pwc-archive/papers-with-abstracts huggingface dataset:
RenderGAN: Generating Realistic Labeled Data
Authors: Leon Sixt, Benjamin Wild, Tim Landgraf
Date: 2016-11-04 00:00:00
Tasks: Generative Adversarial Network

Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable
performance on many computer vision tasks. Due to their large parameter space,
they require many labeled samples when trained in a supervised setting. The
costs of annotating data manually can render the use of DCNNs infeasible. We
present a novel framework called RenderGAN that can generate large amounts of
realistic, labeled images by combining a 3D model and the Generative
Adversarial Network framework. In our approach, image augmentations (e.g.
lighting, background, and detail) are learned from unlabeled data such that the
generated images are strikingly realistic while preserving the labels known
from the 3D model. We apply the RenderGAN framework to generate images of
barcode-like markers that are attached to honeybees. Training a DCNN on data
generated by the RenderGAN yields considerably better performance than training
it on various baselines.
Two-view 3D Reconstruction for Food Volume Estimation
Authors: Joachim Dehais, Marios Anthimopoulos, Sergey Shevchik, Stavroula Mougiakakou
Date: 2017-01-12 00:00:00
Tasks: 3D Reconstruction, Management, Vocal Bursts Valence Prediction

The increasing prevalence of diet-related chronic diseases coupled with the
ineffectiveness of traditional diet management methods have resulted in a need
for novel tools to accurately and automatically assess meals. Recently,
computer vision based systems that use meal images to assess their content have
been proposed. Food portion estimation is the most difficult task for
individuals assessing their meals and it is also the least studied area. The
present paper proposes a three-stage system to calculate portion sizes using
two images of a dish acquired by mobile devices. The first stage consists in
understanding the configuration of the different views, after which a dense 3D
model is built from the two images; finally, this 3D model serves to extract
the volume of the different items. The system was extensively tested on 77 real
dishes of known volume, and achieved an average error of less than 10% in 5.5
seconds per dish. The proposed pipeline is computationally tractable and
requires no user input, making it a viable option for fully automated dietary
assessment.
Approximation and inference methods for stochastic biochemical kinetics - a tutorial review
Authors: David Schnoerr, Guido Sanguinetti, Ramon Grima
Date: 2016-08-23 00:00:00

Stochastic fluctuations of molecule numbers are ubiquitous in biological
systems. Important examples include gene expression and enzymatic processes in
living cells. Such systems are typically modelled as chemical reaction networks
whose dynamics are governed by the Chemical Master Equation. Despite its simple
structure, no analytic solutions to the Chemical Master Equation are known for
most systems. Moreover, stochastic simulations are computationally expensive,
making systematic analysis and statistical inference a challenging task.
Consequently, significant effort has been spent in recent decades on the
development of efficient approximation and inference methods. This article
gives an introduction to basic modelling concepts as well as an overview of
state of the art methods. First, we motivate and introduce deterministic and
stochastic methods for modelling chemical networks, and give an overview of
simulation and exact solution methods. Next, we discuss several approximation
methods, including the chemical Langevin equation, the system size expansion,
moment closure approximations, time-scale separation approximations and hybrid
methods. We discuss their various properties and review recent advances and
remaining challenges for these methods. We present a comparison of several of
these methods by means of a numerical case study and highlight some of their
respective advantages and disadvantages. Finally, we discuss the problem of
inference from experimental data in the Bayesian framework and review recent
methods developed the literature. In summary, this review gives a
self-contained introduction to modelling, approximations and inference methods
for stochastic chemical kinetics.
A Message Passing Algorithm for the Minimum Cost Multicut Problem
Authors: Paul Swoboda, Bjoern Andres
Date: 2016-12-16 00:00:00
Proceeding: CVPR 2017 7

We propose a dual decomposition and linear program relaxation of the NP -hard
minimum cost multicut problem. Unlike other polyhedral relaxations of the
multicut polytope, it is amenable to efficient optimization by message passing.
Like other polyhedral elaxations, it can be tightened efficiently by cutting
planes. We define an algorithm that alternates between message passing and
efficient separation of cycle- and odd-wheel inequalities. This algorithm is
more efficient than state-of-the-art algorithms based on linear programming,
including algorithms written in the framework of leading commercial software,
as we show in experiments with large instances of the problem from applications
in computer vision, biomedical image analysis and data mining.
A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems
Authors: Paul Swoboda, Jan Kuske, Bogdan Savchynskyy
Date: 2016-12-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Graph Matching

We propose a general dual ascent framework for Lagrangean decomposition of
combinatorial problems. Although methods of this type have shown their
efficiency for a number of problems, so far there was no general algorithm
applicable to multiple problem types. In his work, we propose such a general
algorithm. It depends on several parameters, which can be used to optimize its
performance in each particular setting. We demonstrate efficacy of our method
on graph matching and multicut problems, where it outperforms state-of-the-art
solvers including those based on subgradient optimization and off-the-shelf
linear programming solvers.
A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching
Authors: Paul Swoboda, Carsten Rother, Hassan Abu Alhaija, Dagmar Kainmueller, Bogdan Savchynskyy
Date: 2016-12-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Graph Matching

We study the quadratic assignment problem, in computer vision also known as
graph matching. Two leading solvers for this problem optimize the Lagrange
decomposition duals with sub-gradient and dual ascent (also known as message
passing) updates. We explore s direction further and propose several additional
Lagrangean relaxations of the graph matching problem along with corresponding
algorithms, which are all based on a common dual ascent framework. Our
extensive empirical evaluation gives several theoretical insights and suggests
a new state-of-the-art any-time solver for the considered problem. Our
improvement over state-of-the-art is particularly visible on a new dataset with
large-scale sparse problem instances containing more than 500 graph nodes each.
Personalized Machine Translation: Preserving Original Author Traits
Authors: Ella Rabinovich, Shachar Mirkin, Raj Nath Patel, Lucia Specia, Shuly Wintner
Date: 2016-10-18 00:00:00
Proceeding: EACL 2017 4
Tasks: Domain Adaptation, Machine Translation, Translation

The language that we produce reflects our personality, and various personal
and demographic characteristics can be detected in natural language texts. We
focus on one particular personal trait of the author, gender, and study how it
is manifested in original texts and in translations. We show that author's
gender has a powerful, clear signal in originals texts, but this signal is
obfuscated in human and machine translation. We then propose simple
domain-adaptation techniques that help retain the original gender traits in the
translation, without harming the quality of the translation, thereby creating
more personalized machine translation systems.
Modularized Morphing of Neural Networks
Authors: Tao Wei, Changhu Wang, Chang Wen Chen
Date: 2017-01-12 00:00:00
Tasks: MORPH

In this work we study the problem of network morphism, an effective learning
scheme to morph a well-trained neural network to a new one with the network
function completely preserved. Different from existing work where basic
morphing types on the layer level were addressed, we target at the central
problem of network morphism at a higher level, i.e., how a convolutional layer
can be morphed into an arbitrary module of a neural network. To simplify the
representation of a network, we abstract a module as a graph with blobs as
vertices and convolutional layers as edges, based on which the morphing process
is able to be formulated as a graph transformation problem. Two atomic morphing
operations are introduced to compose the graphs, based on which modules are
classified into two families, i.e., simple morphable modules and complex
modules. We present practical morphing solutions for both of these two
families, and prove that any reasonable module can be morphed from a single
convolutional layer. Extensive experiments have been conducted based on the
state-of-the-art ResNet on benchmark datasets, and the effectiveness of the
proposed solution has been verified.
Relaxation of the EM Algorithm via Quantum Annealing for Gaussian Mixture Models
Authors: Hideyuki Miyahara, Koji Tsumura, Yuki Sughiyama
Date: 2017-01-12 00:00:00

We propose a modified expectation-maximization algorithm by introducing the
concept of quantum annealing, which we call the deterministic quantum annealing
expectation-maximization (DQAEM) algorithm. The expectation-maximization (EM)
algorithm is an established algorithm to compute maximum likelihood estimates
and applied to many practical applications. However, it is known that EM
heavily depends on initial values and its estimates are sometimes trapped by
local optima. To solve such a problem, quantum annealing (QA) was proposed as a
novel optimization approach motivated by quantum mechanics. By employing QA, we
then formulate DQAEM and present a theorem that supports its stability.
Finally, we demonstrate numerical simulations to confirm its efficiency.
Probabilistic Diffeomorphic Registration: Representing Uncertainty
Authors: Demian Wassermann, Matt Toews, Marc Niethammer, William Wells III
Date: 2017-01-12 00:00:00
Tasks: Image Registration

This paper presents a novel mathematical framework for representing
uncertainty in large deformation diffeomorphic image registration. The Bayesian
posterior distribution over the deformations aligning a moving and a fixed
image is approximated via a variational formulation. A stochastic differential
equation (SDE) modeling the deformations as the evolution of a time-varying
velocity field leads to a prior density over deformations in the form of a
Gaussian process. This permits estimating the full posterior distribution in
order to represent uncertainty, in contrast to methods in which the posterior
is approximated via Monte Carlo sampling or maximized in maximum a-posteriori
(MAP) estimation. The frame-work is demonstrated in the case of landmark-based
image registration, including simulated data and annotated pre and
intra-operative 3D images.
QuickNet: Maximizing Efficiency and Efficacy in Deep Architectures
Authors: Tapabrata Ghosh
Date: 2017-01-09 00:00:00
Tasks: Model Compression

We present QuickNet, a fast and accurate network architecture that is both
faster and significantly more accurate than other fast deep architectures like
SqueezeNet. Furthermore, it uses less parameters than previous networks, making
it more memory efficient. We do this by making two major modifications to the
reference Darknet model (Redmon et al, 2015): 1) The use of depthwise separable
convolutions and 2) The use of parametric rectified linear units. We make the
observation that parametric rectified linear units are computationally
equivalent to leaky rectified linear units at test time and the observation
that separable convolutions can be interpreted as a compressed Inception
network (Chollet, 2016). Using these observations, we derive a network
architecture, which we call QuickNet, that is both faster and more accurate
than previous models. Our architecture provides at least four major advantages:
(1) A smaller model size, which is more tenable on memory constrained systems;
(2) A significantly faster network which is more tenable on computationally
constrained systems; (3) A high accuracy of 95.7 percent on the CIFAR-10
Dataset which outperforms all but one result published so far, although we note
that our works are orthogonal approaches and can be combined (4) Orthogonality
to previous model compression approaches allowing for further speed gains to be
realized.
Structured illumination microscopy with unknown patterns and a statistical prior
Authors: Li-Hao Yeh, Lei Tian, Laura Waller
Date: 2016-10-26 00:00:00

Structured illumination microscopy (SIM) improves resolution by
down-modulating high-frequency information of an object to fit within the
passband of the optical system. Generally, the reconstruction process requires
prior knowledge of the illumination patterns, which implies a well-calibrated
and aberration-free system. Here, we propose a new \textit{algorithmic
self-calibration} strategy for SIM that does not need to know the exact
patterns {\it a priori}, but only their covariance. The algorithm, termed
PE-SIMS, includes a Pattern-Estimation (PE) step requiring the uniformity of
the sum of the illumination patterns and a SIM reconstruction procedure using a
Statistical prior (SIMS). Additionally, we perform a pixel reassignment process
(SIMS-PR) to enhance the reconstruction quality. We achieve 2$\times$ better
resolution than a conventional widefield microscope, while remaining
insensitive to aberration-induced pattern distortion and robust against
parameter tuning.
Light Source Point Cluster Selection Based Atmosphere Light Estimation
Authors: Wenbo Zhang, Xiaorong Hou
Date: 2017-01-12 00:00:00
Tasks: Clustering

Atmosphere light value is a highly critical parameter in defogging algorithms
that are based on an atmosphere scattering model. Any error in atmosphere light
value will produce a direct impact on the accuracy of scattering computation
and thus bring chromatic distortion to restored images. To address this
problem, this paper propose a method that relies on clustering statistics to
estimate atmosphere light value. It starts by selecting in the original image
some potential atmosphere light source points, which are grouped into point
clusters by means of clustering technique. From these clusters, a number of
clusters containing candidate atmosphere light source points are selected, the
points are then analyzed statistically, and the cluster containing the most
candidate points is used for estimating atmosphere light value. The mean
brightness vector of the candidate atmosphere light points in the chosen point
cluster is taken as the estimate of atmosphere light value, while their
geometric center in the image is accepted as the location of atmosphere light.
Experimental results suggest that this statistics clustering method produces
more accurate atmosphere brightness vectors and light source locations. This
accuracy translates to, from a subjective perspective, more natural defogging
effect on the one hand and to the improvement in various objective image
quality indicators on the other hand.
An Asynchronous Parallel Approach to Sparse Recovery
Authors: Deanna Needell, Tina Woolf
Date: 2017-01-12 00:00:00
Tasks: compressed sensing

Asynchronous parallel computing and sparse recovery are two areas that have
received recent interest. Asynchronous algorithms are often studied to solve
optimization problems where the cost function takes the form $\sum_{i=1}^M
f_i(x)$, with a common assumption that each $f_i$ is sparse; that is, each
$f_i$ acts only on a small number of components of $x\in\mathbb{R}^n$. Sparse
recovery problems, such as compressed sensing, can be formulated as
optimization problems, however, the cost functions $f_i$ are dense with respect
to the components of $x$, and instead the signal $x$ is assumed to be sparse,
meaning that it has only $s$ non-zeros where $s\ll n$. Here we address how one
may use an asynchronous parallel architecture when the cost functions $f_i$ are
not sparse in $x$, but rather the signal $x$ is sparse. We propose an
asynchronous parallel approach to sparse recovery via a stochastic greedy
algorithm, where multiple processors asynchronously update a vector in shared
memory containing information on the estimated signal support. We include
numerical simulations that illustrate the potential benefits of our proposed
asynchronous method.
Single-Pass, Adaptive Natural Language Filtering: Measuring Value in User Generated Comments on Large-Scale, Social Media News Forums
Authors: Manuel Amunategui
Date: 2017-01-12 00:00:00
Tasks: Marketing, Sentence

There are large amounts of insight and social discovery potential in mining
crowd-sourced comments left on popular news forums like Reddit.com, Tumblr.com,
Facebook.com and Hacker News. Unfortunately, due the overwhelming amount of
participation with its varying quality of commentary, extracting value out of
such data isn't always obvious nor timely. By designing efficient, single-pass
and adaptive natural language filters to quickly prune spam, noise, copy-cats,
marketing diversions, and out-of-context posts, we can remove over a third of
entries and return the comments with a higher probability of relatedness to the
original article in question. The approach presented here uses an adaptive,
two-step filtering process. It first leverages the original article posted in
the thread as a starting corpus to parse comments by matching intersecting
words and term-ratio balance per sentence then grows the corpus by adding new
words harvested from high-matching comments to increase filtering accuracy over
time.
Real-Time Bidding by Reinforcement Learning in Display Advertising
Authors: Han Cai, Kan Ren, Wei-Nan Zhang, Kleanthis Malialis, Jun Wang, Yong Yu, Defeng Guo
Date: 2017-01-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The majority of online display ads are served through real-time bidding (RTB)
--- each ad display impression is auctioned off in real-time when it is just
being generated from a user visit. To place an ad automatically and optimally,
it is critical for advertisers to devise a learning algorithm to cleverly bid
an ad impression in real-time. Most previous works consider the bid decision as
a static optimization problem of either treating the value of each impression
independently or setting a bid price to each segment of ad volume. However, the
bidding for a given ad campaign would repeatedly happen during its life span
before the budget runs out. As such, each bid is strategically correlated by
the constrained budget and the overall effectiveness of the campaign (e.g., the
rewards from generated clicks), which is only observed after the campaign has
completed. Thus, it is of great interest to devise an optimal bidding strategy
sequentially so that the campaign budget can be dynamically allocated across
all the available impressions on the basis of both the immediate and future
rewards. In this paper, we formulate the bid decision process as a
reinforcement learning problem, where the state space is represented by the
auction information and the campaign's real-time parameters, while an action is
the bid price to set. By modeling the state transition via auction competition,
we build a Markov Decision Process framework for learning the optimal bidding
policy to optimize the advertising performance in the dynamic real-time bidding
environment. Furthermore, the scalability problem from the large real-world
auction volume and campaign budget is well handled by state value approximation
using neural networks.
Optimal Inference in Crowdsourced Classification via Belief Propagation
Authors: Jungseul Ok, Sewoong Oh, Jinwoo Shin, Yung Yi
Date: 2016-02-11 00:00:00
Tasks: Classification, General Classification

Crowdsourcing systems are popular for solving large-scale labelling tasks
with low-paid workers. We study the problem of recovering the true labels from
the possibly erroneous crowdsourced labels under the popular Dawid-Skene model.
To address this inference problem, several algorithms have recently been
proposed, but the best known guarantee is still significantly larger than the
fundamental limit. We close this gap by introducing a tighter lower bound on
the fundamental limit and proving that Belief Propagation (BP) exactly matches
this lower bound. The guaranteed optimality of BP is the strongest in the sense
that it is information-theoretically impossible for any other algorithm to
correctly label a larger fraction of the tasks. Experimental results suggest
that BP is close to optimal for all regimes considered and improves upon
competing state-of-the-art algorithms.
Unsupervised Latent Behavior Manifold Learning from Acoustic Features: audio2behavior
Authors: Haoqi Li, Brian Baucom, Panayiotis Georgiou
Date: 2017-01-12 00:00:00

Behavioral annotation using signal processing and machine learning is highly
dependent on training data and manual annotations of behavioral labels.
Previous studies have shown that speech information encodes significant
behavioral information and be used in a variety of automated behavior
recognition tasks. However, extracting behavior information from speech is
still a difficult task due to the sparseness of training data coupled with the
complex, high-dimensionality of speech, and the complex and multiple
information streams it encodes. In this work we exploit the slow varying
properties of human behavior. We hypothesize that nearby segments of speech
share the same behavioral context and hence share a similar underlying
representation in a latent space. Specifically, we propose a Deep Neural
Network (DNN) model to connect behavioral context and derive the behavioral
manifold in an unsupervised manner. We evaluate the proposed manifold in the
couples therapy domain and also provide examples from publicly available data
(e.g. stand-up comedy). We further investigate training within the couples'
therapy domain and from movie data. The results are extremely encouraging and
promise improved behavioral quantification in an unsupervised manner and
warrants further investigation in a range of applications.
Parsing Universal Dependencies without training
Authors: Héctor Martínez Alonso, Željko Agić, Barbara Plank, Anders Søgaard
Date: 2017-01-11 00:00:00
Proceeding: EACL 2017 4

We propose UDP, the first training-free parser for Universal Dependencies
(UD). Our algorithm is based on PageRank and a small set of head attachment
rules. It features two-step decoding to guarantee that function words are
attached as leaf nodes. The parser requires no training, and it is competitive
with a delexicalized transfer system. UDP offers a linguistically sound
unsupervised alternative to cross-lingual parsing for UD, which can be used as
a baseline for such systems. The parser has very few parameters and is
distinctly robust to domain change across languages.