pwc-archive/papers-with-abstracts huggingface dataset:
Autonomous driving challenge: To Infer the property of a dynamic object based on its motion pattern using recurrent neural network
Authors: Mona Fathollahi, Rangachar Kasturi
Date: 2016-09-01 00:00:00
Tasks: Autonomous Driving, Object

In autonomous driving applications a critical challenge is to identify action
to take to avoid an obstacle on collision course. For example, when a heavy
object is suddenly encountered it is critical to stop the vehicle or change the
lane even if it causes other traffic disruptions. However,there are situations
when it is preferable to collide with the object rather than take an action
that would result in a much more serious accident than collision with the
object. For example, a heavy object which falls from a truck should be avoided
whereas a bouncing ball or a soft target such as a foam box need not be.We
present a novel method to discriminate between the motion characteristics of
these types of objects based on their physical properties such as bounciness,
elasticity, etc.In this preliminary work, we use recurrent neural net-work with
LSTM cells to train a classifier to classify objects based on their motion
trajectories. We test the algorithm on synthetic data, and, as a proof of
concept, demonstrate its effectiveness on a limited set of real-world data.
An Integrated Classification Model for Financial Data Mining
Authors: Fan Cai, Nhien-An Le-Khac, M-T. Kechadi
Date: 2016-09-09 00:00:00
Tasks: Classification, General Classification, model

Nowadays, financial data analysis is becoming increasingly important in the
business market. As companies collect more and more data from daily operations,
they expect to extract useful knowledge from existing collected data to help
make reasonable decisions for new customer requests, e.g. user credit category,
churn analysis, real estate analysis, etc. Financial institutes have applied
different data mining techniques to enhance their business performance.
However, simple ap-proach of these techniques could raise a performance issue.
Besides, there are very few general models for both understanding and
forecasting different finan-cial fields. We present in this paper a new
classification model for analyzing fi-nancial data. We also evaluate this model
with different real-world data to show its performance.
Learning-Based View Synthesis for Light Field Cameras
Authors: Nima Khademi Kalantari, Ting-Chun Wang, Ravi Ramamoorthi
Date: 2016-09-09 00:00:00

With the introduction of consumer light field cameras, light field imaging
has recently become widespread. However, there is an inherent trade-off between
the angular and spatial resolution, and thus, these cameras often sparsely
sample in either spatial or angular domain. In this paper, we use machine
learning to mitigate this trade-off. Specifically, we propose a novel
learning-based approach to synthesize new views from a sparse set of input
views. We build upon existing view synthesis techniques and break down the
process into disparity and color estimation components. We use two sequential
convolutional neural networks to model these two components and train both
networks simultaneously by minimizing the error between the synthesized and
ground truth images. We show the performance of our approach using only four
corner sub-aperture views from the light fields captured by the Lytro Illum
camera. Experimental results show that our approach synthesizes high-quality
images that are superior to the state-of-the-art techniques on a variety of
challenging real-world scenes. We believe our method could potentially decrease
the required angular resolution of consumer light field cameras, which allows
their spatial resolution to increase.
A Large Scale Corpus of Gulf Arabic
Authors: Salam Khalifa, Nizar Habash, Dana Abdulrahim, Sara Hassan
Date: 2016-09-09 00:00:00
Proceeding: LREC 2016 5

Most Arabic natural language processing tools and resources are developed to
serve Modern Standard Arabic (MSA), which is the official written language in
the Arab World. Some Dialectal Arabic varieties, notably Egyptian Arabic, have
received some attention lately and have a growing collection of resources that
include annotated corpora and morphological analyzers and taggers. Gulf Arabic,
however, lags behind in that respect. In this paper, we present the Gumar
Corpus, a large-scale corpus of Gulf Arabic consisting of 110 million words
from 1,200 forum novels. We annotate the corpus for sub-dialect information at
the document level. We also present results of a preliminary study in the
morphological annotation of Gulf Arabic which includes developing guidelines
for a conventional orthography. The text of the corpus is publicly browsable
through a web interface we developed for it.
The Role of Context Selection in Object Detection
Authors: Ruichi Yu, Xi Chen, Vlad I. Morariu, Larry S. Davis
Date: 2016-09-09 00:00:00
Tasks: Object, object-detection, Object Detection

We investigate the reasons why context in object detection has limited
utility by isolating and evaluating the predictive power of different context
cues under ideal conditions in which context provided by an oracle. Based on
this study, we propose a region-based context re-scoring method with dynamic
context selection to remove noise and emphasize informative context. We
introduce latent indicator variables to select (or ignore) potential contextual
regions, and learn the selection strategy with latent-SVM. We conduct
experiments to evaluate the performance of the proposed context selection
method on the SUN RGB-D dataset. The method achieves a significant improvement
in terms of mean average precision (mAP), compared with both appearance based
detectors and a conventional context model without the selection scheme.
VConv-DAE: Deep Volumetric Shape Learning Without Object Labels
Authors: Abhishek Sharma, Oliver Grau, Mario Fritz
Date: 2016-04-13 00:00:00
Tasks: Denoising, Object

With the advent of affordable depth sensors, 3D capture becomes more and more
ubiquitous and already has made its way into commercial products. Yet,
capturing the geometry or complete shapes of everyday objects using scanning
devices (e.g. Kinect) still comes with several challenges that result in noise
or even incomplete shapes. Recent success in deep learning has shown how to
learn complex shape distributions in a data-driven way from large scale 3D CAD
Model collections and to utilize them for 3D processing on volumetric
representations and thereby circumventing problems of topology and
tessellation. Prior work has shown encouraging results on problems ranging from
shape completion to recognition. We provide an analysis of such approaches and
discover that training as well as the resulting representation are strongly and
unnecessarily tied to the notion of object labels. Thus, we propose a full
convolutional volumetric auto encoder that learns volumetric representation
from noisy data by estimating the voxel occupancy grids. The proposed method
outperforms prior work on challenging tasks like denoising and shape
completion. We also show that the obtained deep embedding gives competitive
performance when used for classification and promising results for shape
interpolation.
Extract fetal ECG from single-lead abdominal ECG by de-shape short time Fourier transform and nonlocal median
Authors: Su Li, Hau-Tieng Wu
Date: 2016-09-09 00:00:00
Tasks: Beat Tracking

The multiple fundamental frequency detection problem and the source
separation problem from a single-channel signal containing multiple oscillatory
components and a nonstationary noise are both challenging tasks. To extract the
fetal electrocardiogram (ECG) from a single-lead maternal abdominal ECG, we
face both challenges. In this paper, we propose a novel method to extract the
fetal ECG signal from the single channel maternal abdominal ECG signal, without
any additional measurement. The algorithm is composed of three main
ingredients. First, the maternal and fetal heart rates are estimated by the
de-shape short time Fourier transform, which is a recently proposed nonlinear
time-frequency analysis technique; second, the beat tracking technique is
applied to accurately obtain the maternal and fetal R peaks; third, the
maternal and fetal ECG waveforms are established by the nonlocal median. The
algorithm is evaluated on a simulated fetal ECG signal database ({\em fecgsyn}
database), and tested on two real databases with the annotation provided by
experts ({\em adfecgdb} database and {\em CinC2013} database). In general, the
algorithm could be applied to solve other detection and source separation
problems, and reconstruct the time-varying wave-shape function of each
oscillatory component.
Image Denoising Via Collaborative Support-Agnostic Recovery
Authors: Muzammil Behzad, Mudassir Masood, Tarig Ballal, Maha Shadaydeh, Tareq Y. Al-Naffouri
Date: 2016-09-09 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration, SSIM

In this paper, we propose a novel image denoising algorithm using
collaborative support-agnostic sparse reconstruction. An observed image is
first divided into patches. Similarly structured patches are grouped together
to be utilized for collaborative processing. In the proposed collaborative
schemes, similar patches are assumed to share the same support taps. For sparse
reconstruction, the likelihood of a tap being active in a patch is computed and
refined through a collaboration process with other similar patches in the same
group. This provides very good patch support estimation, hence enhancing the
quality of image restoration. Performance comparisons with state-of-the-art
algorithms, in terms of SSIM and PSNR, demonstrate the superiority of the
proposed algorithm.
Towards Transparent AI Systems: Interpreting Visual Question Answering Models
Authors: Yash Goyal, Akrit Mohapatra, Devi Parikh, Dhruv Batra
Date: 2016-08-31 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Deep neural networks have shown striking progress and obtained
state-of-the-art results in many AI research fields in the recent years.
However, it is often unsatisfying to not know why they predict what they do. In
this paper, we address the problem of interpreting Visual Question Answering
(VQA) models. Specifically, we are interested in finding what part of the input
(pixels in images or words in questions) the VQA model focuses on while
answering the question. To tackle this problem, we use two visualization
techniques -- guided backpropagation and occlusion -- to find important words
in the question and important regions in the image. We then present qualitative
and quantitative analyses of these importance maps. We found that even without
explicit attention mechanisms, VQA models may sometimes be implicitly attending
to relevant regions in the image, and often to appropriate words in the
question.
Robust Spectral Detection of Global Structures in the Data by Learning a Regularization
Authors: Pan Zhang
Date: 2016-09-09 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Clustering, Community Detection, Matrix Completion

Spectral methods are popular in detecting global structures in the given data
that can be represented as a matrix. However when the data matrix is sparse or
noisy, classic spectral methods usually fail to work, due to localization of
eigenvectors (or singular vectors) induced by the sparsity or noise. In this
work, we propose a general method to solve the localization problem by learning
a regularization matrix from the localized eigenvectors. Using matrix
perturbation analysis, we demonstrate that the learned regularizations suppress
down the eigenvalues associated with localized eigenvectors and enable us to
recover the informative eigenvectors representing the global structure. We show
applications of our method in several inference problems: community detection
in networks, clustering from pairwise similarities, rank estimation and matrix
completion problems. Using extensive experiments, we illustrate that our method
solves the localization problem and works down to the theoretical detectability
limits in different kinds of synthetic data. This is in contrast with existing
spectral algorithms based on data matrix, non-backtracking matrix, Laplacians
and those with rank-one regularizations, which perform poorly in the sparse
case with noise.
Ask the GRU: Multi-Task Learning for Deep Text Recommendations
Authors: Trapit Bansal, David Belanger, Andrew McCallum
Date: 2016-09-07 00:00:00
Tasks: Articles, Collaborative Filtering, Multi-Task Learning, Topic Models, Word Embeddings

In a variety of application domains the content to be recommended to users is
associated with text. This includes research papers, movies with associated
plot summaries, news articles, blog posts, etc. Recommendation approaches based
on latent factor models can be extended naturally to leverage text by employing
an explicit mapping from text to factors. This enables recommendations for new,
unseen content, and may generalize better, since the factors for all items are
produced by a compactly-parametrized model. Previous work has used topic models
or averages of word embeddings for this mapping. In this paper we present a
method leveraging deep recurrent neural networks to encode the text sequence
into a latent vector, specifically gated recurrent units (GRUs) trained
end-to-end on the collaborative filtering task. For the task of scientific
paper recommendation, this yields models with significantly higher accuracy. In
cold-start scenarios, we beat the previous state-of-the-art, all of which
ignore word order. Performance is further improved by multi-task learning,
where the text encoder network is trained for a combination of content
recommendation and item metadata prediction. This regularizes the collaborative
filtering model, ameliorating the problem of sparsity of the observed rating
matrix.
Ashwin: Plug-and-Play System for Machine-Human Image Annotation
Authors: Anand Sriraman, Mandar Kulkarni, Rahul Kumar, Kanika Kalra, Purushotam Radadia, Shirish Karande
Date: 2016-09-08 00:00:00

We present an end-to-end machine-human image annotation system where each
component can be attached in a plug-and-play fashion. These components include
Feature Extraction, Machine Classifier, Task Sampling and Crowd Consensus.
High-Dimensional Continuous Control Using Generalized Advantage Estimation
Authors: John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, Pieter Abbeel
Date: 2015-06-08 00:00:00
Tasks: continuous-control, Continuous Control, Policy Gradient Methods, Reinforcement Learning, Vocal Bursts Intensity Prediction

Policy gradient methods are an appealing approach in reinforcement learning
because they directly optimize the cumulative reward and can straightforwardly
be used with nonlinear function approximators such as neural networks. The two
main challenges are the large number of samples typically required, and the
difficulty of obtaining stable and steady improvement despite the
nonstationarity of the incoming data. We address the first challenge by using
value functions to substantially reduce the variance of policy gradient
estimates at the cost of some bias, with an exponentially-weighted estimator of
the advantage function that is analogous to TD(lambda). We address the second
challenge by using trust region optimization procedure for both the policy and
the value function, which are represented by neural networks.
  Our approach yields strong empirical results on highly challenging 3D
locomotion tasks, learning running gaits for bipedal and quadrupedal simulated
robots, and learning a policy for getting the biped to stand up from starting
out lying on the ground. In contrast to a body of prior work that uses
hand-crafted policy representations, our neural network policies map directly
from raw kinematics to joint torques. Our algorithm is fully model-free, and
the amount of simulated experience required for the learning tasks on 3D bipeds
corresponds to 1-2 weeks of real time.
Component-Based Distributed Framework for Coherent and Real-Time Video Dehazing
Authors: Meihua Wang, Jiaming Mai, Yun Liang, Tom Z. J. Fu, Zhenjie Zhang, Ruichu Cai
Date: 2016-09-07 00:00:00
Tasks: CPU, Decision Making, Robot Navigation

Traditional dehazing techniques, as a well studied topic in image processing,
are now widely used to eliminate the haze effects from individual images.
However, even the state-of-the-art dehazing algorithms may not provide
sufficient support to video analytics, as a crucial pre-processing step for
video-based decision making systems (e.g., robot navigation), due to the
limitations of these algorithms on poor result coherence and low processing
efficiency. This paper presents a new framework, particularly designed for
video dehazing, to output coherent results in real time, with two novel
techniques. Firstly, we decompose the dehazing algorithms into three generic
components, namely transmission map estimator, atmospheric light estimator and
haze-free image generator. They can be simultaneously processed by multiple
threads in the distributed system, such that the processing efficiency is
optimized by automatic CPU resource allocation based on the workloads.
Secondly, a cross-frame normalization scheme is proposed to enhance the
coherence among consecutive frames, by sharing the parameters of atmospheric
light from consecutive frames in the distributed computation platform. The
combination of these techniques enables our framework to generate highly
consistent and accurate dehazing results in real-time, by using only 3 PCs
connected by Ethernet.
Dialogue manager domain adaptation using Gaussian process reinforcement learning
Authors: Milica Gasic, Nikola Mrksic, Lina M. Rojas-Barahona, Pei-Hao Su, Stefan Ultes, David Vandyke, Tsung-Hsien Wen, Steve Young
Date: 2016-09-09 00:00:00
Tasks: Domain Adaptation, Gaussian Processes, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Spoken Dialogue Systems

Spoken dialogue systems allow humans to interact with machines using natural
speech. As such, they have many benefits. By using speech as the primary
communication medium, a computer interface can facilitate swift, human-like
acquisition of information. In recent years, speech interfaces have become ever
more popular, as is evident from the rise of personal assistants such as Siri,
Google Now, Cortana and Amazon Alexa. Recently, data-driven machine learning
methods have been applied to dialogue modelling and the results achieved for
limited-domain applications are comparable to or outperform traditional
approaches. Methods based on Gaussian processes are particularly effective as
they enable good models to be estimated from limited training data.
Furthermore, they provide an explicit estimate of the uncertainty which is
particularly useful for reinforcement learning. This article explores the
additional steps that are necessary to extend these methods to model multiple
dialogue domains. We show that Gaussian process reinforcement learning is an
elegant framework that naturally supports a range of methods, including prior
knowledge, Bayesian committee machines and multi-agent learning, for
facilitating extensible and adaptable dialogue systems.
Distributed Online Optimization in Dynamic Environments Using Mirror Descent
Authors: Shahin Shahrampour, Ali Jadbabaie
Date: 2016-09-09 00:00:00
Tasks: Distributed Optimization

This work addresses decentralized online optimization in non-stationary
environments. A network of agents aim to track the minimizer of a global
time-varying convex function. The minimizer evolves according to a known
dynamics corrupted by an unknown, unstructured noise. At each time, the global
function can be cast as a sum of a finite number of local functions, each of
which is assigned to one agent in the network. Moreover, the local functions
become available to agents sequentially, and agents do not have a prior
knowledge of the future cost functions. Therefore, agents must communicate with
each other to build an online approximation of the global function. We propose
a decentralized variation of the celebrated Mirror Descent, developed by
Nemirovksi and Yudin. Using the notion of Bregman divergence in lieu of
Euclidean distance for projection, Mirror Descent has been shown to be a
powerful tool in large-scale optimization. Our algorithm builds on Mirror
Descent, while ensuring that agents perform a consensus step to follow the
global function and take into account the dynamics of the global minimizer. To
measure the performance of the proposed online algorithm, we compare it to its
offline counterpart, where the global functions are available a priori. The gap
between the two is called dynamic regret. We establish a regret bound that
scales inversely in the spectral gap of the network, and more notably it
represents the deviation of minimizer sequence with respect to the given
dynamics. We then show that our results subsume a number of results in
distributed optimization. We demonstrate the application of our method to
decentralized tracking of dynamic parameters and verify the results via
numerical experiments.
Reconstructing Articulated Rigged Models from RGB-D Videos
Authors: Dimitrios Tzionas, Juergen Gall
Date: 2016-09-06 00:00:00
Tasks: Clustering, Motion Segmentation, Object

Although commercial and open-source software exist to reconstruct a static
object from a sequence recorded with an RGB-D sensor, there is a lack of tools
that build rigged models of articulated objects that deform realistically and
can be used for tracking or animation. In this work, we fill this gap and
propose a method that creates a fully rigged model of an articulated object
from depth data of a single sensor. To this end, we combine deformable mesh
tracking, motion segmentation based on spectral clustering and skeletonization
based on mean curvature flow. The fully rigged model then consists of a
watertight mesh, embedded skeleton, and skinning weights.
Track Facial Points in Unconstrained Videos
Authors: Xi Peng, Qiong Hu, Junzhou Huang, Dimitris N. Metaxas
Date: 2016-09-09 00:00:00
Tasks: Incremental Learning, valid

Tracking Facial Points in unconstrained videos is challenging due to the
non-rigid deformation that changes over time. In this paper, we propose to
exploit incremental learning for person-specific alignment in wild conditions.
Our approach takes advantage of part-based representation and cascade
regression for robust and efficient alignment on each frame. Unlike existing
methods that usually rely on models trained offline, we incrementally update
the representation subspace and the cascade of regressors in a unified
framework to achieve personalized modeling on the fly. To alleviate the
drifting issue, the fitting results are evaluated using a deep neural network,
where well-aligned faces are picked out to incrementally update the
representation and fitting models. Both image and video datasets are employed
to valid the proposed method. The results demonstrate the superior performance
of our approach compared with existing approaches in terms of fitting accuracy
and efficiency.