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Mar 03, 2017
Conferences: ICLR
Machine Learning
Cryptography and Security
Computer Vision
Neural and Optical Computation

Adversarial Examples for Semantic Image Segmentation

Machine learning methods in general and Deep Neural Networks in particular have shown to be vulnerable to adversarial perturbations. So far this ph...
Sep 27, 2017
Computer Vision
Computation and Language

A Read-Write Memory Network for Movie Story Understanding

We propose a novel memory network model named Read-Write Memory Network (RWMN) to perform question and answering tasks for large-scale, multimodal ...
Jan 09, 2016
By zs2262
Conferences: test
Computer Vision

Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs

We address temporal action localization in untrimmed long videos. This is important because videos in real applications are usually unconstrained a...
Apr 07, 2017
By csilvia
Conferences: test
Artificial Intelligence
Machine Learning

Recurrent Environment Simulators

Models that can simulate how environments change in response to actions can be used by agents to plan and act efficiently. We improve on previous e...
Aug 30, 2017
Conferences: test
Computer Vision

Pix2face: Direct 3D Face Model Estimation

An efficient, fully automatic method for 3D face shape and pose estimation in unconstrained 2D imagery is presented. The proposed method jointly es...
Jun 07, 2014
Conferences: test
Machine Learning

A Drifting-Games Analysis for Online Learning and Applications to ...

We provide a general mechanism to design online learning algorithms based on a minimax analysis within a drifting-games framework. Different online...
Nov 05, 2017
Conferences: test
Machine Learning
Discrete Mathematics

We demonstrate the practical utility of the proposed framework and compare it to standard baselines. We compare the performance of the algorithms in terms of their wall-clock running time and the obtained utility. We consider the following problems:

Stochastic Submodular Maximization: The Case of Coverage Functions

Stochastic optimization of continuous objectives is at the heart of modern machine learning. However, many important problems are of discrete natur...
Jan 19, 2018
Conferences: CVPR
Computer Vision

Fully Point-wise Convolutional Neural Network for Modeling Statisti...

Modeling statistical regularities is the problem of representing the pixel distributions in natural images, and usually applied to solve the ill-po...
May 20, 2016
Conferences: test
Computer Vision
Machine Learning
Neural and Evolutionary Computation

Virtual Worlds as Proxy for Multi-Object Tracking Analysis

Modern computer vision algorithms typically require expensive data acquisition and accurate manual labeling. In this work, we instead leverage the ...
Apr 24, 2017
Conferences: CVPR
Computer Vision

Model-based Iterative Restoration for Binary Document Image Compres...

The inherent noise in the observed (e.g., scanned) binary document image degrades the image quality and harms the compression ratio through breakin...
Feb 11, 2015
By sioffe
Conferences: test
Machine Learning

Batch Normalization: Accelerating Deep Network Training by Reducing...

Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of...
Sep 15, 2017
Conferences: test
Artificial Intelligence
Computer Vision
Robotics

Unsupervised state representation learning with robotic priors: a ...

Our understanding of the world depends highly on our capacity to produce intuitive and simplified representations which can be easily used to solve...
Oct 30, 2017
Conferences: ICLR
Machine Learning
Artificial Intelligence
Social and Information Networks

Graph Attention Networks

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attent...
Sep 20, 2017
Conferences: test
Robotics
Artificial Intelligence
Machine Learning
Neural and Optical Computation

Bayesian Optimization with Automatic Prior Selection for Data-Effic...

One of the most interesting features of Bayesian optimization for direct policy search is that it can leverage priors (e.g., from simulation or fro...
May 22, 2017
By mathieu
Machine Learning

Factorization machines and polynomial networks are supervised polynomial models based on an efficient low-rank decomposition. We extend these models to the multi-output setting, i.e., for learning vector-valued functions, with application to multi...

Multi-output Polynomial Networks and Factorization Machines

Factorization machines and polynomial networks are supervised polynomial models based on an efficient low-rank decomposition. We extend these model...
Mar 05, 2018
Conferences: test
Machine Learning

Stochastic Activation Pruning for Robust Adversarial Defense

Neural networks are known to be vulnerable to adversarial examples. Carefully chosen perturbations to real images, while imperceptible to humans, i...
Mar 02, 2018
By ry017
Conferences: CVPR
Computer Vision

Pose-Robust Face Recognition via Deep Residual Equivariant Mapping

Face recognition achieves exceptional success thanks to the emergence of deep learning. However, many contemporary face recognition models still pe...
Mar 16, 2017
Conferences: test
Computer Vision
Machine Learning

Convolutional neural network architecture for geometric matching

We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine or thin-plate spline...
Jun 08, 2015
By songhan
Conferences: test
Neural and Evolutionary Computation
Computer Vision
Machine Learning

Learning both Weights and Connections for Efficient Neural Networks

Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional ne...
Oct 26, 2016
Conferences: test
Machine Learning
Social and Information Networks
Physics and Society

Estimating the Size of a Large Network and its Communities from a R...

Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties...
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