pwc-archive/papers-with-abstracts huggingface dataset:
Gender-From-Iris or Gender-From-Mascara?
Authors: Andrey Kuehlkamp, Benedict Becker, Kevin Bowyer
Date: 2017-02-04 00:00:00

Predicting a person's gender based on the iris texture has been explored by
several researchers. This paper considers several dimensions of experimental
work on this problem, including person-disjoint train and test, and the effect
of cosmetics on eyelash occlusion and imperfect segmentation. We also consider
the use of multi-layer perceptron and convolutional neural networks as
classifiers, comparing the use of data-driven and hand-crafted features. Our
results suggest that the gender-from-iris problem is more difficult than has so
far been appreciated. Estimating accuracy using a mean of N person-disjoint
train and test partitions, and considering the effect of makeup - a combination
of experimental conditions not present in any previous work - we find a much
weaker ability to predict gender-from-iris texture than has been suggested in
previous work.
Image De-raining Using a Conditional Generative Adversarial Network
Authors: He Zhang, Vishwanath Sindagi, Vishal M. Patel
Date: 2017-01-21 00:00:00
Tasks: Generative Adversarial Network, Rain Removal

Severe weather conditions such as rain and snow adversely affect the visual quality of images captured under such conditions thus rendering them useless for further usage and sharing. In addition, such degraded images drastically affect performance of vision systems. Hence, it is important to solve the problem of single image de-raining/de-snowing. However, this is a difficult problem to solve due to its inherent ill-posed nature. Existing approaches attempt to introduce prior information to convert it into a well-posed problem. In this paper, we investigate a new point of view in addressing the single image de-raining problem. Instead of focusing only on deciding what is a good prior or a good framework to achieve good quantitative and qualitative performance, we also ensure that the de-rained image itself does not degrade the performance of a given computer vision algorithm such as detection and classification. In other words, the de-rained result should be indistinguishable from its corresponding clear image to a given discriminator. This criterion can be directly incorporated into the optimization framework by using the recently introduced conditional generative adversarial networks (GANs). To minimize artifacts introduced by GANs and ensure better visual quality, a new refined loss function is introduced. Based on this, we propose a novel single image de-raining method called Image De-raining Conditional General Adversarial Network (ID-CGAN), which considers quantitative, visual and also discriminative performance into the objective function. Experiments evaluated on synthetic images and real images show that the proposed method outperforms many recent state-of-the-art single image de-raining methods in terms of quantitative and visual performance.
Latent Hinge-Minimax Risk Minimization for Inference from a Small Number of Training Samples
Authors: Dolev Raviv, Margarita Osadchy
Date: 2017-02-04 00:00:00
Tasks: Transfer Learning

Deep Learning (DL) methods show very good performance when trained on large,
balanced data sets. However, many practical problems involve imbalanced data
sets, or/and classes with a small number of training samples. The performance
of DL methods as well as more traditional classifiers drops significantly in
such settings. Most of the existing solutions for imbalanced problems focus on
customizing the data for training. A more principled solution is to use mixed
Hinge-Minimax risk [19] specifically designed to solve binary problems with
imbalanced training sets. Here we propose a Latent Hinge Minimax (LHM) risk and
a training algorithm that generalizes this paradigm to an ensemble of
hyperplanes that can form arbitrary complex, piecewise linear boundaries. To
extract good features, we combine LHM model with CNN via transfer learning. To
solve multi-class problem we map pre-trained category-specific LHM classifiers
to a multi-class neural network and adjust the weights with very fast tuning.
LHM classifier enables the use of unlabeled data in its training and the
mapping allows for multi-class inference, resulting in a classifier that
performs better than alternatives when trained on a small number of training
samples.
Doubly-Attentive Decoder for Multi-modal Neural Machine Translation
Authors: Iacer Calixto, Qun Liu, Nick Campbell
Date: 2017-02-04 00:00:00
Proceeding: ACL 2017 7
Tasks: Decoder, Image Description, Machine Translation, Multimodal Machine Translation, Translation

We introduce a Multi-modal Neural Machine Translation model in which a
doubly-attentive decoder naturally incorporates spatial visual features
obtained using pre-trained convolutional neural networks, bridging the gap
between image description and translation. Our decoder learns to attend to
source-language words and parts of an image independently by means of two
separate attention mechanisms as it generates words in the target language. We
find that our model can efficiently exploit not just back-translated in-domain
multi-modal data but also large general-domain text-only MT corpora. We also
report state-of-the-art results on the Multi30k data set.
Solving the Brachistochrone Problem by an Influence Diagram
Authors: Jiří Vomlel
Date: 2017-02-04 00:00:00

Influence diagrams are a decision-theoretic extension of probabilistic
graphical models. In this paper we show how they can be used to solve the
Brachistochrone problem. We present results of numerical experiments on this
problem, compare the solution provided by the influence diagram with the
optimal solution. The R code used for the experiments is presented in the
Appendix.
Using Complex Wavelet Transform and Bilateral Filtering for Image Denoising
Authors: Seyede Mahya Hazavei, Hamid Reza Shahdoosti
Date: 2017-02-04 00:00:00
Tasks: Denoising, Image Denoising

The bilateral filter is a useful nonlinear filter which without smoothing
edges, it does spatial averaging. In the literature, the effectiveness of this
method for image denoising is shown. In this paper, an extension of this method
is proposed which is based on complex wavelet transform. In fact, the bilateral
filtering is applied to the low-frequency (approximation) subbands of the
decomposed image using complex wavelet transform, while the thresholding
approach is applied to the high frequency subbands. Using the bilateral filter
in the complex wavelet domain forms a new image denoising framework.
Experimental results for real data are provided, by which one can see the
effectiveness of the proposed method in eliminating noise.
Network-based methods for outcome prediction in the "sample space"
Authors: Jessica Gliozzo
Date: 2017-02-04 00:00:00

In this thesis we present the novel semi-supervised network-based algorithm
P-Net, which is able to rank and classify patients with respect to a specific
phenotype or clinical outcome under study. The peculiar and innovative
characteristic of this method is that it builds a network of samples/patients,
where the nodes represent the samples and the edges are functional or genetic
relationships between individuals (e.g. similarity of expression profiles), to
predict the phenotype under study. In other words, it constructs the network in
the "sample space" and not in the "biomarker space" (where nodes represent
biomolecules (e.g. genes, proteins) and edges represent functional or genetic
relationships between nodes), as usual in state-of-the-art methods. To assess
the performances of P-Net, we apply it on three different publicly available
datasets from patients afflicted with a specific type of tumor: pancreatic
cancer, melanoma and ovarian cancer dataset, by using the data and following
the experimental set-up proposed in two recently published papers [Barter et
al., 2014, Winter et al., 2012]. We show that network-based methods in the
"sample space" can achieve results competitive with classical supervised
inductive systems. Moreover, the graph representation of the samples can be
easily visualized through networks and can be used to gain visual clues about
the relationships between samples, taking into account the phenotype associated
or predicted for each sample. To our knowledge this is one of the first works
that proposes graph-based algorithms working in the "sample space" of the
biomolecular profiles of the patients to predict their phenotype or outcome,
thus contributing to a novel research line in the framework of the Network
Medicine.
Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review
Authors: Tomaso Poggio, Hrushikesh Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao
Date: 2016-11-02 00:00:00
Tasks: Deep Learning

The paper characterizes classes of functions for which deep learning can be
exponentially better than shallow learning. Deep convolutional networks are a
special case of these conditions, though weight sharing is not the main reason
for their exponential advantage.
Cross-Lingual Syntactic Transfer with Limited Resources
Authors: Mohammad Sadegh Rasooli, Michael Collins
Date: 2016-10-19 00:00:00
Proceeding: TACL 2017 1
Tasks: Translation

We describe a simple but effective method for cross-lingual syntactic
transfer of dependency parsers, in the scenario where a large amount of
translation data is not available. The method makes use of three steps: 1) a
method for deriving cross-lingual word clusters, which can then be used in a
multilingual parser; 2) a method for transferring lexical information from a
target language to source language treebanks; 3) a method for integrating these
steps with the density-driven annotation projection method of Rasooli and
Collins (2015). Experiments show improvements over the state-of-the-art in
several languages used in previous work, in a setting where the only source of
translation data is the Bible, a considerably smaller corpus than the Europarl
corpus used in previous work. Results using the Europarl corpus as a source of
translation data show additional improvements over the results of Rasooli and
Collins (2015). We conclude with results on 38 datasets from the Universal
Dependencies corpora.
A Learning-Based Approach for Lane Departure Warning Systems with a Personalized Driver Model
Authors: Wenshuo Wang, Ding Zhao, Junqiang Xi, Wei Han
Date: 2017-02-04 00:00:00

Misunderstanding of driver correction behaviors (DCB) is the primary reason
for false warnings of lane-departure-prediction systems. We propose a
learning-based approach to predicting unintended lane-departure behaviors (LDB)
and the chance for drivers to bring the vehicle back to the lane. First, in
this approach, a personalized driver model for lane-departure and lane-keeping
behavior is established by combining the Gaussian mixture model and the hidden
Markov model. Second, based on this model, we develop an online model-based
prediction algorithm to predict the forthcoming vehicle trajectory and judge
whether the driver will demonstrate an LDB or a DCB. We also develop a warning
strategy based on the model-based prediction algorithm that allows the
lane-departure warning system to be acceptable for drivers according to the
predicted trajectory. In addition, the naturalistic driving data of 10 drivers
is collected through the University of Michigan Safety Pilot Model Deployment
program to train the personalized driver model and validate this approach. We
compare the proposed method with a basic time-to-lane-crossing (TLC) method and
a TLC-directional sequence of piecewise lateral slopes (TLC-DSPLS) method. The
results show that the proposed approach can reduce the false-warning rate to
3.07\%.
Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective
Authors: Shixia Liu, Xiting Wang, Mengchen Liu, Jun Zhu
Date: 2017-02-04 00:00:00
Tasks: BIG-bench Machine Learning

Interactive model analysis, the process of understanding, diagnosing, and
refining a machine learning model with the help of interactive visualization,
is very important for users to efficiently solve real-world artificial
intelligence and data mining problems. Dramatic advances in big data analytics
has led to a wide variety of interactive model analysis tasks. In this paper,
we present a comprehensive analysis and interpretation of this rapidly
developing area. Specifically, we classify the relevant work into three
categories: understanding, diagnosis, and refinement. Each category is
exemplified by recent influential work. Possible future research opportunities
are also explored and discussed.
Space-Time Representation of People Based on 3D Skeletal Data: A Review
Authors: Fei Han, Brian Reily, William Hoff, Hao Zhang
Date: 2016-01-05 00:00:00
Tasks: Feature Engineering

Spatiotemporal human representation based on 3D visual perception data is a
rapidly growing research area. Based on the information sources, these
representations can be broadly categorized into two groups based on RGB-D
information or 3D skeleton data. Recently, skeleton-based human representations
have been intensively studied and kept attracting an increasing attention, due
to their robustness to variations of viewpoint, human body scale and motion
speed as well as the realtime, online performance. This paper presents a
comprehensive survey of existing space-time representations of people based on
3D skeletal data, and provides an informative categorization and analysis of
these methods from the perspectives, including information modality,
representation encoding, structure and transition, and feature engineering. We
also provide a brief overview of skeleton acquisition devices and construction
methods, enlist a number of public benchmark datasets with skeleton data, and
discuss potential future research directions.
Probabilistic Sensor Fusion for Ambient Assisted Living
Authors: Tom Diethe, Niall Twomey, Meelis Kull, Peter Flach, Ian Craddock
Date: 2017-02-04 00:00:00
Tasks: Activity Recognition, Sensor Fusion

There is a widely-accepted need to revise current forms of health-care
provision, with particular interest in sensing systems in the home. Given a
multiple-modality sensor platform with heterogeneous network connectivity, as
is under development in the Sensor Platform for HEalthcare in Residential
Environment (SPHERE) Interdisciplinary Research Collaboration (IRC), we face
specific challenges relating to the fusion of the heterogeneous sensor
modalities.
  We introduce Bayesian models for sensor fusion, which aims to address the
challenges of fusion of heterogeneous sensor modalities. Using this approach we
are able to identify the modalities that have most utility for each particular
activity, and simultaneously identify which features within that activity are
most relevant for a given activity.
  We further show how the two separate tasks of location prediction and
activity recognition can be fused into a single model, which allows for
simultaneous learning an prediction for both tasks.
  We analyse the performance of this model on data collected in the SPHERE
house, and show its utility. We also compare against some benchmark models
which do not have the full structure,and show how the proposed model compares
favourably to these methods
A Theoretical Analysis of First Heuristics of Crowdsourced Entity Resolution
Authors: Arya Mazumdar, Barna Saha
Date: 2017-02-03 00:00:00
Tasks: Entity Resolution

Entity resolution (ER) is the task of identifying all records in a database
that refer to the same underlying entity, and are therefore duplicates of each
other. Due to inherent ambiguity of data representation and poor data quality,
ER is a challenging task for any automated process. As a remedy, human-powered
ER via crowdsourcing has become popular in recent years. Using crowd to answer
queries is costly and time consuming. Furthermore, crowd-answers can often be
faulty. Therefore, crowd-based ER methods aim to minimize human participation
without sacrificing the quality and use a computer generated similarity matrix
actively. While, some of these methods perform well in practice, no theoretical
analysis exists for them, and further their worst case performances do not
reflect the experimental findings. This creates a disparity in the
understanding of the popular heuristics for this problem. In this paper, we
make the first attempt to close this gap. We provide a thorough analysis of the
prominent heuristic algorithms for crowd-based ER. We justify experimental
observations with our analysis and information theoretic lower bounds.
Traffic Lights with Auction-Based Controllers: Algorithms and Real-World Data
Authors: Shumeet Baluja, Michele Covell, Rahul Sukthankar
Date: 2017-02-03 00:00:00

Real-time optimization of traffic flow addresses important practical
problems: reducing a driver's wasted time, improving city-wide efficiency,
reducing gas emissions and improving air quality. Much of the current research
in traffic-light optimization relies on extending the capabilities of traffic
lights to either communicate with each other or communicate with vehicles.
However, before such capabilities become ubiquitous, opportunities exist to
improve traffic lights by being more responsive to current traffic situations
within the current, already deployed, infrastructure. In this paper, we
introduce a traffic light controller that employs bidding within micro-auctions
to efficiently incorporate traffic sensor information; no other outside sources
of information are assumed. We train and test traffic light controllers on
large-scale data collected from opted-in Android cell-phone users over a period
of several months in Mountain View, California and the River North neighborhood
of Chicago, Illinois. The learned auction-based controllers surpass (in both
the relevant metrics of road-capacity and mean travel time) the currently
deployed lights, optimized static-program lights, and longer-term planning
approaches, in both cities, measured using real user driving data.
Fuzzy Clustering Data Given on the Ordinal Scale Based on Membership and Likelihood Functions Sharing
Authors: Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Viktoriia O. Samitova
Date: 2017-02-03 00:00:00
Tasks: Clustering

A task of clustering data given in the ordinal scale under conditions of
overlapping clusters has been considered. It's proposed to use an approach
based on memberhsip and likelihood functions sharing. A number of performed
experiments proved effectiveness of the proposed method. The proposed method is
characterized by robustness to outliers due to a way of ordering values while
constructing membership functions.
Named Entity Evolution Recognition on the Blogosphere
Authors: Helge Holzmann, Nina Tahmasebi, Thomas Risse
Date: 2017-02-03 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Advancements in technology and culture lead to changes in our language. These
changes create a gap between the language known by users and the language
stored in digital archives. It affects user's possibility to firstly find
content and secondly interpret that content. In previous work we introduced our
approach for Named Entity Evolution Recognition~(NEER) in newspaper
collections. Lately, increasing efforts in Web preservation lead to increased
availability of Web archives covering longer time spans. However, language on
the Web is more dynamic than in traditional media and many of the basic
assumptions from the newspaper domain do not hold for Web data. In this paper
we discuss the limitations of existing methodology for NEER. We approach these
by adapting an existing NEER method to work on noisy data like the Web and the
Blogosphere in particular. We develop novel filters that reduce the noise and
make use of Semantic Web resources to obtain more information about terms. Our
evaluation shows the potentials of the proposed approach.
Uncertainty-Aware Reinforcement Learning for Collision Avoidance
Authors: Gregory Kahn, Adam Villaflor, Vitchyr Pong, Pieter Abbeel, Sergey Levine
Date: 2017-02-03 00:00:00
Tasks: Collision Avoidance, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning can enable complex, adaptive behavior to be learned
automatically for autonomous robotic platforms. However, practical deployment
of reinforcement learning methods must contend with the fact that the training
process itself can be unsafe for the robot. In this paper, we consider the
specific case of a mobile robot learning to navigate an a priori unknown
environment while avoiding collisions. In order to learn collision avoidance,
the robot must experience collisions at training time. However, high-speed
collisions, even at training time, could damage the robot. A successful
learning method must therefore proceed cautiously, experiencing only low-speed
collisions until it gains confidence. To this end, we present an
uncertainty-aware model-based learning algorithm that estimates the probability
of collision together with a statistical estimate of uncertainty. By
formulating an uncertainty-dependent cost function, we show that the algorithm
naturally chooses to proceed cautiously in unfamiliar environments, and
increases the velocity of the robot in settings where it has high confidence.
Our predictive model is based on bootstrapped neural networks using dropout,
allowing it to process raw sensory inputs from high-bandwidth sensors such as
cameras. Our experimental evaluation demonstrates that our method effectively
minimizes dangerous collisions at training time in an obstacle avoidance task
for a simulated and real-world quadrotor, and a real-world RC car. Videos of
the experiments can be found at https://sites.google.com/site/probcoll.
Extraction of Evolution Descriptions from the Web
Authors: Helge Holzmann, Thomas Risse
Date: 2017-02-03 00:00:00
Tasks: Information Retrieval, Retrieval

The evolution of named entities affects exploration and retrieval tasks in
digital libraries. An information retrieval system that is aware of name
changes can actively support users in finding former occurrences of evolved
entities. However, current structured knowledge bases, such as DBpedia or
Freebase, do not provide enough information about evolutions, even though the
data is available on their resources, like Wikipedia. Our \emph{Evolution Base}
prototype will demonstrate how excerpts describing name evolutions can be
identified on these websites with a promising precision. The descriptions are
classified by means of models that we trained based on a recent analysis of
named entity evolutions on Wikipedia.
An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation
Authors: Chenhui Chu, Raj Dabre, Sadao Kurohashi
Date: 2017-01-12 00:00:00
Tasks: Domain Adaptation, Machine Translation, NMT, Translation

In this paper, we propose a novel domain adaptation method named "mixed fine
tuning" for neural machine translation (NMT). We combine two existing
approaches namely fine tuning and multi domain NMT. We first train an NMT model
on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus
which is a mix of the in-domain and out-of-domain corpora. All corpora are
augmented with artificial tags to indicate specific domains. We empirically
compare our proposed method against fine tuning and multi domain methods and
discuss its benefits and shortcomings.
Named Entity Evolution Analysis on Wikipedia
Authors: Helge Holzmann, Thomas Risse
Date: 2017-02-03 00:00:00
Tasks: Articles, Information Retrieval, Retrieval

Accessing Web archives raises a number of issues caused by their temporal
characteristics. Additional knowledge is needed to find and understand older
texts. Especially entities mentioned in texts are subject to change. Most
severe in terms of information retrieval are name changes. In order to find
entities that have changed their name over time, search engines need to be
aware of this evolution. We tackle this problem by analyzing Wikipedia in terms
of entity evolutions mentioned in articles. We present statistical data on
excerpts covering name changes, which will be used to discover similar text
passages and extract evolution knowledge in future work.