pwc-archive/papers-with-abstracts huggingface dataset:
Solving finite-domain linear constraints in presence of the $\texttt{alldifferent}$
Authors: Milan Banković
Date: 2016-07-08 00:00:00

In this paper, we investigate the possibility of improvement of the
widely-used filtering algorithm for the linear constraints in constraint
satisfaction problems in the presence of the alldifferent constraints. In many
cases, the fact that the variables in a linear constraint are also constrained
by some alldifferent constraints may help us to calculate stronger bounds of
the variables, leading to a stronger constraint propagation. We propose an
improved filtering algorithm that targets such cases. We provide a detailed
description of the proposed algorithm and prove its correctness. We evaluate
the approach on five different problems that involve combinations of the linear
and the alldifferent constraints. We also compare our algorithm to other
relevant approaches. The experimental results show a great potential of the
proposed improvement.
Sieve-based Coreference Resolution in the Biomedical Domain
Authors: Dane Bell, Gus Hahn-Powell, Marco A. Valenzuela-Escárcega, Mihai Surdeanu
Date: 2016-03-11 00:00:00
Proceeding: LREC 2016 5
Tasks: coreference-resolution, Coreference Resolution, Event Coreference Resolution, Event Extraction

We describe challenges and advantages unique to coreference resolution in the
biomedical domain, and a sieve-based architecture that leverages domain
knowledge for both entity and event coreference resolution. Domain-general
coreference resolution algorithms perform poorly on biomedical documents,
because the cues they rely on such as gender are largely absent in this domain,
and because they do not encode domain-specific knowledge such as the number and
type of participants required in chemical reactions. Moreover, it is difficult
to directly encode this knowledge into most coreference resolution algorithms
because they are not rule-based. Our rule-based architecture uses sequentially
applied hand-designed "sieves", with the output of each sieve informing and
constraining subsequent sieves. This architecture provides a 3.2% increase in
throughput to our Reach event extraction system with precision parallel to that
of the stricter system that relies solely on syntactic patterns for extraction.
STFCN: Spatio-Temporal FCN for Semantic Video Segmentation
Authors: Mohsen Fayyaz, Mohammad Hajizadeh Saffar, Mohammad Sabokrou, Mahmood Fathy, Reinhard klette, Fay Huang
Date: 2016-08-21 00:00:00
Tasks: Segmentation, Semantic Segmentation, Video Segmentation, Video Semantic Segmentation

This paper presents a novel method to involve both spatial and temporal
features for semantic video segmentation. Current work on convolutional neural
networks(CNNs) has shown that CNNs provide advanced spatial features supporting
a very good performance of solutions for both image and video analysis,
especially for the semantic segmentation task. We investigate how involving
temporal features also has a good effect on segmenting video data. We propose a
module based on a long short-term memory (LSTM) architecture of a recurrent
neural network for interpreting the temporal characteristics of video frames
over time. Our system takes as input frames of a video and produces a
correspondingly-sized output; for segmenting the video our method combines the
use of three components: First, the regional spatial features of frames are
extracted using a CNN; then, using LSTM the temporal features are added;
finally, by deconvolving the spatio-temporal features we produce pixel-wise
predictions. Our key insight is to build spatio-temporal convolutional networks
(spatio-temporal CNNs) that have an end-to-end architecture for semantic video
segmentation. We adapted fully some known convolutional network architectures
(such as FCN-AlexNet and FCN-VGG16), and dilated convolution into our
spatio-temporal CNNs. Our spatio-temporal CNNs achieve state-of-the-art
semantic segmentation, as demonstrated for the Camvid and NYUDv2 datasets.
SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques
Authors: Elad Richardson, Rom Herskovitz, Boris Ginsburg, Michael Zibulevsky
Date: 2016-09-02 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Stochastic Optimization

We present SEBOOST, a technique for boosting the performance of existing
stochastic optimization methods. SEBOOST applies a secondary optimization
process in the subspace spanned by the last steps and descent directions. The
method was inspired by the SESOP optimization method for large-scale problems,
and has been adapted for the stochastic learning framework. It can be applied
on top of any existing optimization method with no need to tweak the internal
algorithm. We show that the method is able to boost the performance of
different algorithms, and make them more robust to changes in their
hyper-parameters. As the boosting steps of SEBOOST are applied between large
sets of descent steps, the additional subspace optimization hardly increases
the overall computational burden. We introduce two hyper-parameters that
control the balance between the baseline method and the secondary optimization
process. The method was evaluated on several deep learning tasks, demonstrating
promising results.
Generic Inference in Latent Gaussian Process Models
Authors: Edwin V. Bonilla, Karl Krauth, Amir Dezfouli
Date: 2016-09-02 00:00:00
Tasks: General Classification, Stochastic Optimization

We develop an automated variational method for inference in models with
Gaussian process (GP) priors and general likelihoods. The method supports
multiple outputs and multiple latent functions and does not require detailed
knowledge of the conditional likelihood, only needing its evaluation as a
black-box function. Using a mixture of Gaussians as the variational
distribution, we show that the evidence lower bound and its gradients can be
estimated efficiently using samples from univariate Gaussian distributions.
Furthermore, the method is scalable to large datasets which is achieved by
using an augmented prior via the inducing-variable approach underpinning most
sparse GP approximations, along with parallel computation and stochastic
optimization. We evaluate our approach quantitatively and qualitatively with
experiments on small datasets, medium-scale datasets and large datasets,
showing its competitiveness under different likelihood models and sparsity
levels. On the large-scale experiments involving prediction of airline delays
and classification of handwritten digits, we show that our method is on par
with the state-of-the-art hard-coded approaches for scalable GP regression and
classification.
Kernelized Covariance for Action Recognition
Authors: Jacopo Cavazza, Andrea Zunino, Marco San Biagio, Vittorio Murino
Date: 2016-04-22 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Descriptive, Temporal Action Localization

In this paper we aim at increasing the descriptive power of the covariance
matrix, limited in capturing linear mutual dependencies between variables only.
We present a rigorous and principled mathematical pipeline to recover the
kernel trick for computing the covariance matrix, enhancing it to model more
complex, non-linear relationships conveyed by the raw data. To this end, we
propose Kernelized-COV, which generalizes the original covariance
representation without compromising the efficiency of the computation. In the
experiments, we validate the proposed framework against many previous
approaches in the literature, scoring on par or superior with respect to the
state of the art on benchmark datasets for 3D action recognition.
Skipping Word: A Character-Sequential Representation based Framework for Question Answering
Authors: Lingxun Meng, Yan Li, Mengyi Liu, Peng Shu
Date: 2016-09-02 00:00:00
Tasks: Answer Selection, Question Answering

Recent works using artificial neural networks based on word distributed
representation greatly boost the performance of various natural language
learning tasks, especially question answering. Though, they also carry along
with some attendant problems, such as corpus selection for embedding learning,
dictionary transformation for different learning tasks, etc. In this paper, we
propose to straightforwardly model sentences by means of character sequences,
and then utilize convolutional neural networks to integrate character embedding
learning together with point-wise answer selection training. Compared with deep
models pre-trained on word embedding (WE) strategy, our character-sequential
representation (CSR) based method shows a much simpler procedure and more
stable performance across different benchmarks. Extensive experiments on two
benchmark answer selection datasets exhibit the competitive performance
compared with the state-of-the-art methods.
Efficient Two-Stream Motion and Appearance 3D CNNs for Video Classification
Authors: Ali Diba, Ali Mohammad Pazandeh, Luc van Gool
Date: 2016-08-31 00:00:00
Tasks: 3D Architecture, Action Classification, General Classification, Optical Flow Estimation, Video Classification, Vocal Bursts Valence Prediction

The video and action classification have extremely evolved by deep neural
networks specially with two stream CNN using RGB and optical flow as inputs and
they present outstanding performance in terms of video analysis. One of the
shortcoming of these methods is handling motion information extraction which is
done out side of the CNNs and relatively time consuming also on GPUs. So
proposing end-to-end methods which are exploring to learn motion
representation, like 3D-CNN can achieve faster and accurate performance. We
present some novel deep CNNs using 3D architecture to model actions and motion
representation in an efficient way to be accurate and also as fast as
real-time. Our new networks learn distinctive models to combine deep motion
features into appearance model via learning optical flow features inside the
network.
Direct Prediction of 3D Body Poses from Motion Compensated Sequences
Authors: Bugra Tekin, Artem Rozantsev, Vincent Lepetit, Pascal Fua
Date: 2015-11-20 00:00:00
Proceeding: CVPR 2016 6
Tasks: 3D Human Pose Estimation, Pose Estimation

We propose an efficient approach to exploiting motion information from
consecutive frames of a video sequence to recover the 3D pose of people.
Previous approaches typically compute candidate poses in individual frames and
then link them in a post-processing step to resolve ambiguities. By contrast,
we directly regress from a spatio-temporal volume of bounding boxes to a 3D
pose in the central frame.
  We further show that, for this approach to achieve its full potential, it is
essential to compensate for the motion in consecutive frames so that the
subject remains centered. This then allows us to effectively overcome
ambiguities and improve upon the state-of-the-art by a large margin on the
Human3.6m, HumanEva, and KTH Multiview Football 3D human pose estimation
benchmarks.
Learning Moore Machines from Input-Output Traces
Authors: Georgios Giantamidis, Stavros Tripakis
Date: 2016-05-25 00:00:00
Tasks: Learning Theory

The problem of learning automata from example traces (but no equivalence or
membership queries) is fundamental in automata learning theory and practice. In
this paper we study this problem for finite state machines with inputs and
outputs, and in particular for Moore machines. We develop three algorithms for
solving this problem: (1) the PTAP algorithm, which transforms a set of
input-output traces into an incomplete Moore machine and then completes the
machine with self-loops; (2) the PRPNI algorithm, which uses the well-known
RPNI algorithm for automata learning to learn a product of automata encoding a
Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore
machine using PTAP extended with state merging. We prove that MooreMI has the
fundamental identification in the limit property. We also compare the
algorithms experimentally in terms of the size of the learned machine and
several notions of accuracy, introduced in this paper. Finally, we compare with
OSTIA, an algorithm that learns a more general class of transducers, and find
that OSTIA generally does not learn a Moore machine, even when fed with a
characteristic sample.
On Horizontal and Vertical Separation in Hierarchical Text Classification
Authors: Mostafa Dehghani, Hosein Azarbonyad, Jaap Kamps, Maarten Marx
Date: 2016-09-02 00:00:00
Tasks: Classification, General Classification, Position, text-classification, Text Classification

Hierarchy is a common and effective way of organizing data and representing
their relationships at different levels of abstraction. However, hierarchical
data dependencies cause difficulties in the estimation of "separable" models
that can distinguish between the entities in the hierarchy. Extracting
separable models of hierarchical entities requires us to take their relative
position into account and to consider the different types of dependencies in
the hierarchy. In this paper, we present an investigation of the effect of
separability in text-based entity classification and argue that in hierarchical
classification, a separation property should be established between entities
not only in the same layer, but also in different layers. Our main findings are
the followings. First, we analyse the importance of separability on the data
representation in the task of classification and based on that, we introduce a
"Strong Separation Principle" for optimizing expected effectiveness of
classifiers decision based on separation property. Second, we present
Hierarchical Significant Words Language Models (HSWLM) which capture all, and
only, the essential features of hierarchical entities according to their
relative position in the hierarchy resulting in horizontally and vertically
separable models. Third, we validate our claims on real-world data and
demonstrate that how HSWLM improves the accuracy of classification and how it
provides transferable models over time. Although discussions in this paper
focus on the classification problem, the models are applicable to any
information access tasks on data that has, or can be mapped to, a hierarchical
structure.
Problem Theory
Authors: Ramón Casares
Date: 2014-12-01 00:00:00

The Turing machine, as it was presented by Turing himself, models the
calculations done by a person. This means that we can compute whatever any
Turing machine can compute, and therefore we are Turing complete. The question
addressed here is why, Why are we Turing complete? Being Turing complete also
means that somehow our brain implements the function that a universal Turing
machine implements. The point is that evolution achieved Turing completeness,
and then the explanation should be evolutionary, but our explanation is
mathematical. The trick is to introduce a mathematical theory of problems,
under the basic assumption that solving more problems provides more survival
opportunities. So we build a problem theory by fusing set and computing
theories. Then we construct a series of resolvers, where each resolver is
defined by its computing capacity, that exhibits the following property: all
problems solved by a resolver are also solved by the next resolver in the
series if certain condition is satisfied. The last of the conditions is to be
Turing complete. This series defines a resolvers hierarchy that could be seen
as a framework for the evolution of cognition. Then the answer to our question
would be: to solve most problems. By the way, the problem theory defines
adaptation, perception, and learning, and it shows that there are just three
ways to resolve any problem: routine, trial, and analogy. And, most
importantly, this theory demonstrates how problems can be used to found
mathematics and computing on biology.
Penalized estimation in large-scale generalized linear array models
Authors: Adam Lund, Martin Vincent, Niels Richard Hansen
Date: 2015-10-12 00:00:00

Large-scale generalized linear array models (GLAMs) can be challenging to
fit. Computation and storage of its tensor product design matrix can be
impossible due to time and memory constraints, and previously considered design
matrix free algorithms do not scale well with the dimension of the parameter
vector. A new design matrix free algorithm is proposed for computing the
penalized maximum likelihood estimate for GLAMs, which, in particular, handles
nondifferentiable penalty functions. The proposed algorithm is implemented and
available via the R package \verb+glamlasso+. It combines several ideas --
previously considered separately -- to obtain sparse estimates while at the
same time efficiently exploiting the GLAM structure. In this paper the
convergence of the algorithm is treated and the performance of its
implementation is investigated and compared to that of \verb+glmnet+ on
simulated as well as real data. It is shown that the computation time for
A case study of algorithm selection for the traveling thief problem
Authors: Markus Wagner, Marius Lindauer, Mustafa Misir, Samadhi Nallaperuma, Frank Hutter
Date: 2016-09-02 00:00:00
Tasks: Combinatorial Optimization

Many real-world problems are composed of several interacting components. In
order to facilitate research on such interactions, the Traveling Thief Problem
(TTP) was created in 2013 as the combination of two well-understood
combinatorial optimization problems.
  With this article, we contribute in four ways. First, we create a
comprehensive dataset that comprises the performance data of 21 TTP algorithms
on the full original set of 9720 TTP instances. Second, we define 55
characteristics for all TPP instances that can be used to select the best
algorithm on a per-instance basis. Third, we use these algorithms and features
to construct the first algorithm portfolios for TTP, clearly outperforming the
single best algorithm. Finally, we study which algorithms contribute most to
this portfolio.
Least Ambiguous Set-Valued Classifiers with Bounded Error Levels
Authors: Mauricio Sadinle, Jing Lei, Larry Wasserman
Date: 2016-09-02 00:00:00
Tasks: General Classification

In most classification tasks there are observations that are ambiguous and
therefore difficult to correctly label. Set-valued classifiers output sets of
plausible labels rather than a single label, thereby giving a more appropriate
and informative treatment to the labeling of ambiguous instances. We introduce
a framework for multiclass set-valued classification, where the classifiers
guarantee user-defined levels of coverage or confidence (the probability that
the true label is contained in the set) while minimizing the ambiguity (the
expected size of the output). We first derive oracle classifiers assuming the
true distribution to be known. We show that the oracle classifiers are obtained
from level sets of the functions that define the conditional probability of
each class. Then we develop estimators with good asymptotic and finite sample
properties. The proposed estimators build on existing single-label classifiers.
The optimal classifier can sometimes output the empty set, but we provide two
solutions to fix this issue that are suitable for various practical needs.
Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation
Authors: Fatemehsadat Saleh, Mohammad Sadegh Ali Akbarian, Mathieu Salzmann, Lars Petersson, Stephen Gould, Jose M. Alvarez
Date: 2016-09-02 00:00:00
Tasks: Segmentation, Semantic Segmentation, TAG, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Pixel-level annotations are expensive and time consuming to obtain. Hence,
weak supervision using only image tags could have a significant impact in
semantic segmentation. Recently, CNN-based methods have proposed to fine-tune
pre-trained networks using image tags. Without additional information, this
leads to poor localization accuracy. This problem, however, was alleviated by
making use of objectness priors to generate foreground/background masks.
Unfortunately these priors either require training pixel-level
annotations/bounding boxes, or still yield inaccurate object boundaries. Here,
we propose a novel method to extract markedly more accurate masks from the
pre-trained network itself, forgoing external objectness modules. This is
accomplished using the activations of the higher-level convolutional layers,
smoothed by a dense CRF. We demonstrate that our method, based on these masks
and a weakly-supervised loss, outperforms the state-of-the-art tag-based
weakly-supervised semantic segmentation techniques. Furthermore, we introduce a
new form of inexpensive weak supervision yielding an additional accuracy boost.
Citation Classification for Behavioral Analysis of a Scientific Field
Authors: David Jurgens, Srijan Kumar, Raine Hoover, Dan McFarland, Dan Jurafsky
Date: 2016-09-02 00:00:00
Tasks: Classification, General Classification

Citations are an important indicator of the state of a scientific field,
reflecting how authors frame their work, and influencing uptake by future
scholars. However, our understanding of citation behavior has been limited to
small-scale manual citation analysis. We perform the largest behavioral study
of citations to date, analyzing how citations are both framed and taken up by
scholars in one entire field: natural language processing. We introduce a new
dataset of nearly 2,000 citations annotated for function and centrality, and
use it to develop a state-of-the-art classifier and label the entire ACL
Reference Corpus. We then study how citations are framed by authors and use
both papers and online traces to track how citations are followed by readers.
We demonstrate that authors are sensitive to discourse structure and
publication venue when citing, that online readers follow temporal links to
previous and future work rather than methodological links, and that how a paper
cites related work is predictive of its citation count. Finally, we use changes
in citation roles to show that the field of NLP is undergoing a significant
increase in consensus.
Peacock Bundles: Bundle Coloring for Graphs with Globality-Locality Trade-off
Authors: Jaakko Peltonen, Ziyuan Lin
Date: 2016-09-02 00:00:00
Tasks: Dimensionality Reduction

Bundling of graph edges (node-to-node connections) is a common technique to
enhance visibility of overall trends in the edge structure of a large graph
layout, and a large variety of bundling algorithms have been proposed. However,
with strong bundling, it becomes hard to identify origins and destinations of
individual edges. We propose a solution: we optimize edge coloring to
differentiate bundled edges. We quantify strength of bundling in a flexible
pairwise fashion between edges, and among bundled edges, we quantify how
dissimilar their colors should be by dissimilarity of their origins and
destinations. We solve the resulting nonlinear optimization, which is also
interpretable as a novel dimensionality reduction task. In large graphs the
necessary compromise is whether to differentiate colors sharply between locally
occurring strongly bundled edges ("local bundles"), or also between the weakly
bundled edges occurring globally over the graph ("global bundles"); we allow a
user-set global-local tradeoff. We call the technique "peacock bundles".
Experiments show the coloring clearly enhances comprehensibility of graph
layouts with edge bundling.