pwc-archive/papers-with-abstracts huggingface dataset:
What's Mine is Yours: Pretrained CNNs for Limited Training Sonar ATR
Authors: John McKay, Isaac Gerg, Vishal Monga, Raghu Raj
Date: 2017-06-29 00:00:00
Tasks: Transfer Learning

Finding mines in Sonar imagery is a significant problem with a great deal of
relevance for seafaring military and commercial endeavors. Unfortunately, the
lack of enormous Sonar image data sets has prevented automatic target
recognition (ATR) algorithms from some of the same advances seen in other
computer vision fields. Namely, the boom in convolutional neural nets (CNNs)
which have been able to achieve incredible results - even surpassing human
actors - has not been an easily feasible route for many practitioners of Sonar
ATR. We demonstrate the power of one avenue to incorporating CNNs into Sonar
ATR: transfer learning. We first show how well a straightforward, flexible CNN
feature-extraction strategy can be used to obtain impressive if not
state-of-the-art results. Secondly, we propose a way to utilize the powerful
transfer learning approach towards multiple instance target detection and
identification within a provided synthetic aperture Sonar data set.
Automatic Mapping of French Discourse Connectives to PDTB Discourse Relations
Authors: Majid Laali, Leila Kosseim
Date: 2017-06-29 00:00:00
Proceeding: WS 2017 8
Tasks: Machine Translation, Translation

In this paper, we present an approach to exploit phrase tables generated by
statistical machine translation in order to map French discourse connectives to
discourse relations. Using this approach, we created ConcoLeDisCo, a lexicon of
French discourse connectives and their PDTB relations. When evaluated against
LEXCONN, ConcoLeDisCo achieves a recall of 0.81 and an Average Precision of
0.68 for the Concession and Condition relations.
Fair Algorithms for Infinite and Contextual Bandits
Authors: Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
Date: 2016-10-29 00:00:00
Tasks: Fairness, Multi-Armed Bandits

We study fairness in linear bandit problems. Starting from the notion of
meritocratic fairness introduced in Joseph et al. [2016], we carry out a more
refined analysis of a more general problem, achieving better performance
guarantees with fewer modelling assumptions on the number and structure of
available choices as well as the number selected. We also analyze the
previously-unstudied question of fairness in infinite linear bandit problems,
obtaining instance-dependent regret upper bounds as well as lower bounds
demonstrating that this instance-dependence is necessary. The result is a
framework for meritocratic fairness in an online linear setting that is
substantially more powerful, general, and realistic than the current state of
the art.
Robust Face Tracking using Multiple Appearance Models and Graph Relational Learning
Authors: Tanushri Chakravorty, Guillaume-Alexandre Bilodeau, Eric Granger
Date: 2017-06-29 00:00:00
Tasks: Relational Reasoning

This paper addresses the problem of appearance matching across different
challenges while doing visual face tracking in real-world scenarios. In this
paper, FaceTrack is proposed that utilizes multiple appearance models with its
long-term and short-term appearance memory for efficient face tracking. It
demonstrates robustness to deformation, in-plane and out-of-plane rotation,
scale, distractors and background clutter. It capitalizes on the advantages of
the tracking-by-detection, by using a face detector that tackles drastic scale
appearance change of a face. The detector also helps to reinitialize FaceTrack
during drift. A weighted score-level fusion strategy is proposed to obtain the
face tracking output having the highest fusion score by generating candidates
around possible face locations. The tracker showcases impressive performance
when initiated automatically by outperforming many state-of-the-art trackers,
except Struck by a very minute margin: 0.001 in precision and 0.017 in success
respectively.
Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation
Authors: Shikhar Sharma, Layla El Asri, Hannes Schulz, Jeremie Zumer
Date: 2017-06-29 00:00:00
Proceeding: ICLR 2018 1
Tasks: Dialogue Generation, Diversity, Machine Translation, Response Generation, Sentence, Text Generation, Translation

Automated metrics such as BLEU are widely used in the machine translation
literature. They have also been used recently in the dialogue community for
evaluating dialogue response generation. However, previous work in dialogue
response generation has shown that these metrics do not correlate strongly with
human judgment in the non task-oriented dialogue setting. Task-oriented
dialogue responses are expressed on narrower domains and exhibit lower
diversity. It is thus reasonable to think that these automated metrics would
correlate well with human judgment in the task-oriented setting where the
generation task consists of translating dialogue acts into a sentence. We
conduct an empirical study to confirm whether this is the case. Our findings
indicate that these automated metrics have stronger correlation with human
judgments in the task-oriented setting compared to what has been observed in
the non task-oriented setting. We also observe that these metrics correlate
even better for datasets which provide multiple ground truth reference
sentences. In addition, we show that some of the currently available corpora
for task-oriented language generation can be solved with simple models and
advocate for more challenging datasets.
Feature uncertainty bounding schemes for large robust nonlinear SVM classifiers
Authors: Nicolas Couellan, Sophie Jan
Date: 2017-06-29 00:00:00
Tasks: Binary Classification, General Classification

We consider the binary classification problem when data are large and subject
to unknown but bounded uncertainties. We address the problem by formulating the
nonlinear support vector machine training problem with robust optimization. To
do so, we analyze and propose two bounding schemes for uncertainties associated
to random approximate features in low dimensional spaces. The proposed
techniques are based on Random Fourier Features and the Nystr\"om methods. The
resulting formulations can be solved with efficient stochastic approximation
techniques such as stochastic (sub)-gradient, stochastic proximal gradient
techniques or their variants.
Recurrent Additive Networks
Authors: Kenton Lee, Omer Levy, Luke Zettlemoyer
Date: 2017-05-21 00:00:00
Tasks: Language Modeling, Language Modelling

We introduce recurrent additive networks (RANs), a new gated RNN which is
distinguished by the use of purely additive latent state updates. At every time
step, the new state is computed as a gated component-wise sum of the input and
the previous state, without any of the non-linearities commonly used in RNN
transition dynamics. We formally show that RAN states are weighted sums of the
input vectors, and that the gates only contribute to computing the weights of
these sums. Despite this relatively simple functional form, experiments
demonstrate that RANs perform on par with LSTMs on benchmark language modeling
problems. This result shows that many of the non-linear computations in LSTMs
and related networks are not essential, at least for the problems we consider,
and suggests that the gates are doing more of the computational work than
previously understood.
Improving Speech Related Facial Action Unit Recognition by Audiovisual Information Fusion
Authors: Zibo Meng, Shizhong Han, Ping Liu, Yan Tong
Date: 2017-06-29 00:00:00
Tasks: Facial Action Unit Detection

It is challenging to recognize facial action unit (AU) from spontaneous
facial displays, especially when they are accompanied by speech. The major
reason is that the information is extracted from a single source, i.e., the
visual channel, in the current practice. However, facial activity is highly
correlated with voice in natural human communications.
  Instead of solely improving visual observations, this paper presents a novel
audiovisual fusion framework, which makes the best use of visual and acoustic
cues in recognizing speech-related facial AUs. In particular, a dynamic
Bayesian network (DBN) is employed to explicitly model the semantic and dynamic
physiological relationships between AUs and phonemes as well as measurement
uncertainty. A pilot audiovisual AU-coded database has been collected to
evaluate the proposed framework, which consists of a "clean" subset containing
frontal faces under well controlled circumstances and a challenging subset with
large head movements and occlusions. Experiments on this database have
demonstrated that the proposed framework yields significant improvement in
recognizing speech-related AUs compared to the state-of-the-art visual-based
methods especially for those AUs whose visual observations are impaired during
speech, and more importantly also outperforms feature-level fusion methods by
explicitly modeling and exploiting physiological relationships between AUs and
phonemes.
Speaker Identification in each of the Neutral and Shouted Talking Environments based on Gender-Dependent Approach Using SPHMMs
Authors: Ismail Shahin
Date: 2017-06-29 00:00:00
Tasks: Speaker Identification

It is well known that speaker identification performs extremely well in the
neutral talking environments; however, the identification performance is
declined sharply in the shouted talking environments. This work aims at
proposing, implementing and testing a new approach to enhance the declined
performance in the shouted talking environments. The new proposed approach is
based on gender-dependent speaker identification using Suprasegmental Hidden
Markov Models (SPHMMs) as classifiers. This proposed approach has been tested
on two different and separate speech databases: our collected database and the
Speech Under Simulated and Actual Stress (SUSAS) database. The results of this
work show that gender-dependent speaker identification based on SPHMMs
outperforms gender-independent speaker identification based on the same models
and gender-dependent speaker identification based on Hidden Markov Models
(HMMs) by about 6% and 8%, respectively. The results obtained based on the
proposed approach are close to those obtained in subjective evaluation by human
judges.
Dynamical selection of Nash equilibria using Experience Weighted Attraction Learning: emergence of heterogeneous mixed equilibria
Authors: Robin Nicole, Peter Sollich
Date: 2017-06-29 00:00:00
Tasks: Imputation

We study the distribution of strategies in a large game that models how
agents choose among different double auction markets. We classify the possible
mean field Nash equilibria, which include potentially segregated states where
an agent population can split into subpopulations adopting different
strategies. As the game is aggregative, the actual equilibrium strategy
distributions remain undetermined, however. We therefore compare with the
results of Experience-Weighted Attraction (EWA) learning, which at long times
leads to Nash equilibria in the appropriate limits of large intensity of
choice, low noise (long agent memory) and perfect imputation of missing scores
(fictitious play). The learning dynamics breaks the indeterminacy of the Nash
equilibria. Non-trivially, depending on how the relevant limits are taken, more
than one type of equilibrium can be selected. These include the standard
homogeneous mixed and heterogeneous pure states, but also \emph{heterogeneous
mixed} states where different agents play different strategies that are not all
pure. The analysis of the EWA learning involves Fokker-Planck modeling combined
with large deviation methods. The theoretical results are confirmed by
multi-agent simulations.
Bayesian Semisupervised Learning with Deep Generative Models
Authors: Jonathan Gordon, José Miguel Hernández-Lobato
Date: 2017-06-29 00:00:00
Tasks: Active Learning, Missing Labels

Neural network based generative models with discriminative components are a
powerful approach for semi-supervised learning. However, these techniques a)
cannot account for model uncertainty in the estimation of the model's
discriminative component and b) lack flexibility to capture complex stochastic
patterns in the label generation process. To avoid these problems, we first
propose to use a discriminative component with stochastic inputs for increased
noise flexibility. We show how an efficient Gibbs sampling procedure can
marginalize the stochastic inputs when inferring missing labels in this model.
Following this, we extend the discriminative component to be fully Bayesian and
produce estimates of uncertainty in its parameter values. This opens the door
for semi-supervised Bayesian active learning.
Indoor UAV scheduling with Restful Task Assignment Algorithm
Authors: Yohanes Khosiawan, Izabela Nielsen
Date: 2017-06-29 00:00:00
Tasks: Scheduling

Research in UAV scheduling has obtained an emerging interest from scientists
in the optimization field. When the scheduling itself has established a strong
root since the 19th century, works on UAV scheduling in indoor environment has
come forth in the latest decade. Several works on scheduling UAV operations in
indoor (two and three dimensional) and outdoor environments are reported. In
this paper, a further study on UAV scheduling in three dimensional indoor
environment is investigated. Dealing with indoor environment\textemdash where
humans, UAVs, and other elements or infrastructures are likely to coexist in
the same space\textemdash draws attention towards the safety of the operations.
In relation to the battery level, a preserved battery level leads to safer
operations, promoting the UAV to have a decent remaining power level. A
methodology which consists of a heuristic approach based on Restful Task
Assignment Algorithm, incorporated with Particle Swarm Optimization Algorithm,
is proposed. The motivation is to preserve the battery level throughout the
operations, which promotes less possibility in having failed UAVs on duty. This
methodology is tested with 54 benchmark datasets stressing on 4 different
aspects: geographical distance, number of tasks, number of predecessors, and
slack time. The test results and their characteristics in regard to the
proposed methodology are discussed and presented.
Stronger Baselines for Trustable Results in Neural Machine Translation
Authors: Michael Denkowski, Graham Neubig
Date: 2017-06-29 00:00:00
Proceeding: WS 2017 8
Tasks: Machine Translation, NMT, Translation

Interest in neural machine translation has grown rapidly as its effectiveness
has been demonstrated across language and data scenarios. New research
regularly introduces architectural and algorithmic improvements that lead to
significant gains over "vanilla" NMT implementations. However, these new
techniques are rarely evaluated in the context of previously published
techniques, specifically those that are widely used in state-of-theart
production and shared-task systems. As a result, it is often difficult to
determine whether improvements from research will carry over to systems
deployed for real-world use. In this work, we recommend three specific methods
that are relatively easy to implement and result in much stronger experimental
systems. Beyond reporting significantly higher BLEU scores, we conduct an
in-depth analysis of where improvements originate and what inherent weaknesses
of basic NMT models are being addressed. We then compare the relative gains
afforded by several other techniques proposed in the literature when starting
with vanilla systems versus our stronger baselines, showing that experimental
conclusions may change depending on the baseline chosen. This indicates that
choosing a strong baseline is crucial for reporting reliable experimental
results.
Iterative Spectral Clustering for Unsupervised Object Localization
Authors: Aditya Vora, Shanmuganathan Raman
Date: 2017-06-29 00:00:00
Tasks: Clustering, Object, Object Discovery, Object Localization, Unsupervised Object Localization

This paper addresses the problem of unsupervised object localization in an
image. Unlike previous supervised and weakly supervised algorithms that require
bounding box or image level annotations for training classifiers in order to
learn features representing the object, we propose a simple yet effective
technique for localization using iterative spectral clustering. This iterative
spectral clustering approach along with appropriate cluster selection strategy
in each iteration naturally helps in searching of object region in the image.
In order to estimate the final localization window, we group the proposals
obtained from the iterative spectral clustering step based on the perceptual
similarity, and average the coordinates of the proposals from the top scoring
groups. We benchmark our algorithm on challenging datasets like Object
Discovery and PASCAL VOC 2007, achieving an average CorLoc percentage of 51%
and 35% respectively which is comparable to various other weakly supervised
algorithms despite being completely unsupervised.
Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data
Authors: Karl Øyvind Mikalsen, Filippo Maria Bianchi, Cristina Soguero-Ruiz, Robert Jenssen
Date: 2017-04-03 00:00:00
Tasks: Clustering, Ensemble Learning, Time Series, Time Series Analysis

Similarity-based approaches represent a promising direction for time series
analysis. However, many such methods rely on parameter tuning, and some have
shortcomings if the time series are multivariate (MTS), due to dependencies
between attributes, or the time series contain missing data. In this paper, we
address these challenges within the powerful context of kernel methods by
proposing the robust \emph{time series cluster kernel} (TCK). The approach
taken leverages the missing data handling properties of Gaussian mixture models
(GMM) augmented with informative prior distributions. An ensemble learning
approach is exploited to ensure robustness to parameters by combining the
clustering results of many GMM to form the final kernel.
  We evaluate the TCK on synthetic and real data and compare to other
state-of-the-art techniques. The experimental results demonstrate that the TCK
is robust to parameter choices, provides competitive results for MTS without
missing data and outstanding results for missing data.
Image classification using local tensor singular value decompositions
Authors: Elizabeth Newman, Misha Kilmer, Lior Horesh
Date: 2017-06-29 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Object Recognition, Position, tensor algebra

From linear classifiers to neural networks, image classification has been a
widely explored topic in mathematics, and many algorithms have proven to be
effective classifiers. However, the most accurate classifiers typically have
significantly high storage costs, or require complicated procedures that may be
computationally expensive. We present a novel (nonlinear) classification
approach using truncation of local tensor singular value decompositions (tSVD)
that robustly offers accurate results, while maintaining manageable storage
costs. Our approach takes advantage of the optimality of the representation
under the tensor algebra described to determine to which class an image
belongs. We extend our approach to a method that can determine specific
pairwise match scores, which could be useful in, for example, object
recognition problems where pose/position are different. We demonstrate the
promise of our new techniques on the MNIST data set.
Tensor-based approach to accelerate deformable part models
Authors: D. V. Parkhomenko, I. L. Mazurenko
Date: 2017-06-29 00:00:00
Tasks: Tensor Decomposition

This article provides next step towards solving speed bottleneck of any
system that intensively uses convolutions operations (e.g. CNN). Method
described in the article is applied on deformable part models (DPM) algorithm.
Method described here is based on multidimensional tensors and provides
efficient tradeoff between DPM performance and accuracy. Experiments on various
databases, including Pascal VOC, show that the proposed method allows
decreasing a number of convolutions up to 4.5 times compared with DPM v.5,
while maintaining similar accuracy. If insignificant accuracy degradation is
allowable, higher computational gain can be achieved. The method consists of
filters tensor decomposition and convolutions shortening using the decomposed
filter. Mathematical overview of the proposed method as well as simulation
results are provided.
Improving Distributed Representations of Tweets - Present and Future
Authors: Ganesh J
Date: 2017-06-29 00:00:00
Tasks: Representation Learning, Sentiment Analysis

Unsupervised representation learning for tweets is an important research
field which helps in solving several business applications such as sentiment
analysis, hashtag prediction, paraphrase detection and microblog ranking. A
good tweet representation learning model must handle the idiosyncratic nature
of tweets which poses several challenges such as short length, informal words,
unusual grammar and misspellings. However, there is a lack of prior work which
surveys the representation learning models with a focus on tweets. In this
work, we organize the models based on its objective function which aids the
understanding of the literature. We also provide interesting future directions,
which we believe are fruitful in advancing this field by building high-quality
tweet representation learning models.
Co-salient Object Detection Based on Deep Saliency Networks and Seed Propagation over an Integrated Graph
Authors: Dong-ju Jeong, Insung Hwang, Nam Ik Cho
Date: 2017-06-29 00:00:00
Tasks: Co-Salient Object Detection, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection, Weakly-supervised Learning

This paper presents a co-salient object detection method to find common
salient regions in a set of images. We utilize deep saliency networks to
transfer co-saliency prior knowledge and better capture high-level semantic
information, and the resulting initial co-saliency maps are enhanced by seed
propagation steps over an integrated graph. The deep saliency networks are
trained in a supervised manner to avoid online weakly supervised learning and
exploit them not only to extract high-level features but also to produce both
intra- and inter-image saliency maps. Through a refinement step, the initial
co-saliency maps can uniformly highlight co-salient regions and locate accurate
object boundaries. To handle input image groups inconsistent in size, we
propose to pool multi-regional descriptors including both within-segment and
within-group information. In addition, the integrated multilayer graph is
constructed to find the regions that the previous steps may not detect by seed
propagation with low-level descriptors. In this work, we utilize the useful
complementary components of high-, low-level information, and several
learning-based steps. Our experiments have demonstrated that the proposed
approach outperforms comparable co-saliency detection methods on widely used
public databases and can also be directly applied to co-segmentation tasks.