen, Xianjie; Mottaghi, Roozbeh; Liu, Xiaobai; Fidler, Sanja; Urtasun, Raquel; e.a. (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 ...
    • Detecting Semantic Parts on Partially Occluded Objects 

      Wang, Jianyu; Xe, Cihang; Zhang, Zhishuai; Zhu, Jun; Xie, Lingxi; e.a. (Center for Brains, Minds and Machines (CBMM), 2017-09-04)
      In this paper, we address the task of detecting semantic parts on partially occluded objects. We consider a scenario where the model is trained using non-occluded images but tested on occluded images. The motivation is ...
    • Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets 

      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 ...
    • Do Deep Neural Networks Suffer from Crowding? 

      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 ...
    • Do You See What I Mean? Visual Resolution of Linguistic Ambiguities 

      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 ...
    • Double descent in the condition number 

      Poggio, Tomaso; Kur, Gil; Banburski, Andrzej (Center for Brains, Minds and Machines (CBMM), 2019-12-04)
      In solving a system of n linear equations in d variables Ax=b, the condition number of the (n,d) matrix A measures how much errors in the data b affect the solution x. Bounds of this type are important in many inverse ...
    • Exact Equivariance, Disentanglement and Invariance of Transformations 

      Liao, Qianli; Poggio, Tomaso (2017-12-31)
      Invariance, equivariance and disentanglement of transformations are important topics in the field of representation learning. Previous models like Variational Autoencoder [1] and Generative Adversarial Networks [2] attempted ...
    • Fast, invariant representation for human action in the visual system 

      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 ...
    • Foveation-based Mechanisms Alleviate Adversarial Examples 

      Lou, Yan; Boix, Xavier; Roig, Gemma; Poggio, Tomaso; Zhao, Qi (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-01-19)
      We show that adversarial examples, i.e., the visually imperceptible perturbations that result in Convolutional Neural Networks (CNNs) fail, can be alleviated with a mechanism based on foveations---applying the CNN in ...
    • 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, ...
    • 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 ...