Search
Now showing items 1-10 of 20
The Language of Fake News: Opening the Black-Box of Deep Learning Based Detectors
(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 ...
Theory IIIb: Generalization in Deep Networks
(Center for Brains, Minds and Machines (CBMM), arXiv.org, 2018-06-29)
The general features of the optimization problem for the case of overparametrized nonlinear networks have been clear for a while: SGD selects with high probability global minima vs local minima. In the overparametrized ...
Classical generalization bounds are surprisingly tight for Deep Networks
(Center for Brains, Minds and Machines (CBMM), 2018-07-11)
Deep networks are usually trained and tested in a regime in which the training classification error is not a good predictor of the test error. Thus the consensus has been that generalization, defined as convergence of the ...
Constant Modulus Algorithms via Low-Rank Approximation
(Center for Brains, Minds and Machines (CBMM), 2018-04-12)
We present a novel convex-optimization-based approach to the solutions of a family of problems involving constant modulus signals. The family of problems includes the constant modulus and the constrained constant modulus, ...
DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion
(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 ...
Recurrent Multimodal Interaction for Referring Image Segmentation
(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 ...
Deep Regression Forests for Age Estimation
(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 ...
An analysis of training and generalization errors in shallow and deep networks
(Center for Brains, Minds and Machines (CBMM), arXiv.org, 2018-02-20)
An open problem around deep networks is the apparent absence of over-fitting despite large over-parametrization which allows perfect fitting of the training data. In this paper, we explain this phenomenon when each unit ...
Scene Graph Parsing as Dependency Parsing
(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 ...
Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results
(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 ...