pwc-archive/papers-with-abstracts huggingface dataset:
Task-Specific Attentive Pooling of Phrase Alignments Contributes to Sentence Matching
Authors: Wenpeng Yin, Hinrich Schütze
Date: 2017-01-09 00:00:00
Proceeding: EACL 2017 4
Tasks: Answer Selection, Natural Language Inference, Representation Learning, Sentence

This work studies comparatively two typical sentence matching tasks: textual
entailment (TE) and answer selection (AS), observing that weaker phrase
alignments are more critical in TE, while stronger phrase alignments deserve
more attention in AS. The key to reach this observation lies in phrase
detection, phrase representation, phrase alignment, and more importantly how to
connect those aligned phrases of different matching degrees with the final
classifier. Prior work (i) has limitations in phrase generation and
representation, or (ii) conducts alignment at word and phrase levels by
handcrafted features or (iii) utilizes a single framework of alignment without
considering the characteristics of specific tasks, which limits the framework's
effectiveness across tasks. We propose an architecture based on Gated Recurrent
Unit that supports (i) representation learning of phrases of arbitrary
granularity and (ii) task-specific attentive pooling of phrase alignments
between two sentences. Experimental results on TE and AS match our observation
and show the effectiveness of our approach.
Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey
Authors: Elike Hodo, Xavier Bellekens, Andrew Hamilton, Christos Tachtatzis, Robert Atkinson
Date: 2017-01-09 00:00:00
Tasks: BIG-bench Machine Learning, feature selection, Intrusion Detection, Survey

Intrusion detection has attracted a considerable interest from researchers
and industries. The community, after many years of research, still faces the
problem of building reliable and efficient IDS that are capable of handling
large quantities of data, with changing patterns in real time situations. The
work presented in this manuscript classifies intrusion detection systems (IDS).
Moreover, a taxonomy and survey of shallow and deep networks intrusion
detection systems is presented based on previous and current works. This
taxonomy and survey reviews machine learning techniques and their performance
in detecting anomalies. Feature selection which influences the effectiveness of
machine learning (ML) IDS is discussed to explain the role of feature selection
in the classification and training phase of ML IDS. Finally, a discussion of
the false and true positive alarm rates is presented to help researchers model
reliable and efficient machine learning based intrusion detection systems.
Deep driven fMRI decoding of visual categories
Authors: Michele Svanera, Sergio Benini, Gal Raz, Talma Hendler, Rainer Goebel, Giancarlo Valente
Date: 2017-01-09 00:00:00
Tasks: Decoder

Deep neural networks have been developed drawing inspiration from the brain
visual pathway, implementing an end-to-end approach: from image data to video
object classes. However building an fMRI decoder with the typical structure of
Convolutional Neural Network (CNN), i.e. learning multiple level of
representations, seems impractical due to lack of brain data. As a possible
solution, this work presents the first hybrid fMRI and deep features decoding
approach: collected fMRI and deep learnt representations of video object
classes are linked together by means of Kernel Canonical Correlation Analysis.
In decoding, this allows exploiting the discriminatory power of CNN by relating
the fMRI representation to the last layer of CNN (fc7). We show the
effectiveness of embedding fMRI data onto a subspace related to deep features
in distinguishing semantic visual categories based solely on brain imaging
data.
Low-rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging
Authors: Saiprasad Ravishankar, Brian E. Moore, Raj Rao Nadakuditi, Jeffrey A. Fessler
Date: 2016-11-13 00:00:00
Tasks: compressed sensing, Image Reconstruction

Sparsity-based approaches have been popular in many applications in image
processing and imaging. Compressed sensing exploits the sparsity of images in a
transform domain or dictionary to improve image recovery from undersampled
measurements. In the context of inverse problems in dynamic imaging, recent
research has demonstrated the promise of sparsity and low-rank techniques. For
example, the patches of the underlying data are modeled as sparse in an
adaptive dictionary domain, and the resulting image and dictionary estimation
from undersampled measurements is called dictionary-blind compressed sensing,
or the dynamic image sequence is modeled as a sum of low-rank and sparse (in
some transform domain) components (L+S model) that are estimated from limited
measurements. In this work, we investigate a data-adaptive extension of the L+S
model, dubbed LASSI, where the temporal image sequence is decomposed into a
low-rank component and a component whose spatiotemporal (3D) patches are sparse
in some adaptive dictionary domain. We investigate various formulations and
efficient methods for jointly estimating the underlying dynamic signal
components and the spatiotemporal dictionary from limited measurements. We also
obtain efficient sparsity penalized dictionary-blind compressed sensing methods
as special cases of our LASSI approaches. Our numerical experiments demonstrate
the promising performance of LASSI schemes for dynamic magnetic resonance image
reconstruction from limited k-t space data compared to recent methods such as
k-t SLR and L+S, and compared to the proposed dictionary-blind compressed
sensing method.
Green-Blue Stripe Pattern for Range Sensing from a Single Image
Authors: Changsoo Je, Kyuhyoung Choi, Sang Wook Lee
Date: 2017-01-09 00:00:00

In this paper, we present a novel method for rapid high-resolution range sensing using green-blue stripe pattern. We use green and blue for designing high-frequency stripe projection pattern. For accurate and reliable range recovery, we identify the stripe patterns by our color-stripe segmentation and unwrapping algorithms. The experimental result for a naked human face shows the effectiveness of our method.
Learning Sparse Structural Changes in High-dimensional Markov Networks: A Review on Methodologies and Theories
Authors: Song Liu, Kenji Fukumizu, Taiji Suzuki
Date: 2017-01-06 00:00:00

Recent years have seen an increasing popularity of learning the sparse
\emph{changes} in Markov Networks. Changes in the structure of Markov Networks
reflect alternations of interactions between random variables under different
regimes and provide insights into the underlying system. While each individual
network structure can be complicated and difficult to learn, the overall change
from one network to another can be simple. This intuition gave birth to an
approach that \emph{directly} learns the sparse changes without modelling and
learning the individual (possibly dense) networks. In this paper, we review
such a direct learning method with some latest developments along this line of
research.
Temporal scale selection in time-causal scale space
Authors: Tony Lindeberg
Date: 2017-01-09 00:00:00

When designing and developing scale selection mechanisms for generating
hypotheses about characteristic scales in signals, it is essential that the
selected scale levels reflect the extent of the underlying structures in the
signal.
  This paper presents a theory and in-depth theoretical analysis about the
scale selection properties of methods for automatically selecting local
temporal scales in time-dependent signals based on local extrema over temporal
scales of scale-normalized temporal derivative responses. Specifically, this
paper develops a novel theoretical framework for performing such temporal scale
selection over a time-causal and time-recursive temporal domain as is necessary
when processing continuous video or audio streams in real time or when
modelling biological perception.
  For a recently developed time-causal and time-recursive scale-space concept
defined by convolution with a scale-invariant limit kernel, we show that it is
possible to transfer a large number of the desirable scale selection properties
that hold for the Gaussian scale-space concept over a non-causal temporal
domain to this temporal scale-space concept over a truly time-causal domain.
Specifically, we show that for this temporal scale-space concept, it is
possible to achieve true temporal scale invariance although the temporal scale
levels have to be discrete, which is a novel theoretical construction.
Neural Personalized Response Generation as Domain Adaptation
Authors: Wei-Nan Zhang, Ting Liu, Yifa Wang, Qingfu Zhu
Date: 2017-01-09 00:00:00
Tasks: Decoder, Domain Adaptation, Response Generation

In this paper, we focus on the personalized response generation for conversational systems. Based on the sequence to sequence learning, especially the encoder-decoder framework, we propose a two-phase approach, namely initialization then adaptation, to model the responding style of human and then generate personalized responses. For evaluation, we propose a novel human aided method to evaluate the performance of the personalized response generation models by online real-time conversation and offline human judgement. Moreover, the lexical divergence of the responses generated by the 5 personalized models indicates that the proposed two-phase approach achieves good results on modeling the responding style of human and generating personalized responses for the conversational systems.
Optimal statistical decision for Gaussian graphical model selection
Authors: Valery A. Kalyagin, Alexander P. Koldanov, Petr A. Koldanov, Panos M. Pardalos
Date: 2017-01-09 00:00:00
Tasks: Information Retrieval, model, Model Selection, Retrieval

Gaussian graphical model is a graphical representation of the dependence
structure for a Gaussian random vector. It is recognized as a powerful tool in
different applied fields such as bioinformatics, error-control codes, speech
language, information retrieval and others. Gaussian graphical model selection
is a statistical problem to identify the Gaussian graphical model from a sample
of a given size. Different approaches for Gaussian graphical model selection
are suggested in the literature. One of them is based on considering the family
of individual conditional independence tests. The application of this approach
leads to the construction of a variety of multiple testing statistical
procedures for Gaussian graphical model selection. An important characteristic
of these procedures is its error rate for a given sample size. In existing
literature great attention is paid to the control of error rates for incorrect
edge inclusion (Type I error). However, in graphical model selection it is also
important to take into account error rates for incorrect edge exclusion (Type
II error). To deal with this issue we consider the graphical model selection
problem in the framework of the multiple decision theory. The quality of
statistical procedures is measured by a risk function with additive losses.
Additive losses allow both types of errors to be taken into account. We
construct the tests of a Neyman structure for individual hypotheses and combine
them to obtain a multiple decision statistical procedure. We show that the
obtained procedure is optimal in the sense that it minimizes the linear
combination of expected numbers of Type I and Type II errors in the class of
unbiased multiple decision procedures.
Coupled Compound Poisson Factorization
Authors: Mehmet E. Basbug, Barbara E. Engelhardt
Date: 2017-01-09 00:00:00
Tasks: Clustering, Variational Inference

We present a general framework, the coupled compound Poisson factorization
(CCPF), to capture the missing-data mechanism in extremely sparse data sets by
coupling a hierarchical Poisson factorization with an arbitrary data-generating
model. We derive a stochastic variational inference algorithm for the resulting
model and, as examples of our framework, implement three different
data-generating models---a mixture model, linear regression, and factor
analysis---to robustly model non-random missing data in the context of
clustering, prediction, and matrix factorization. In all three cases, we test
our framework against models that ignore the missing-data mechanism on large
scale studies with non-random missing data, and we show that explicitly
modeling the missing-data mechanism substantially improves the quality of the
results, as measured using data log likelihood on a held-out test set.
Simultaneous Low-rank Component and Graph Estimation for High-dimensional Graph Signals: Application to Brain Imaging
Authors: Rui Liu, Hossein Nejati, Seyed Hamid Safavi, Ngai-Man Cheung
Date: 2016-09-26 00:00:00
Tasks: General Classification

We propose an algorithm to uncover the intrinsic low-rank component of a
high-dimensional, graph-smooth and grossly-corrupted dataset, under the
situations that the underlying graph is unknown. Based on a model with a
low-rank component plus a sparse perturbation, and an initial graph estimation,
our proposed algorithm simultaneously learns the low-rank component and refines
the graph. Our evaluations using synthetic and real brain imaging data in
unsupervised and supervised classification tasks demonstrate encouraging
performance.
Data Programming: Creating Large Training Sets, Quickly
Authors: Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré
Date: 2016-05-25 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: BIG-bench Machine Learning, Slot Filling

Large labeled training sets are the critical building blocks of supervised
learning methods and are key enablers of deep learning techniques. For some
applications, creating labeled training sets is the most time-consuming and
expensive part of applying machine learning. We therefore propose a paradigm
for the programmatic creation of training sets called data programming in which
users express weak supervision strategies or domain heuristics as labeling
functions, which are programs that label subsets of the data, but that are
noisy and may conflict. We show that by explicitly representing this training
set labeling process as a generative model, we can "denoise" the generated
training set, and establish theoretically that we can recover the parameters of
these generative models in a handful of settings. We then show how to modify a
discriminative loss function to make it noise-aware, and demonstrate our method
over a range of discriminative models including logistic regression and LSTMs.
Experimentally, on the 2014 TAC-KBP Slot Filling challenge, we show that data
programming would have led to a new winning score, and also show that applying
data programming to an LSTM model leads to a TAC-KBP score almost 6 F1 points
over a state-of-the-art LSTM baseline (and into second place in the
competition). Additionally, in initial user studies we observed that data
programming may be an easier way for non-experts to create machine learning
models when training data is limited or unavailable.
Team-Optimal Distributed MMSE Estimation in General and Tree Networks
Authors: Muhammed O. Sayin, Suleyman S. Kozat, Tamer Başar
Date: 2016-10-03 00:00:00
Tasks: State Estimation

We construct team-optimal estimation algorithms over distributed networks for
state estimation in the finite-horizon mean-square error (MSE) sense. Here, we
have a distributed collection of agents with processing and cooperation
capabilities. These agents observe noisy samples of a desired state through a
linear model and seek to learn this state by interacting with each other.
Although this problem has attracted significant attention and been studied
extensively in fields including machine learning and signal processing, all the
well-known strategies do not achieve team-optimal learning performance in the
finite-horizon MSE sense. To this end, we formulate the finite-horizon
distributed minimum MSE (MMSE) when there is no restriction on the size of the
disclosed information, i.e., oracle performance, over an arbitrary network
topology. Subsequently, we show that exchange of local estimates is sufficient
to achieve the oracle performance only over certain network topologies. By
inspecting these network structures, we propose recursive algorithms achieving
the oracle performance through the disclosure of local estimates. For practical
implementations we also provide approaches to reduce the complexity of the
algorithms through the time-windowing of the observations. Finally, in the
numerical examples, we demonstrate the superior performance of the introduced
algorithms in the finite-horizon MSE sense due to optimal estimation.
Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling
Authors: Chengtao Li, Stefanie Jegelka, Suvrit Sra
Date: 2016-08-02 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Point Processes

We study probability measures induced by set functions with constraints. Such
measures arise in a variety of real-world settings, where prior knowledge,
resource limitations, or other pragmatic considerations impose constraints. We
consider the task of rapidly sampling from such constrained measures, and
develop fast Markov chain samplers for them. Our first main result is for MCMC
sampling from Strongly Rayleigh (SR) measures, for which we present sharp
polynomial bounds on the mixing time. As a corollary, this result yields a fast
mixing sampler for Determinantal Point Processes (DPPs), yielding (to our
knowledge) the first provably fast MCMC sampler for DPPs since their inception
over four decades ago. Beyond SR measures, we develop MCMC samplers for
probabilistic models with hard constraints and identify sufficient conditions
under which their chains mix rapidly. We illustrate our claims by empirically
verifying the dependence of mixing times on the key factors governing our
theoretical bounds.
MS and PAN image fusion by combining Brovey and wavelet methods
Authors: Hamid Reza Shahdoosti
Date: 2017-01-08 00:00:00

Among the existing fusion algorithms, the wavelet fusion method is the most
frequently discussed one in recent publications because the wavelet approach
preserves the spectral characteristics of the multispectral image better than
other methods. The Brovey is also a popular fusion method used for its ability
in preserving the spatial information of the PAN image. This study presents a
new fusion approach that integrates the advantages of both the Brovey (which
preserves a high degree of spatial information) and the wavelet (which
preserves a high degree of spectral information) techniques to reduce the
colour distortion of fusion results. Visual and statistical analyzes show that
the proposed algorithm clearly improves the merging quality in terms of:
correlation coefficient and UIQI; compared to fusion methods including, IHS,
Brovey, PCA , HPF, discrete wavelet transform (DWT), and a-trous wavelet.
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
Authors: Konstantinos Kamnitsas, Christian Ledig, Virginia F. J. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K. Menon, Daniel Rueckert, Ben Glocker
Date: 2016-03-18 00:00:00
Tasks: 3D Medical Imaging Segmentation, Brain Lesion Segmentation From Mri, Brain Tumor Segmentation, Lesion Segmentation, Medical Image Segmentation, Segmentation

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional
Neural Network for the challenging task of brain lesion segmentation. The
devised architecture is the result of an in-depth analysis of the limitations
of current networks proposed for similar applications. To overcome the
computational burden of processing 3D medical scans, we have devised an
efficient and effective dense training scheme which joins the processing of
adjacent image patches into one pass through the network while automatically
adapting to the inherent class imbalance present in the data. Further, we
analyze the development of deeper, thus more discriminative 3D CNNs. In order
to incorporate both local and larger contextual information, we employ a dual
pathway architecture that processes the input images at multiple scales
simultaneously. For post-processing of the network's soft segmentation, we use
a 3D fully connected Conditional Random Field which effectively removes false
positives. Our pipeline is extensively evaluated on three challenging tasks of
lesion segmentation in multi-channel MRI patient data with traumatic brain
injuries, brain tumors, and ischemic stroke. We improve on the state-of-the-art
for all three applications, with top ranking performance on the public
benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient,
which allows its adoption in a variety of research and clinical settings. The
source code of our implementation is made publicly available.
Multi-spectral Image Panchromatic Sharpening, Outcome and Process Quality Assessment Protocol
Authors: Andrea Baraldi, Francesca Despini, Sergio Teggi
Date: 2017-01-08 00:00:00
Tasks: Earth Observation

Multispectral (MS) image panchromatic (PAN) sharpening algorithms proposed to
the remote sensing community are ever increasing in number and variety. Their
aim is to sharpen a coarse spatial resolution MS image with a fine spatial
resolution PAN image acquired simultaneously by a spaceborne or airborne Earth
observation (EO) optical imaging sensor pair. Unfortunately, to date, no
standard evaluation procedure for MS image PAN sharpening outcome and process
is community agreed upon, in contrast with the Quality Assurance Framework for
Earth Observation (QA4EO) guidelines proposed by the intergovernmental Group on
Earth Observations (GEO). In general, process is easier to measure, outcome is
more important. The original contribution of the present study is fourfold.
First, existing procedures for quantitative quality assessment (Q2A) of the
(sole) PAN sharpened MS product are critically reviewed. Their conceptual and
implementation drawbacks are highlighted to be overcome for quality
improvement. Second, a novel (to the best of these authors' knowledge, the
first) protocol for Q2A of MS image PAN sharpening product and process is
designed, implemented and validated by independent means. Third, within this
protocol, an innovative categorization of spectral and spatial image quality
indicators and metrics is presented. Fourth, according to this new taxonomy, an
original third order isotropic multi scale gray level co occurrence matrix
(TIMS GLCM) calculator and a TIMS GLCM texture feature extractor are proposed
to replace popular second order GLCMs.