pwc-archive/papers-with-abstracts huggingface dataset:
Understanding Innovation to Drive Sustainable Development
Authors: Prasanna Sattigeri, Aurélie Lozano, Aleksandra Mojsilović, Kush R. Varshney, Mahmoud Naghshineh
Date: 2016-06-15 00:00:00

Innovation is among the key factors driving a country's economic and social
growth. But what are the factors that make a country innovative? How do they
differ across different parts of the world and different stages of development?
In this work done in collaboration with the World Economic Forum (WEF), we
analyze the scores obtained through executive opinion surveys that constitute
the WEF's Global Competitiveness Index in conjunction with other country-level
metrics and indicators to identify actionable levers of innovation. The
findings can help country leaders and organizations shape the policies to drive
developmental activities and increase the capacity of innovation.
Discrete Distribution Estimation under Local Privacy
Authors: Peter Kairouz, Keith Bonawitz, Daniel Ramage
Date: 2016-02-24 00:00:00

The collection and analysis of user data drives improvements in the app and
web ecosystems, but comes with risks to privacy. This paper examines discrete
distribution estimation under local privacy, a setting wherein service
providers can learn the distribution of a categorical statistic of interest
without collecting the underlying data. We present new mechanisms, including
hashed K-ary Randomized Response (KRR), that empirically meet or exceed the
utility of existing mechanisms at all privacy levels. New theoretical results
demonstrate the order-optimality of KRR and the existing RAPPOR mechanism at
different privacy regimes.
cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL
Authors: Hugh Perkins
Date: 2016-06-15 00:00:00

This paper presents cltorch, a hardware-agnostic backend for the Torch neural
network framework. cltorch enables training of deep neural networks on GPUs
from diverse hardware vendors, including AMD, NVIDIA, and Intel. cltorch
contains sufficient implementation to run models such as AlexNet, VGG,
Overfeat, and GoogleNet. It is written using the OpenCL language, a portable
compute language, governed by the Khronos Group. cltorch is the top-ranked
hardware-agnostic machine learning framework on Chintala's convnet-benchmarks
page.
  This paper presents the technical challenges encountered whilst creating the
cltorch backend for Torch, and looks in detail at the challenges related to
obtaining a fast hardware-agnostic implementation.
  The convolutional layers are identified as the key area of focus for
accelerating hardware-agnostic frameworks. Possible approaches to accelerating
the convolutional implementation are identified including: implementation of
the convolutions using the implicitgemm or winograd algorithm, using a GEMM
implementation adapted to the geometries associated with the convolutional
algorithm, or using a pluggable hardware-specific convolutional implementation.
Smart Reply: Automated Response Suggestion for Email
Authors: Anjuli Kannan, Karol Kurach, Sujith Ravi, Tobias Kaufmann, Andrew Tomkins, Balint Miklos, Greg Corrado, Laszlo Lukacs, Marina Ganea, Peter Young, Vivek Ramavajjala
Date: 2016-06-15 00:00:00
Tasks: Clustering, Diversity

In this paper we propose and investigate a novel end-to-end method for
automatically generating short email responses, called Smart Reply. It
generates semantically diverse suggestions that can be used as complete email
responses with just one tap on mobile. The system is currently used in Inbox by
Gmail and is responsible for assisting with 10% of all mobile responses. It is
designed to work at very high throughput and process hundreds of millions of
messages daily. The system exploits state-of-the-art, large-scale deep
learning.
  We describe the architecture of the system as well as the challenges that we
faced while building it, like response diversity and scalability. We also
introduce a new method for semantic clustering of user-generated content that
requires only a modest amount of explicitly labeled data.
The ND-IRIS-0405 Iris Image Dataset
Authors: Kevin W. Bowyer, Patrick J. Flynn
Date: 2016-06-15 00:00:00

The Computer Vision Research Lab at the University of Notre Dame began
collecting iris images in the spring semester of 2004. The initial data
collections used an LG 2200 iris imaging system for image acquisition. Image
datasets acquired in 2004-2005 at Notre Dame with this LG 2200 have been used
in the ICE 2005 and ICE 2006 iris biometric evaluations. The ICE 2005 iris
image dataset has been distributed to over 100 research groups around the
world. The purpose of this document is to describe the content of the
ND-IRIS-0405 iris image dataset. This dataset is a superset of the iris image
datasets used in ICE 2005 and ICE 2006. The ND 2004-2005 iris image dataset
contains 64,980 images corresponding to 356 unique subjects, and 712 unique
irises. The age range of the subjects is 18 to 75 years old. 158 of the
subjects are female, and 198 are male. 250 of the subjects are Caucasian, 82
are Asian, and 24 are other ethnicities.
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Authors: Fausto Milletari, Nassir Navab, Seyed-Ahmad Ahmadi
Date: 2016-06-15 00:00:00
Tasks: Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Semantic Segmentation, Volumetric Medical Image Segmentation

Convolutional Neural Networks (CNNs) have been recently employed to solve
problems from both the computer vision and medical image analysis fields.
Despite their popularity, most approaches are only able to process 2D images
while most medical data used in clinical practice consists of 3D volumes. In
this work we propose an approach to 3D image segmentation based on a
volumetric, fully convolutional, neural network. Our CNN is trained end-to-end
on MRI volumes depicting prostate, and learns to predict segmentation for the
whole volume at once. We introduce a novel objective function, that we optimise
during training, based on Dice coefficient. In this way we can deal with
situations where there is a strong imbalance between the number of foreground
and background voxels. To cope with the limited number of annotated volumes
available for training, we augment the data applying random non-linear
transformations and histogram matching. We show in our experimental evaluation
that our approach achieves good performances on challenging test data while
requiring only a fraction of the processing time needed by other previous
methods.
Free Form based active contours for image segmentation and free space perception
Authors: Ouiddad Labbani I., Pauline Merveilleux O, Olivier Ruatta
Date: 2016-06-15 00:00:00
Tasks: Form, Image Segmentation, Segmentation, Semantic Segmentation

In this paper we present a novel approach for representing and evolving
deformable active contours. The method combines piecewise regular B{\'e}zier
models and curve evolution defined by local Free Form Deformation. The contour
deformation is locally constrained which allows contour convergence with almost
linear complexity while adapting to various shape settings and handling
topology changes of the active contour.
  We demonstrate the effectiveness of the new active contour scheme for visual
free space perception and segmentation using omnidirectional images acquired by
a robot exploring unknown indoor and outdoor environments. Several experiments
validate the approach with comparison to state-of-the art parametric and
geometric active contours and provide fast and real-time robot free space
segmentation and navigation.
Probe-based Rapid Hybrid Hyperspectral and Tissue Surface Imaging Aided by Fully Convolutional Networks
Authors: Jianyu Lin, Neil T. Clancy, Xueqing Sun, Ji Qi, Mirek Janatka, Danail Stoyanov, Daniel S. Elson
Date: 2016-06-15 00:00:00
Tasks: GPU

Tissue surface shape and reflectance spectra provide rich intra-operative
information useful in surgical guidance. We propose a hybrid system which
displays an endoscopic image with a fast joint inspection of tissue surface
shape using structured light (SL) and hyperspectral imaging (HSI). For SL a
miniature fibre probe is used to project a coloured spot pattern onto the
tissue surface. In HSI mode standard endoscopic illumination is used, with the
fibre probe collecting reflected light and encoding the spatial information
into a linear format that can be imaged onto the slit of a spectrograph.
Correspondence between the arrangement of fibres at the distal and proximal
ends of the bundle was found using spectral encoding. Then during pattern
decoding, a fully convolutional network (FCN) was used for spot detection,
followed by a matching propagation algorithm for spot identification. This
method enabled fast reconstruction (12 frames per second) using a GPU. The
hyperspectral image was combined with the white light image and the
reconstructed surface, showing the spectral information of different areas.
Validation of this system using phantom and ex vivo experiments has been
demonstrated.
A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation
Authors: Amrita Saha, Mitesh M. Khapra, Sarath Chandar, Janarthanan Rajendran, Kyunghyun Cho
Date: 2016-06-15 00:00:00
Proceeding: COLING 2016 12
Tasks: Decoder, Machine Translation, Transliteration

Interlingua based Machine Translation (MT) aims to encode multiple languages
into a common linguistic representation and then decode sentences in multiple
target languages from this representation. In this work we explore this idea in
the context of neural encoder decoder architectures, albeit on a smaller scale
and without MT as the end goal. Specifically, we consider the case of three
languages or modalities X, Z and Y wherein we are interested in generating
sequences in Y starting from information available in X. However, there is no
parallel training data available between X and Y but, training data is
available between X & Z and Z & Y (as is often the case in many real world
applications). Z thus acts as a pivot/bridge. An obvious solution, which is
perhaps less elegant but works very well in practice is to train a two stage
model which first converts from X to Z and then from Z to Y. Instead we explore
an interlingua inspired solution which jointly learns to do the following (i)
encode X and Z to a common representation and (ii) decode Y from this common
representation. We evaluate our model on two tasks: (i) bridge transliteration
and (ii) bridge captioning. We report promising results in both these
applications and believe that this is a right step towards truly interlingua
inspired encoder decoder architectures.
Multi-Modal Hybrid Deep Neural Network for Speech Enhancement
Authors: Zhenzhou Wu, Sunil Sivadas, Yong Kiam Tan, Ma Bin, Rick Siow Mong Goh
Date: 2016-06-15 00:00:00
Tasks: Speech Enhancement

Deep Neural Networks (DNN) have been successful in en- hancing noisy speech
signals. Enhancement is achieved by learning a nonlinear mapping function from
the features of the corrupted speech signal to that of the reference clean
speech signal. The quality of predicted features can be improved by providing
additional side channel information that is robust to noise, such as visual
cues. In this paper we propose a novel deep learning model inspired by insights
from human audio visual perception. In the proposed unified hybrid
architecture, features from a Convolution Neural Network (CNN) that processes
the visual cues and features from a fully connected DNN that processes the
audio signal are integrated using a Bidirectional Long Short-Term Memory
(BiLSTM) network. The parameters of the hybrid model are jointly learned using
backpropagation. We compare the quality of enhanced speech from the hybrid
models with those from traditional DNN and BiLSTM models.
Self-organized control for musculoskeletal robots
Authors: Ralf Der, Georg Martius
Date: 2016-02-09 00:00:00

With the accelerated development of robot technologies, optimal control
becomes one of the central themes of research. In traditional approaches, the
controller, by its internal functionality, finds appropriate actions on the
basis of the history of sensor values, guided by the goals, intentions,
objectives, learning schemes, and so on planted into it. The idea is that the
controller controls the world---the body plus its environment---as reliably as
possible. However, in elastically actuated robots this approach faces severe
difficulties. This paper advocates for a new paradigm of self-organized
control. The paper presents a solution with a controller that is devoid of any
functionalities of its own, given by a fixed, explicit and context-free
function of the recent history of the sensor values. When applying this
controller to a muscle-tendon driven arm-shoulder system from the Myorobotics
toolkit, we observe a vast variety of self-organized behavior patterns: when
left alone, the arm realizes pseudo-random sequences of different poses but one
can also manipulate the system into definite motion patterns. But most
interestingly, after attaching an object, the controller gets in a functional
resonance with the object's internal dynamics: when given a half-filled bottle,
the system spontaneously starts shaking the bottle so that maximum response
from the dynamics of the water is being generated. After attaching a pendulum
to the arm, the controller drives the pendulum into a circular mode. In this
way, the robot discovers dynamical affordances of objects its body is
interacting with. We also discuss perspectives for using this controller
paradigm for intention driven behavior generation.
Personality Traits and Echo Chambers on Facebook
Authors: Alessandro Bessi
Date: 2016-06-15 00:00:00
Tasks: Misinformation

In online social networks, users tend to select information that adhere to
their system of beliefs and to form polarized groups of like minded people.
Polarization as well as its effects on online social interactions have been
extensively investigated. Still, the relation between group formation and
personality traits remains unclear. A better understanding of the cognitive and
psychological determinants of online social dynamics might help to design more
efficient communication strategies and to challenge the digital misinformation
threat. In this work, we focus on users commenting posts published by US
Facebook pages supporting scientific and conspiracy-like narratives, and we
classify the personality traits of those users according to their online
behavior. We show that different and conflicting communities are populated by
users showing similar psychological profiles, and that the dominant personality
model is the same in both scientific and conspiracy echo chambers. Moreover, we
observe that the permanence within echo chambers slightly shapes users'
psychological profiles. Our results suggest that the presence of specific
personality traits in individuals lead to their considerable involvement in
supporting narratives inside virtual echo chambers.
Personalizing Human Video Pose Estimation
Authors: James Charles, Tomas Pfister, Derek Magee, David Hogg, Andrew Zisserman
Date: 2015-11-20 00:00:00
Proceeding: CVPR 2016 6
Tasks: Optical Flow Estimation, Pose Estimation

We propose a personalized ConvNet pose estimator that automatically adapts
itself to the uniqueness of a person's appearance to improve pose estimation in
long videos. We make the following contributions: (i) we show that given a few
high-precision pose annotations, e.g. from a generic ConvNet pose estimator,
additional annotations can be generated throughout the video using a
combination of image-based matching for temporally distant frames, and dense
optical flow for temporally local frames; (ii) we develop an occlusion aware
self-evaluation model that is able to automatically select the high-quality and
reject the erroneous additional annotations; and (iii) we demonstrate that
these high-quality annotations can be used to fine-tune a ConvNet pose
estimator and thereby personalize it to lock on to key discriminative features
of the person's appearance. The outcome is a substantial improvement in the
pose estimates for the target video using the personalized ConvNet compared to
the original generic ConvNet. Our method outperforms the state of the art
(including top ConvNet methods) by a large margin on two standard benchmarks,
as well as on a new challenging YouTube video dataset. Furthermore, we show
that training from the automatically generated annotations can be used to
improve the performance of a generic ConvNet on other benchmarks.
Neither Quick Nor Proper -- Evaluation of QuickProp for Learning Deep Neural Networks
Authors: Clemens-Alexander Brust, Sven Sickert, Marcel Simon, Erik Rodner, Joachim Denzler
Date: 2016-06-14 00:00:00
Tasks: Segmentation, Semantic Segmentation

Neural networks and especially convolutional neural networks are of great
interest in current computer vision research. However, many techniques,
extensions, and modifications have been published in the past, which are not
yet used by current approaches. In this paper, we study the application of a
method called QuickProp for training of deep neural networks. In particular, we
apply QuickProp during learning and testing of fully convolutional networks for
the task of semantic segmentation. We compare QuickProp empirically with
gradient descent, which is the current standard method. Experiments suggest
that QuickProp can not compete with standard gradient descent techniques for
complex computer vision tasks like semantic segmentation.
DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers
Authors: Amir Ghodrati, Ali Diba, Marco Pedersoli, Tinne Tuytelaars, Luc van Gool
Date: 2016-06-15 00:00:00
Tasks: Object

In this paper, a new method for generating object and action proposals in
images and videos is proposed. It builds on activations of different
convolutional layers of a pretrained CNN, combining the localization accuracy
of the early layers with the high informative-ness (and hence recall) of the
later layers. To this end, we build an inverse cascade that, going backward
from the later to the earlier convolutional layers of the CNN, selects the most
promising locations and refines them in a coarse-to-fine manner. The method is
efficient, because i) it re-uses the same features extracted for detection, ii)
it aggregates features using integral images, and iii) it avoids a dense
evaluation of the proposals thanks to the use of the inverse coarse-to-fine
cascade. The method is also accurate. We show that our DeepProposals outperform
most of the previously proposed object proposal and action proposal approaches
and, when plugged into a CNN-based object detector, produce state-of-the-art
detection performance.
A practical local tomography reconstruction algorithm based on known subregion
Authors: Pierre Paleo, Michel Desvignes, Alessandro Mirone
Date: 2016-06-15 00:00:00

We propose a new method to reconstruct data acquired in a local tomography
setup. This method uses an initial reconstruction and refines it by correcting
the low frequency artifacts known as the cupping effect. A basis of Gaussian
functions is used to correct the initial reconstruction. The coefficients of
this basis are iteratively optimized under the constraint of a known subregion.
Using a coarse basis reduces the degrees of freedom of the problem while
actually correcting the cupping effect. Simulations show that the known region
constraint yields an unbiased reconstruction, in accordance to uniqueness
theorems stated in local tomography.
Strategic Attentive Writer for Learning Macro-Actions
Authors: Alexander, Vezhnevets, Volodymyr Mnih, John Agapiou, Simon Osindero, Alex Graves, Oriol Vinyals, Koray Kavukcuoglu
Date: 2016-06-15 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Atari Games, Reinforcement Learning

We present a novel deep recurrent neural network architecture that learns to
build implicit plans in an end-to-end manner by purely interacting with an
environment in reinforcement learning setting. The network builds an internal
plan, which is continuously updated upon observation of the next input from the
environment. It can also partition this internal representation into contiguous
sub- sequences by learning for how long the plan can be committed to - i.e.
followed without re-planing. Combining these properties, the proposed model,
dubbed STRategic Attentive Writer (STRAW) can learn high-level, temporally
abstracted macro- actions of varying lengths that are solely learnt from data
without any prior information. These macro-actions enable both structured
exploration and economic computation. We experimentally demonstrate that STRAW
delivers strong improvements on several ATARI games by employing temporally
extended planning strategies (e.g. Ms. Pacman and Frostbite). It is at the same
time a general algorithm that can be applied on any sequence data. To that end,
we also show that when trained on text prediction task, STRAW naturally
predicts frequent n-grams (instead of macro-actions), demonstrating the
generality of the approach.
Natural Language Generation as Planning under Uncertainty Using Reinforcement Learning
Authors: Verena Rieser, Oliver Lemon
Date: 2016-06-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Spoken Dialogue Systems, Text Generation

We present and evaluate a new model for Natural Language Generation (NLG) in
Spoken Dialogue Systems, based on statistical planning, given noisy feedback
from the current generation context (e.g. a user and a surface realiser). We
study its use in a standard NLG problem: how to present information (in this
case a set of search results) to users, given the complex trade- offs between
utterance length, amount of information conveyed, and cognitive load. We set
these trade-offs by analysing existing MATCH data. We then train a NLG pol- icy
using Reinforcement Learning (RL), which adapts its behaviour to noisy feed-
back from the current generation context. This policy is compared to several
base- lines derived from previous work in this area. The learned policy
significantly out- performs all the prior approaches.
From Word Embeddings to Item Recommendation
Authors: Makbule Gulcin Ozsoy
Date: 2016-01-07 00:00:00
Tasks: Recommendation Systems, Word Embeddings

Social network platforms can use the data produced by their users to serve
them better. One of the services these platforms provide is recommendation
service. Recommendation systems can predict the future preferences of users
using their past preferences. In the recommendation systems literature there
are various techniques, such as neighborhood based methods, machine-learning
based methods and matrix-factorization based methods. In this work, a set of
well known methods from natural language processing domain, namely Word2Vec, is
applied to recommendation systems domain. Unlike previous works that use
Word2Vec for recommendation, this work uses non-textual features, the
check-ins, and it recommends venues to visit/check-in to the target users. For
the experiments, a Foursquare check-in dataset is used. The results show that
use of continuous vector space representations of items modeled by techniques
of Word2Vec is promising for making recommendations.