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Dec 11, 2016
Conferences: NIPS
Optimization and Control
Machine Learning

An Empirical Study of ADMM for Nonconvex Problems

The alternating direction method of multipliers (ADMM) is a common optimization tool for solving constrained and non-differentiable problems. We prov…
May 18, 2017
Conferences: ICLR
Machine Learning

Delving into adversarial attacks on deep policies

Adversarial examples have been shown to exist for a variety of deep learning architectures. Deep reinforcement learning has shown promising results o…
Mar 05, 2012
Conferences: ICCV
Computer Vision

Online Discriminative Dictionary Learning for Image Classification Ba…

Previous researches have demonstrated that the framework of dictionary learning with sparse coding, in which signals are decomposed as linear combina…
Feb 24, 2018
Conferences: CVPR
Computer Vision

Residual Dense Network for Image Super-Resolution

A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features …
Feb 14, 2018
Conferences: ICLR
Machine Learning

Uncertainty Estimation via Stochastic Batch Normalization

In this work, we investigate Batch Normalization technique and propose its probabilistic interpretation. We propose a probabilistic model and show th…
May 16, 2017
Conferences: ICML
Machine Learning
Computer Vision

Real-Time Adaptive Image Compression

We present a machine learning-based approach to lossy image compression which outperforms all existing codecs, while running in real-time. Our algo…
Aug 25, 2016
Conferences: ECCV
Computer Vision

Ambient Sound Provides Supervision for Visual Learning

The sound of crashing waves, the roar of fast-moving cars -- sound conveys important information about the objects in our surroundings. In this work,…
May 23, 2016
By ayanc
Conferences: NIPS
Machine Learning

Learning Sensor Multiplexing Design through Back-propagation

Recent progress on many imaging and vision tasks has been driven by the use of deep feed-forward neural networks, which are trained by propagating gr…
May 17, 2016
By zizhao
Conferences: CVPR
Computer Vision

SemiContour: A Semi-supervised Learning Approach for Contour Detection

Supervised contour detection methods usually require many labeled training images to obtain satisfactory performance. However, a large set of annotat…
Aug 22, 2017
By gh349
Conferences: ICCV
Computer Vision
Artificial Intelligence
Machine Learning

Learning Efficient Convolutional Networks through Network Slimming

The deployment of deep convolutional neural networks (CNNs) in many real world applications is largely hindered by their high computational cost. In …
May 22, 2017
Conferences: CVPR
Computer Vision

Facial Expression Recognition Using Enhanced Deep 3D Convolutional …

Deep Neural Networks (DNNs) have shown to outperform traditional methods in various visual recognition tasks including Facial Expression Recognition …
Nov 01, 2016
Conferences: ICLR
Artificial Intelligence
Computer Vision

Learning recurrent representations for hierarchical behavior modeling

We propose a framework for detecting action patterns from motion sequences and modeling the sensory-motor relationship of animals, using a generative…
Apr 09, 2018
Conferences: CVPR
Computer Vision

Photometric Stereo in Participating Media Considering Shape-Dependent…

Images captured in participating media such as murky water, fog, or smoke are degraded by scattered light. Thus, the use of traditional three-dimensi…
Dec 21, 2015
Conferences: CVPR
Computer Vision

Quantized Convolutional Neural Networks for Mobile Devices

Recently, convolutional neural networks (CNN) have demonstrated impressive performance in various computer vision tasks. However, high performance ha…
Jun 13, 2011
By shie
Conferences: NIPS
Machine Learning

From Bandits to Experts: On the Value of Side-Observations

We consider an adversarial online learning setting where a decision maker can choose an action in every stage of the game. In addition to observing t…
Sep 24, 2018
By stec
Conferences: test
Machine Learning

Unified recurrent neural network for many feature types

There are time series that are amenable to recurrent neural network (RNN) solutions when treated as sequences, but some series, e.g. asynchronous tim…
Dec 01, 2017
Conferences: NIPS
Machine Learning
Optimization and Control

The reparameterization trick for acquisition functions

Bayesian optimization is a sample-efficient approach to solving global optimization problems. Along with a surrogate model, this approach relies on t…
Nov 23, 2016
Conferences: CVPR
Computer Vision

Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose

This paper addresses the challenge of 3D human pose estimation from a single color image. Despite the general success of the end-to-end learning para…
Jul 08, 2017
By zizhao
Conferences: CVPR
Computer Vision

MDNet: A Semantically and Visually Interpretable Medical Image Diagno…

The inability to interpret the model prediction in semantically and visually meaningful ways is a well-known shortcoming of most existing computer-ai…
Nov 03, 2015
By vb292
Conferences: NIPS
Machine Learning
Artificial Intelligence
Computer Vision

Understanding symmetries in deep networks

Recent works have highlighted scale invariance or symmetry present in the weight space of a typical deep network and the adverse effect it has on the…
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