pwc-archive/papers-with-abstracts huggingface dataset:
Projected Nesterov's Proximal-Gradient Algorithm for Sparse Signal Reconstruction with a Convex Constraint
Authors: Renliang Gu, Aleksandar Dogandžić
Date: 2015-02-09 00:00:00
Tasks: compressed sensing

We develop a projected Nesterov's proximal-gradient (PNPG) approach for
sparse signal reconstruction that combines adaptive step size with Nesterov's
momentum acceleration. The objective function that we wish to minimize is the
sum of a convex differentiable data-fidelity (negative log-likelihood (NLL))
term and a convex regularization term. We apply sparse signal regularization
where the signal belongs to a closed convex set within the closure of the
domain of the NLL; the convex-set constraint facilitates flexible NLL domains
and accurate signal recovery. Signal sparsity is imposed using the
$\ell_1$-norm penalty on the signal's linear transform coefficients or gradient
map, respectively. The PNPG approach employs projected Nesterov's acceleration
step with restart and an inner iteration to compute the proximal mapping. We
propose an adaptive step-size selection scheme to obtain a good local
majorizing function of the NLL and reduce the time spent backtracking. Thanks
to step-size adaptation, PNPG does not require Lipschitz continuity of the
gradient of the NLL. We present an integrated derivation of the momentum
acceleration and its $\mathcal{O}(k^{-2})$ convergence-rate and iterate
convergence proofs, which account for adaptive step-size selection, inexactness
of the iterative proximal mapping, and the convex-set constraint. The tuning of
PNPG is largely application-independent. Tomographic and compressed-sensing
reconstruction experiments with Poisson generalized linear and Gaussian linear
measurement models demonstrate the performance of the proposed approach.
Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?
Authors: Christophe Servan, Alexandre Berard, Zied Elloumi, Hervé Blanchon, Laurent Besacier
Date: 2016-10-05 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation, Translation

This paper presents an approach combining lexico-semantic resources and
distributed representations of words applied to the evaluation in machine
translation (MT). This study is made through the enrichment of a well-known MT
evaluation metric: METEOR. This metric enables an approximate match (synonymy
or morphological similarity) between an automatic and a reference translation.
Our experiments are made in the framework of the Metrics task of WMT 2014. We
show that distributed representations are a good alternative to lexico-semantic
resources for MT evaluation and they can even bring interesting additional
information. The augmented versions of METEOR, using vector representations,
are made available on our Github page.
Deep Residual Networks with Exponential Linear Unit
Authors: Anish Shah, Eashan Kadam, Hena Shah, Sameer Shinde, Sandip Shingade
Date: 2016-04-14 00:00:00
Tasks: Image Classification

Very deep convolutional neural networks introduced new problems like
vanishing gradient and degradation. The recent successful contributions towards
solving these problems are Residual and Highway Networks. These networks
introduce skip connections that allow the information (from the input or those
learned in earlier layers) to flow more into the deeper layers. These very deep
models have lead to a considerable decrease in test errors, on benchmarks like
ImageNet and COCO. In this paper, we propose the use of exponential linear unit
instead of the combination of ReLU and Batch Normalization in Residual
Networks. We show that this not only speeds up learning in Residual Networks
but also improves the accuracy as the depth increases. It improves the test
error on almost all data sets, like CIFAR-10 and CIFAR-100
An ensemble learning method for scene classification based on Hidden Markov Model image representation
Authors: Fariborz Taherkhani, Reza Hedayati
Date: 2016-07-22 00:00:00
Tasks: Ensemble Learning, General Classification, Scene Classification

Low level images representation in feature space performs poorly for
classification with high accuracy since this level of representation is not
able to project images into the discriminative feature space. In this work, we
propose an efficient image representation model for classification. First we
apply Hidden Markov Model (HMM) on ordered grids represented by different type
of image descriptors in order to include causality of local properties existing
in image for feature extraction and then we train up a separate classifier for
each of these features sets. Finally we ensemble these classifiers efficiently
in a way that they can cancel out each other errors for obtaining higher
accuracy. This method is evaluated on 15 natural scene dataset. Experimental
results show the superiority of the proposed method in comparison to some
current existing methods
A probabilistic patch based image representation using Conditional Random Field model for image classification
Authors: Fariborz Taherkhani
Date: 2016-07-22 00:00:00
Tasks: Classification, Face Recognition, General Classification, image-classification, Image Classification, Scene Classification, Texture Classification

In this paper we proposed an ordered patch based method using Conditional
Random Field (CRF) in order to encode local properties and their spatial
relationship in images to address texture classification, face recognition, and
scene classification problems. Typical image classification approaches work
without considering spatial causality among distinctive properties of an image
for image representation in feature space. In this method first, each image is
encoded as a sequence of ordered patches, including local properties. Second,
the sequence of these ordered patches is modeled as a probabilistic feature
vector by CRF to model spatial relationship of these local properties. And
finally, image classification is performed on such probabilistic image
representation. Experimental results on several standard image datasets
indicate that proposed method outperforms some of existing image classification
methods.
Active Sensing of Social Networks
Authors: Hoi-To Wai, Anna Scaglione, Amir Leshem
Date: 2016-01-21 00:00:00
Tasks: compressed sensing

This paper develops an active sensing method to estimate the relative weight
(or trust) agents place on their neighbors' information in a social network.
The model used for the regression is based on the steady state equation in the
linear DeGroot model under the influence of stubborn agents, i.e., agents whose
opinions are not influenced by their neighbors. This method can be viewed as a
\emph{social RADAR}, where the stubborn agents excite the system and the latter
can be estimated through the reverberation observed from the analysis of the
agents' opinions. The social network sensing problem can be interpreted as a
blind compressed sensing problem with a sparse measurement matrix. We prove
that the network structure will be revealed when a sufficient number of
stubborn agents independently influence a number of ordinary (non-stubborn)
agents. We investigate the scenario with a deterministic or randomized DeGroot
model and propose a consistent estimator of the steady states for the latter
scenario. Simulation results on synthetic and real world networks support our
findings.
Incorporating Discrete Translation Lexicons into Neural Machine Translation
Authors: Philip Arthur, Graham Neubig, Satoshi Nakamura
Date: 2016-06-07 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Machine Translation, NMT, Sentence, Translation

Neural machine translation (NMT) often makes mistakes in translating
low-frequency content words that are essential to understanding the meaning of
the sentence. We propose a method to alleviate this problem by augmenting NMT
systems with discrete translation lexicons that efficiently encode translations
of these low-frequency words. We describe a method to calculate the lexicon
probability of the next word in the translation candidate by using the
attention vector of the NMT model to select which source word lexical
probabilities the model should focus on. We test two methods to combine this
probability with the standard NMT probability: (1) using it as a bias, and (2)
linear interpolation. Experiments on two corpora show an improvement of 2.0-2.3
BLEU and 0.13-0.44 NIST score, and faster convergence time.
ECAT: Event Capture Annotation Tool
Authors: Tuan Do, Nikhil Krishnaswamy, James Pustejovsky
Date: 2016-10-05 00:00:00
Tasks: Attribute

This paper introduces the Event Capture Annotation Tool (ECAT), a
user-friendly, open-source interface tool for annotating events and their
participants in video, capable of extracting the 3D positions and orientations
of objects in video captured by Microsoft's Kinect(R) hardware. The modeling
language VoxML (Pustejovsky and Krishnaswamy, 2016) underlies ECAT's object,
program, and attribute representations, although ECAT uses its own spec for
explicit labeling of motion instances. The demonstration will show the tool's
workflow and the options available for capturing event-participant relations
and browsing visual data. Mapping ECAT's output to VoxML will also be
addressed.
A Low Complexity Algorithm with $O(\sqrt{T})$ Regret and $O(1)$ Constraint Violations for Online Convex Optimization with Long Term Constraints
Authors: Hao Yu, Michael J. Neely
Date: 2016-04-08 00:00:00

This paper considers online convex optimization over a complicated constraint set, which typically consists of multiple functional constraints and a set constraint. The conventional online projection algorithm (Zinkevich, 2003) can be difficult to implement due to the potentially high computation complexity of the projection operation. In this paper, we relax the functional constraints by allowing them to be violated at each round but still requiring them to be satisfied in the long term. This type of relaxed online convex optimization (with long term constraints) was first considered in Mahdavi et al. (2012). That prior work proposes an algorithm to achieve $O(\sqrt{T})$ regret and $O(T^{3/4})$ constraint violations for general problems and another algorithm to achieve an $O(T^{2/3})$ bound for both regret and constraint violations when the constraint set can be described by a finite number of linear constraints. A recent extension in \citet{Jenatton16ICML} can achieve $O(T^{\max\{\theta,1-\theta\}})$ regret and $O(T^{1-\theta/2})$ constraint violations where $\theta\in (0,1)$. The current paper proposes a new simple algorithm that yields improved performance in comparison to prior works. The new algorithm achieves an $O(\sqrt{T})$ regret bound with $O(1)$ constraint violations.
Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models
Authors: Marc Niethammer, Kilian M. Pohl, Firdaus Janoos, William M. Wells III
Date: 2015-01-22 00:00:00
Tasks: Denoising, Image Denoising, Image Segmentation, Segmentation, Semantic Segmentation

Segmentation is a fundamental task for extracting semantically meaningful
regions from an image. The goal of segmentation algorithms is to accurately
assign object labels to each image location. However, image-noise, shortcomings
of algorithms, and image ambiguities cause uncertainty in label assignment.
Estimating the uncertainty in label assignment is important in multiple
application domains, such as segmenting tumors from medical images for
radiation treatment planning. One way to estimate these uncertainties is
through the computation of posteriors of Bayesian models, which is
computationally prohibitive for many practical applications. On the other hand,
most computationally efficient methods fail to estimate label uncertainty. We
therefore propose in this paper the Active Mean Fields (AMF) approach, a
technique based on Bayesian modeling that uses a mean-field approximation to
efficiently compute a segmentation and its corresponding uncertainty. Based on
a variational formulation, the resulting convex model combines any
label-likelihood measure with a prior on the length of the segmentation
boundary. A specific implementation of that model is the Chan-Vese segmentation
model (CV), in which the binary segmentation task is defined by a Gaussian
likelihood and a prior regularizing the length of the segmentation boundary.
Furthermore, the Euler-Lagrange equations derived from the AMF model are
equivalent to those of the popular Rudin-Osher-Fatemi (ROF) model for image
denoising. Solutions to the AMF model can thus be implemented by directly
utilizing highly-efficient ROF solvers on log-likelihood ratio fields. We
qualitatively assess the approach on synthetic data as well as on real natural
and medical images. For a quantitative evaluation, we apply our approach to the
icgbench dataset.
Feature Learning from Spectrograms for Assessment of Personality Traits
Authors: Marc-André Carbonneau, Eric Granger, Yazid Attabi, Ghyslain Gagnon
Date: 2016-10-04 00:00:00

Several methods have recently been proposed to analyze speech and
automatically infer the personality of the speaker. These methods often rely on
prosodic and other hand crafted speech processing features extracted with
off-the-shelf toolboxes. To achieve high accuracy, numerous features are
typically extracted using complex and highly parameterized algorithms. In this
paper, a new method based on feature learning and spectrogram analysis is
proposed to simplify the feature extraction process while maintaining a high
level of accuracy. The proposed method learns a dictionary of discriminant
features from patches extracted in the spectrogram representations of training
speech segments. Each speech segment is then encoded using the dictionary, and
the resulting feature set is used to perform classification of personality
traits. Experiments indicate that the proposed method achieves state-of-the-art
results with a significant reduction in complexity when compared to the most
recent reference methods. The number of features, and difficulties linked to
the feature extraction process are greatly reduced as only one type of
descriptors is used, for which the 6 parameters can be tuned automatically. In
contrast, the simplest reference method uses 4 types of descriptors to which 6
functionals are applied, resulting in over 20 parameters to be tuned.
Seer: Empowering Software Defined Networking with Data Analytics
Authors: Kyriakos Sideris, Reza Nejabati, Dimitra Simeonidou
Date: 2016-10-04 00:00:00

Network complexity is increasing, making network control and orchestration a
challenging task. The proliferation of network information and tools for data
analytics can provide an important insight into resource provisioning and
optimisation. The network knowledge incorporated in software defined networking
can facilitate the knowledge driven control, leveraging the network
programmability. We present Seer: a flexible, highly configurable data
analytics platform for network intelligence based on software defined
networking and big data principles. Seer combines a computational engine with a
distributed messaging system to provide a scalable, fault tolerant and
real-time platform for knowledge extraction. Our first prototype uses Apache
Spark for streaming analytics and open network operating system (ONOS)
controller to program a network in real-time. The first application we
developed aims to predict the mobility pattern of mobile devices inside a smart
city environment.
RAISR: Rapid and Accurate Image Super Resolution
Authors: Yaniv Romano, John Isidoro, Peyman Milanfar
Date: 2016-06-03 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Given an image, we wish to produce an image of larger size with significantly
more pixels and higher image quality. This is generally known as the Single
Image Super-Resolution (SISR) problem. The idea is that with sufficient
training data (corresponding pairs of low and high resolution images) we can
learn set of filters (i.e. a mapping) that when applied to given image that is
not in the training set, will produce a higher resolution version of it, where
the learning is preferably low complexity. In our proposed approach, the
run-time is more than one to two orders of magnitude faster than the best
competing methods currently available, while producing results comparable or
better than state-of-the-art.
  A closely related topic is image sharpening and contrast enhancement, i.e.,
improving the visual quality of a blurry image by amplifying the underlying
details (a wide range of frequencies). Our approach additionally includes an
extremely efficient way to produce an image that is significantly sharper than
the input blurry one, without introducing artifacts such as halos and noise
amplification. We illustrate how this effective sharpening algorithm, in
addition to being of independent interest, can be used as a pre-processing step
to induce the learning of more effective upscaling filters with built-in
sharpening and contrast enhancement effect.
Single Image 3D Interpreter Network
Authors: Jiajun Wu, Tianfan Xue, Joseph J. Lim, Yuandong Tian, Joshua B. Tenenbaum, Antonio Torralba, William T. Freeman
Date: 2016-04-29 00:00:00
Tasks: Image Retrieval, Keypoint Estimation, Object, Retrieval

Understanding 3D object structure from a single image is an important but
difficult task in computer vision, mostly due to the lack of 3D object
annotations in real images. Previous work tackles this problem by either
solving an optimization task given 2D keypoint positions, or training on
synthetic data with ground truth 3D information. In this work, we propose 3D
INterpreter Network (3D-INN), an end-to-end framework which sequentially
estimates 2D keypoint heatmaps and 3D object structure, trained on both real
2D-annotated images and synthetic 3D data. This is made possible mainly by two
technical innovations. First, we propose a Projection Layer, which projects
estimated 3D structure to 2D space, so that 3D-INN can be trained to predict 3D
structural parameters supervised by 2D annotations on real images. Second,
heatmaps of keypoints serve as an intermediate representation connecting real
and synthetic data, enabling 3D-INN to benefit from the variation and abundance
of synthetic 3D objects, without suffering from the difference between the
statistics of real and synthesized images due to imperfect rendering. The
network achieves state-of-the-art performance on both 2D keypoint estimation
and 3D structure recovery. We also show that the recovered 3D information can
be used in other vision applications, such as 3D rendering and image retrieval.
FLOCK: Combating Astroturfing on Livestreaming Platforms
Authors: Neil Shah
Date: 2016-10-04 00:00:00

Livestreaming platforms have become increasingly popular in recent years as a
means of sharing and advertising creative content. Popular content streamers
who attract large viewership to their live broadcasts can earn a living by
means of ad revenue, donations and channel subscriptions. Unfortunately, this
incentivized popularity has simultaneously resulted in incentive for fraudsters
to provide services to astroturf, or artificially inflate viewership metrics by
providing fake "live" views to customers. Our work provides a number of major
contributions: (a) formulation: we are the first to introduce and characterize
the viewbot fraud problem in livestreaming platforms, (b) methodology: we
propose FLOCK, a principled and unsupervised method which efficiently and
effectively identifies botted broadcasts and their constituent botted views,
and (c) practicality: our approach achieves over 98% precision in identifying
botted broadcasts and over 90% precision/recall against sizable synthetically
generated viewbot attacks on a real-world livestreaming workload of over 16
million views and 92 thousand broadcasts. FLOCK successfully operates on larger
datasets in practice and is regularly used at a large, undisclosed
livestreaming corporation.
Towards the Design of Prospect-Theory based Human Decision Rules for Hypothesis Testing
Authors: V. Sriram Siddhardh Nadendla, Swastik Brahma, Pramod K. Varshney
Date: 2016-10-04 00:00:00
Tasks: Two-sample testing

Detection rules have traditionally been designed for rational agents that
minimize the Bayes risk (average decision cost). With the advent of
crowd-sensing systems, there is a need to redesign binary hypothesis testing
rules for behavioral agents, whose cognitive behavior is not captured by
traditional utility functions such as Bayes risk. In this paper, we adopt
prospect theory based models for decision makers. We consider special agent
models namely optimists and pessimists in this paper, and derive optimal
detection rules under different scenarios. Using an illustrative example, we
also show how the decision rule of a human agent deviates from the Bayesian
decision rule under various behavioral models, considered in this paper.
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Authors: Yarin Gal, Zoubin Ghahramani
Date: 2015-06-06 00:00:00
Tasks: Bayesian Inference, Deep Reinforcement Learning, Gaussian Processes, General Classification, regression, Reinforcement Learning, Uncertainty Quantification

Deep learning tools have gained tremendous attention in applied machine
learning. However such tools for regression and classification do not capture
model uncertainty. In comparison, Bayesian models offer a mathematically
grounded framework to reason about model uncertainty, but usually come with a
prohibitive computational cost. In this paper we develop a new theoretical
framework casting dropout training in deep neural networks (NNs) as approximate
Bayesian inference in deep Gaussian processes. A direct result of this theory
gives us tools to model uncertainty with dropout NNs -- extracting information
from existing models that has been thrown away so far. This mitigates the
problem of representing uncertainty in deep learning without sacrificing either
computational complexity or test accuracy. We perform an extensive study of the
properties of dropout's uncertainty. Various network architectures and
non-linearities are assessed on tasks of regression and classification, using
MNIST as an example. We show a considerable improvement in predictive
log-likelihood and RMSE compared to existing state-of-the-art methods, and
finish by using dropout's uncertainty in deep reinforcement learning.
Tutorial on Answering Questions about Images with Deep Learning
Authors: Mateusz Malinowski, Mario Fritz
Date: 2016-10-04 00:00:00
Tasks: Deep Learning, Natural Language Understanding, Visual Question Answering (VQA)

Together with the development of more accurate methods in Computer Vision and
Natural Language Understanding, holistic architectures that answer on questions
about the content of real-world images have emerged. In this tutorial, we build
a neural-based approach to answer questions about images. We base our tutorial
on two datasets: (mostly on) DAQUAR, and (a bit on) VQA. With small tweaks the
models that we present here can achieve a competitive performance on both
datasets, in fact, they are among the best methods that use a combination of
LSTM with a global, full frame CNN representation of an image. We hope that
after reading this tutorial, the reader will be able to use Deep Learning
frameworks, such as Keras and introduced Kraino, to build various architectures
that will lead to a further performance improvement on this challenging task.