pwc-archive/papers-with-abstracts huggingface dataset:
Learning from Comparisons and Choices
Authors: Sahand Negahban, Sewoong Oh, Kiran K. Thekumparampil, Jiaming Xu
Date: 2017-04-24 00:00:00
Tasks: Articles, Marketing, Recommendation Systems

When tracking user-specific online activities, each user's preference is
revealed in the form of choices and comparisons. For example, a user's purchase
history is a record of her choices, i.e. which item was chosen among a subset
of offerings. A user's preferences can be observed either explicitly as in
movie ratings or implicitly as in viewing times of news articles. Given such
individualized ordinal data in the form of comparisons and choices, we address
the problem of collaboratively learning representations of the users and the
items. The learned features can be used to predict a user's preference of an
unseen item to be used in recommendation systems. This also allows one to
compute similarities among users and items to be used for categorization and
search. Motivated by the empirical successes of the MultiNomial Logit (MNL)
model in marketing and transportation, and also more recent successes in word
embedding and crowdsourced image embedding, we pose this problem as learning
the MNL model parameters that best explain the data. We propose a convex
relaxation for learning the MNL model, and show that it is minimax optimal up
to a logarithmic factor by comparing its performance to a fundamental lower
bound. This characterizes the minimax sample complexity of the problem, and
proves that the proposed estimator cannot be improved upon other than by a
logarithmic factor. Further, the analysis identifies how the accuracy depends
on the topology of sampling via the spectrum of the sampling graph. This
provides a guideline for designing surveys when one can choose which items are
to be compared. This is accompanied by numerical simulations on synthetic and
real data sets, confirming our theoretical predictions.
Entropic Trace Estimates for Log Determinants
Authors: Jack Fitzsimons, Diego Granziol, Kurt Cutajar, Michael Osborne, Maurizio Filippone, Stephen Roberts
Date: 2017-04-24 00:00:00
Tasks: Gaussian Processes, Point Processes

The scalable calculation of matrix determinants has been a bottleneck to the
widespread application of many machine learning methods such as determinantal
point processes, Gaussian processes, generalised Markov random fields, graph
models and many others. In this work, we estimate log determinants under the
framework of maximum entropy, given information in the form of moment
constraints from stochastic trace estimation. The estimates demonstrate a
significant improvement on state-of-the-art alternative methods, as shown on a
wide variety of UFL sparse matrices. By taking the example of a general Markov
random field, we also demonstrate how this approach can significantly
accelerate inference in large-scale learning methods involving the log
determinant.
Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM
Authors: Elena Kochkina, Maria Liakata, Isabelle Augenstein
Date: 2017-04-24 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Rumour Detection, Stance Classification, Stance Detection

This paper describes team Turing's submission to SemEval 2017 RumourEval:
Determining rumour veracity and support for rumours (SemEval 2017 Task 8,
Subtask A). Subtask A addresses the challenge of rumour stance classification,
which involves identifying the attitude of Twitter users towards the
truthfulness of the rumour they are discussing. Stance classification is
considered to be an important step towards rumour verification, therefore
performing well in this task is expected to be useful in debunking false
rumours. In this work we classify a set of Twitter posts discussing rumours
into either supporting, denying, questioning or commenting on the underlying
rumours. We propose a LSTM-based sequential model that, through modelling the
conversational structure of tweets, which achieves an accuracy of 0.784 on the
RumourEval test set outperforming all other systems in Subtask A.
Predicting membrane protein contacts from non-membrane proteins by deep transfer learning
Authors: Zhen Li, Sheng Wang, Yizhou Yu, Jinbo Xu
Date: 2017-04-24 00:00:00
Tasks: Prediction, Transfer Learning

Computational prediction of membrane protein (MP) structures is very
challenging partially due to lack of sufficient solved structures for homology
modeling. Recently direct evolutionary coupling analysis (DCA) sheds some light
on protein contact prediction and accordingly, contact-assisted folding, but
DCA is effective only on some very large-sized families since it uses
information only in a single protein family. This paper presents a deep
transfer learning method that can significantly improve MP contact prediction
by learning contact patterns and complex sequence-contact relationship from
thousands of non-membrane proteins (non-MPs). Tested on 510 non-redundant MPs,
our deep model (learned from only non-MPs) has top L/10 long-range contact
prediction accuracy 0.69, better than our deep model trained by only MPs (0.63)
and much better than a representative DCA method CCMpred (0.47) and the CASP11
winner MetaPSICOV (0.55). The accuracy of our deep model can be further
improved to 0.72 when trained by a mix of non-MPs and MPs. When only contacts
in transmembrane regions are evaluated, our method has top L/10 long-range
accuracy 0.62, 0.57, and 0.53 when trained by a mix of non-MPs and MPs, by
non-MPs only, and by MPs only, respectively, still much better than MetaPSICOV
(0.45) and CCMpred (0.40). All these results suggest that sequence-structure
relationship learned by our deep model from non-MPs generalizes well to MP
contact prediction. Improved contact prediction also leads to better
contact-assisted folding. Using only top predicted contacts as restraints, our
deep learning method can fold 160 and 200 of 510 MPs with TMscore>0.6 when
trained by non-MPs only and by a mix of non-MPs and MPs, respectively, while
CCMpred and MetaPSICOV can do so for only 56 and 77 MPs, respectively. Our
contact-assisted folding also greatly outperforms homology modeling.
Reinforcement Learning Based Dynamic Selection of Auxiliary Objectives with Preserving of the Best Found Solution
Authors: Irina Petrova, Arina Buzdalova
Date: 2017-04-24 00:00:00
Tasks: Reinforcement Learning

Efficiency of single-objective optimization can be improved by introducing
some auxiliary objectives. Ideally, auxiliary objectives should be helpful.
However, in practice, objectives may be efficient on some optimization stages
but obstructive on others. In this paper we propose a modification of the EA+RL
method which dynamically selects optimized objectives using reinforcement
learning. The proposed modification prevents from losing the best found
solution. We analysed the proposed modification and compared it with the EA+RL
method and Random Local Search on XdivK, Generalized OneMax and LeadingOnes
problems. The proposed modification outperforms the EA+RL method on all problem
instances. It also outperforms the single objective approach on the most
problem instances. We also provide detailed analysis of how different
components of the considered algorithms influence efficiency of optimization.
In addition, we present theoretical analysis of the proposed modification on
the XdivK problem.
Stochastic Constraint Programming as Reinforcement Learning
Authors: Steven Prestwich, Roberto Rossi, Armagan Tarim
Date: 2017-04-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Stochastic Constraint Programming (SCP) is an extension of Constraint
Programming (CP) used for modelling and solving problems involving constraints
and uncertainty. SCP inherits excellent modelling abilities and filtering
algorithms from CP, but so far it has not been applied to large problems.
Reinforcement Learning (RL) extends Dynamic Programming to large stochastic
problems, but is problem-specific and has no generic solvers. We propose a
hybrid combining the scalability of RL with the modelling and constraint
filtering methods of CP. We implement a prototype in a CP system and
demonstrate its usefulness on SCP problems.
Autonomous Braking System via Deep Reinforcement Learning
Authors: Hyunmin Chae, Chang Mook Kang, ByeoungDo Kim, Jaekyum Kim, Chung Choo Chung, Jun Won Choi
Date: 2017-02-08 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we propose a new autonomous braking system based on deep
reinforcement learning. The proposed autonomous braking system automatically
decides whether to apply the brake at each time step when confronting the risk
of collision using the information on the obstacle obtained by the sensors. The
problem of designing brake control is formulated as searching for the optimal
policy in Markov decision process (MDP) model where the state is given by the
relative position of the obstacle and the vehicle's speed, and the action space
is defined as whether brake is stepped or not. The policy used for brake
control is learned through computer simulations using the deep reinforcement
learning method called deep Q-network (DQN). In order to derive desirable
braking policy, we propose the reward function which balances the damage
imposed to the obstacle in case of accident and the reward achieved when the
vehicle runs out of risk as soon as possible. DQN is trained for the scenario
where a vehicle is encountered with a pedestrian crossing the urban road.
Experiments show that the control agent exhibits desirable control behavior and
avoids collision without any mistake in various uncertain environments.
On the Complexity of Constrained Determinantal Point Processes
Authors: L. Elisa Celis, Amit Deshpande, Tarun Kathuria, Damian Straszak, Nisheeth K. Vishnoi
Date: 2016-08-01 00:00:00
Tasks: Fairness, Point Processes

Determinantal Point Processes (DPPs) are probabilistic models that arise in
quantum physics and random matrix theory and have recently found numerous
applications in computer science. DPPs define distributions over subsets of a
given ground set, they exhibit interesting properties such as negative
correlation, and, unlike other models, have efficient algorithms for sampling.
When applied to kernel methods in machine learning, DPPs favor subsets of the
given data with more diverse features. However, many real-world applications
require efficient algorithms to sample from DPPs with additional constraints on
the subset, e.g., partition or matroid constraints that are important to ensure
priors, resource or fairness constraints on the sampled subset. Whether one can
efficiently sample from DPPs in such constrained settings is an important
problem that was first raised in a survey of DPPs by \cite{KuleszaTaskar12} and
studied in some recent works in the machine learning literature.
  The main contribution of our paper is the first resolution of the complexity
of sampling from DPPs with constraints. We give exact efficient algorithms for
sampling from constrained DPPs when their description is in unary. Furthermore,
we prove that when the constraints are specified in binary, this problem is
#P-hard via a reduction from the problem of computing mixed discriminants
implying that it may be unlikely that there is an FPRAS. Our results benefit
from viewing the constrained sampling problem via the lens of polynomials.
Consequently, we obtain a few algorithms of independent interest: 1) to count
over the base polytope of regular matroids when there are additional (succinct)
budget constraints and, 2) to evaluate and compute the mixed characteristic
polynomials, that played a central role in the resolution of the Kadison-Singer
problem, for certain special cases.
Using a new parsimonious AHP methodology combined with the Choquet integral: An application for evaluating social housing initiatives
Authors: Francesca Abastante, Salvatore Corrente, Salvatore Greco, Alessio Ishizaka, Isabella Lami
Date: 2017-04-24 00:00:00

We propose a development of the Analytic Hierarchy Process (AHP) permitting
to use the methodology also in cases of decision problems with a very large
number of alternatives evaluated with respect to several criteria. While the
application of the original AHP method involves many pairwise comparisons
between alternatives and criteria, our proposal is composed of three steps: (i)
direct evaluation of the alternatives at hand on the considered criteria, (ii)
selection of some reference evaluations; (iii) application of the original AHP
method to reference evaluations; (iv) revision of the direct evaluation on the
basis of the prioritization supplied by AHP on reference evaluations. The new
proposal has been tested and validated in an experiment conducted on a sample
of university students. The new methodology has been therefore applied to a
real world problem involving the evaluation of 21 Social Housing initiatives
sited in the Piedmont region (Italy). To take into account interaction between
criteria, the Choquet integral preference model has been considered within a
Non Additive Robust Ordinal Regression approach.
Towards crowdsourcing and cooperation in linguistic resources
Authors: Dmitry Ustalov
Date: 2014-08-19 00:00:00

Linguistic resources can be populated with data through the use of such
approaches as crowdsourcing and gamification when motivated people are
involved. However, current crowdsourcing genre taxonomies lack the concept of
cooperation, which is the principal element of modern video games and may
potentially drive the annotators' interest. This survey on crowdsourcing
taxonomies and cooperation in linguistic resources provides recommendations on
using cooperation in existent genres of crowdsourcing and an evidence of the
efficiency of cooperation using a popular Russian linguistic resource created
through crowdsourcing as an example.
We don't need no bounding-boxes: Training object class detectors using only human verification
Authors: Dim P. Papadopoulos, Jasper R. R. Uijlings, Frank Keller, Vittorio Ferrari
Date: 2016-02-26 00:00:00
Proceeding: CVPR 2016 6

Training object class detectors typically requires a large set of images in
which objects are annotated by bounding-boxes. However, manually drawing
bounding-boxes is very time consuming. We propose a new scheme for training
object detectors which only requires annotators to verify bounding-boxes
produced automatically by the learning algorithm. Our scheme iterates between
re-training the detector, re-localizing objects in the training images, and
human verification. We use the verification signal both to improve re-training
and to reduce the search space for re-localisation, which makes these steps
different to what is normally done in a weakly supervised setting. Extensive
experiments on PASCAL VOC 2007 show that (1) using human verification to update
detectors and reduce the search space leads to the rapid production of
high-quality bounding-box annotations; (2) our scheme delivers detectors
performing almost as good as those trained in a fully supervised setting,
without ever drawing any bounding-box; (3) as the verification task is very
quick, our scheme substantially reduces total annotation time by a factor
6x-9x.
Monocular Visual Odometry with a Rolling Shutter Camera
Authors: Chang-Ryeol Lee, Kuk-Jin Yoon
Date: 2017-04-24 00:00:00
Tasks: Monocular Visual Odometry, motion prediction, Visual Odometry

Rolling Shutter (RS) cameras have become popularized because of low-cost
imaging capability. However, the RS cameras suffer from undesirable artifacts
when the camera or the subject is moving, or illumination condition changes.
For that reason, Monocular Visual Odometry (MVO) with RS cameras produces
inaccurate ego-motion estimates. Previous works solve this RS distortion
problem with motion prediction from images and/or inertial sensors. However,
the MVO still has trouble in handling the RS distortion when the camera motion
changes abruptly (e.g. vibration of mobile cameras causes extremely fast motion
instantaneously). To address the problem, we propose the novel MVO algorithm in
consideration of the geometric characteristics of RS cameras. The key idea of
the proposed algorithm is the new RS essential matrix which incorporates the
instantaneous angular and linear velocities at each frame. Our algorithm
produces accurate and robust ego-motion estimates in an online manner, and is
applicable to various mobile applications with RS cameras. The superiority of
the proposed algorithm is validated through quantitative and qualitative
comparison on both synthetic and real dataset.
Watset: Automatic Induction of Synsets from a Graph of Synonyms
Authors: Dmitry Ustalov, Alexander Panchenko, Chris Biemann
Date: 2017-04-24 00:00:00
Proceeding: ACL 2017 7
Tasks: Clustering, Word Embeddings, Word Sense Induction

This paper presents a new graph-based approach that induces synsets using
synonymy dictionaries and word embeddings. First, we build a weighted graph of
synonyms extracted from commonly available resources, such as Wiktionary.
Second, we apply word sense induction to deal with ambiguous words. Finally, we
cluster the disambiguated version of the ambiguous input graph into synsets.
Our meta-clustering approach lets us use an efficient hard clustering algorithm
to perform a fuzzy clustering of the graph. Despite its simplicity, our
approach shows excellent results, outperforming five competitive
state-of-the-art methods in terms of F-score on three gold standard datasets
for English and Russian derived from large-scale manually constructed lexical
resources.
Semi-supervised Multitask Learning for Sequence Labeling
Authors: Marek Rei
Date: 2017-04-24 00:00:00
Proceeding: ACL 2017 7
Tasks: Chunking, Grammatical Error Detection, Language Modeling, Language Modelling, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging, POS, POS Tagging

We propose a sequence labeling framework with a secondary training objective,
learning to predict surrounding words for every word in the dataset. This
language modeling objective incentivises the system to learn general-purpose
patterns of semantic and syntactic composition, which are also useful for
improving accuracy on different sequence labeling tasks. The architecture was
evaluated on a range of datasets, covering the tasks of error detection in
learner texts, named entity recognition, chunking and POS-tagging. The novel
language modeling objective provided consistent performance improvements on
every benchmark, without requiring any additional annotated or unannotated
data.
A Neural Network model with Bidirectional Whitening
Authors: Yuki Fujimoto, Toru Ohira
Date: 2017-04-24 00:00:00
Tasks: model

We present here a new model and algorithm which performs an efficient Natural
gradient descent for Multilayer Perceptrons. Natural gradient descent was
originally proposed from a point of view of information geometry, and it
performs the steepest descent updates on manifolds in a Riemannian space. In
particular, we extend an approach taken by the "Whitened neural networks"
model. We make the whitening process not only in feed-forward direction as in
the original model, but also in the back-propagation phase. Its efficacy is
shown by an application of this "Bidirectional whitened neural networks" model
to a handwritten character recognition data (MNIST data).
Found in Translation: Reconstructing Phylogenetic Language Trees from Translations
Authors: Ella Rabinovich, Noam Ordan, Shuly Wintner
Date: 2017-04-24 00:00:00
Proceeding: ACL 2017 7
Tasks: Translation

Translation has played an important role in trade, law, commerce, politics,
and literature for thousands of years. Translators have always tried to be
invisible; ideal translations should look as if they were written originally in
the target language. We show that traces of the source language remain in the
translation product to the extent that it is possible to uncover the history of
the source language by looking only at the translation. Specifically, we
automatically reconstruct phylogenetic language trees from monolingual texts
(translated from several source languages). The signal of the source language
is so powerful that it is retained even after two phases of translation. This
strongly indicates that source language interference is the most dominant
characteristic of translated texts, overshadowing the more subtle signals of
universal properties of translation.
Dense 3D Facial Reconstruction from a Single Depth Image in Unconstrained Environment
Authors: Shu Zhang, Hui Yu, Ting Wang, Junyu Dong, Honghai Liu
Date: 2017-04-24 00:00:00

With the increasing demands of applications in virtual reality such as 3D
films, virtual Human-Machine Interactions and virtual agents, the analysis of
3D human face analysis is considered to be more and more important as a
fundamental step for those virtual reality tasks. Due to information provided
by an additional dimension, 3D facial reconstruction enables aforementioned
tasks to be achieved with higher accuracy than those based on 2D facial
analysis. The denser the 3D facial model is, the more information it could
provide. However, most existing dense 3D facial reconstruction methods require
complicated processing and high system cost. To this end, this paper presents a
novel method that simplifies the process of dense 3D facial reconstruction by
employing only one frame of depth data obtained with an off-the-shelf RGB-D
sensor. The experiments showed competitive results with real world data.
An Aposteriorical Clusterability Criterion for $k$-Means++ and Simplicity of Clustering
Authors: Mieczysław A. Kłopotek
Date: 2017-04-24 00:00:00
Tasks: Clustering, Common Sense Reasoning

We define the notion of a well-clusterable data set combining the point of
view of the objective of $k$-means clustering algorithm (minimising the centric
spread of data elements) and common sense (clusters shall be separated by
gaps). We identify conditions under which the optimum of $k$-means objective
coincides with a clustering under which the data is separated by predefined
gaps.
  We investigate two cases: when the whole clusters are separated by some gap
and when only the cores of the clusters meet some separation condition.
  We overcome a major obstacle in using clusterability criteria due to the fact
that known approaches to clusterability checking had the disadvantage that they
are related to the optimal clustering which is NP hard to identify.
  Compared to other approaches to clusterability, the novelty consists in the
possibility of an a posteriori (after running $k$-means) check if the data set
is well-clusterable or not. As the $k$-means algorithm applied for this purpose
has polynomial complexity so does therefore the appropriate check.
Additionally, if $k$-means++ fails to identify a clustering that meets
clusterability criteria, with high probability the data is not
well-clusterable.
A Network-based End-to-End Trainable Task-oriented Dialogue System
Authors: Tsung-Hsien Wen, David Vandyke, Nikola Mrksic, Milica Gasic, Lina M. Rojas-Barahona, Pei-Hao Su, Stefan Ultes, Steve Young
Date: 2016-04-15 00:00:00
Proceeding: EACL 2017 4
Tasks: Task-Oriented Dialogue Systems

Teaching machines to accomplish tasks by conversing naturally with humans is
challenging. Currently, developing task-oriented dialogue systems requires
creating multiple components and typically this involves either a large amount
of handcrafting, or acquiring costly labelled datasets to solve a statistical
learning problem for each component. In this work we introduce a neural
network-based text-in, text-out end-to-end trainable goal-oriented dialogue
system along with a new way of collecting dialogue data based on a novel
pipe-lined Wizard-of-Oz framework. This approach allows us to develop dialogue
systems easily and without making too many assumptions about the task at hand.
The results show that the model can converse with human subjects naturally
whilst helping them to accomplish tasks in a restaurant search domain.