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Apr 20, 2017
By yinxi1
Conferences: ICCV
Computer Vision

Towards Large-Pose Face Frontalization in the Wild

Despite recent advances in face recognition using deep learning, severe accuracy drops are observed for large pose variations in unconstrained enviro…
Sep 05, 2017
Conferences: ICCV
Computer Vision

Dense Face Alignment

Face alignment is a classic problem in the computer vision field. Previous works mostly focus on sparse alignment with a limited number of facial lan…
Nov 23, 2016
By slund1
Conferences: NIPS
Artificial Intelligence

An unexpected unity among methods for interpreting model predictions

Understanding why a model made a certain prediction is crucial in many data science fields. Interpretable predictions engender appropriate trust and …
Nov 24, 2015
Conferences: ICLR
Computer Vision

Learning Visual Predictive Models of Physics for Playing Billiards

The ability to plan and execute goal specific actions in varied, unexpected settings is a central requirement of intelligent agents. In this paper, w…
Jun 27, 2012
Conferences: ICML
Machine Learning
Cryptography and Security

Poisoning Attacks against Support Vector Machines

We investigate a family of poisoning attacks against Support Vector Machines (SVM). Such attacks inject specially crafted training data that increase…
Jul 31, 2017
Conferences: ICCV
Computer Vision

Scene Graph Generation from Objects, Phrases and Region Captions

Object detection, scene graph generation and region captioning, which are three scene understanding tasks at different semantic levels, are tied toge…
Mar 19, 2018
Conferences: ECCV
Computer Vision

Weakly Supervised Object Localization on grocery shelves using simple…

We propose a weakly supervised method using two algorithms to predict object bounding boxes given only an image classification dataset. First algorit…
Nov 08, 2013
Conferences: CVPR
Computer Vision

Submodularization for Quadratic Pseudo-Boolean Optimization

Many computer vision problems require optimization of binary non-submodular energies. We propose a general optimization framework based on local subm…
Aug 03, 2017
By zayd
Conferences: ICLR
Sound
Machine Learning

Audio Super Resolution using Neural Networks

We introduce a new audio processing technique that increases the sampling rate of signals such as speech or music using deep convolutional neural net…
Oct 20, 2016
By dlp
Conferences: ICLR
Machine Learning

Revisiting Classifier Two-Sample Tests

The goal of two-sample tests is to assess whether two samples, $S_P \sim P^n$ and $S_Q \sim Q^m$, are drawn from the same distribution. Perhaps intri…
Sep 12, 2014
Conferences: NIPS
Computational Complexity
Artificial Intelligence
Information Theory

We consider the problem of learning the canonical parameters specifying an undirected graphical model (Markov random field) from the mean parameters. For graphical models representing a minimal exponential family, the canonical parameters are unique…

Hardness of parameter estimation in graphical models

We consider the problem of learning the canonical parameters specifying an undirected graphical model (Markov random field) from the mean parameters.…
Sep 20, 2017
Conferences: ICCV
Computer Vision

SegFlow: Joint Learning for Video Object Segmentation and Optical Flow

This paper proposes an end-to-end trainable network, SegFlow, for simultaneously predicting pixel-wise object segmentation and optical flow in videos…
Apr 17, 2013
By yxchen
Conferences: ICML
Information Theory
Machine Learning
Numerical Analysis

Spectral Compressed Sensing via Structured Matrix Completion

The paper studies the problem of recovering a spectrally sparse object from a small number of time domain samples. Specifically, the object of intere…
Dec 06, 2017
By wangyue
Conferences: ICRA
Robotics
Machine Learning

LocNet: Global localization in 3D point clouds for mobile robots

Global localization in 3D point clouds is a challenging problem of estimating the pose of robots without priori knowledge. In this paper, a solution …
Oct 10, 2016
Conferences: ICLR
Machine Learning
Computation and Language

We presented the Latent Sequence Decompositions (LSD) framework. LSD allows us to learn decompositions of sequences that are a function of both the input and output sequence. We presented a biased training algorithm based on sampling valid extension…

Latent Sequence Decompositions

We present the Latent Sequence Decompositions (LSD) framework. LSD decomposes sequences with variable lengthed output units as a function of both the…
Oct 28, 2011
By tim
Conferences: NIPS
Machine Learning

Adaptive Hedge

Most methods for decision-theoretic online learning are based on the Hedge algorithm, which takes a parameter called the learning rate. In most previ…
May 07, 2017
Conferences: ICLR
Machine Learning
Artificial Intelligence

Metacontrol for Adaptive Imagination-Based Optimization

Many machine learning systems are built to solve the hardest examples of a particular task, which often makes them large and expensive to run---espec…
Jul 25, 2016
Conferences: ECCV
Computer Vision

Automatic Attribute Discovery with Neural Activations

How can a machine learn to recognize visual attributes emerging out of online community without a definitive supervised dataset? This paper proposes …
May 07, 2017
Conferences: NIPS
Artificial Intelligence
Machine Learning

Experimental results : Reinforcement Learning of POMDPs using Spectra…

We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods.…
Mar 06, 2017
Conferences: CVPR
Computer Vision

Diversified Texture Synthesis with Feed-forward Networks

Recent progresses on deep discriminative and generative modeling have shown promising results on texture synthesis. However, existing feed-forward ba…
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