pwc-archive/papers-with-abstracts huggingface dataset:
Using a Distributional Semantic Vector Space with a Knowledge Base for Reasoning in Uncertain Conditions
Authors: Douglas Summers-Stay, Clare Voss, Taylor Cassidy
Date: 2016-06-13 00:00:00

The inherent inflexibility and incompleteness of commonsense knowledge bases
(KB) has limited their usefulness. We describe a system called Displacer for
performing KB queries extended with the analogical capabilities of the word2vec
distributional semantic vector space (DSVS). This allows the system to answer
queries with information which was not contained in the original KB in any
form. By performing analogous queries on semantically related terms and mapping
their answers back into the context of the original query using displacement
vectors, we are able to give approximate answers to many questions which, if
posed to the KB alone, would return no results.
  We also show how the hand-curated knowledge in a KB can be used to increase
the accuracy of a DSVS in solving analogy problems. In these ways, a KB and a
DSVS can make up for each other's weaknesses.
Inferring Sparsity: Compressed Sensing using Generalized Restricted Boltzmann Machines
Authors: Eric W. Tramel, Andre Manoel, Francesco Caltagirone, Marylou Gabrié, Florent Krzakala
Date: 2016-06-13 00:00:00
Tasks: compressed sensing

In this work, we consider compressed sensing reconstruction from $M$
measurements of $K$-sparse structured signals which do not possess a writable
correlation model. Assuming that a generative statistical model, such as a
Boltzmann machine, can be trained in an unsupervised manner on example signals,
we demonstrate how this signal model can be used within a Bayesian framework of
signal reconstruction. By deriving a message-passing inference for general
distribution restricted Boltzmann machines, we are able to integrate these
inferred signal models into approximate message passing for compressed sensing
reconstruction. Finally, we show for the MNIST dataset that this approach can
be very effective, even for $M < K$.
Systematic evaluation of CNN advances on the ImageNet
Authors: Dmytro Mishkin, Nikolay Sergievskiy, Jiri Matas
Date: 2016-06-07 00:00:00
Tasks: Object Categorization

The paper systematically studies the impact of a range of recent advances in
CNN architectures and learning methods on the object categorization (ILSVRC)
problem. The evalution tests the influence of the following choices of the
architecture: non-linearity (ReLU, ELU, maxout, compatibility with batch
normalization), pooling variants (stochastic, max, average, mixed), network
width, classifier design (convolutional, fully-connected, SPP), image
pre-processing, and of learning parameters: learning rate, batch size,
cleanliness of the data, etc.
  The performance gains of the proposed modifications are first tested
individually and then in combination. The sum of individual gains is bigger
than the observed improvement when all modifications are introduced, but the
"deficit" is small suggesting independence of their benefits. We show that the
use of 128x128 pixel images is sufficient to make qualitative conclusions about
optimal network structure that hold for the full size Caffe and VGG nets. The
results are obtained an order of magnitude faster than with the standard 224
pixel images.
Efficient Estimation of k for the Nearest Neighbors Class of Methods
Authors: Aleksander Lodwich, Faisal Shafait, Thomas Breuel
Date: 2016-06-08 00:00:00

The k Nearest Neighbors (kNN) method has received much attention in the past
decades, where some theoretical bounds on its performance were identified and
where practical optimizations were proposed for making it work fairly well in
high dimensional spaces and on large datasets. From countless experiments of
the past it became widely accepted that the value of k has a significant impact
on the performance of this method. However, the efficient optimization of this
parameter has not received so much attention in literature. Today, the most
common approach is to cross-validate or bootstrap this value for all values in
question. This approach forces distances to be recomputed many times, even if
efficient methods are used. Hence, estimating the optimal k can become
expensive even on modern systems. Frequently, this circumstance leads to a
sparse manual search of k. In this paper we want to point out that a systematic
and thorough estimation of the parameter k can be performed efficiently. The
discussed approach relies on large matrices, but we want to argue, that in
practice a higher space complexity is often much less of a problem than
repetitive distance computations.
A Probabilistic-Based Model for Binary CSP
Authors: Amine Balafrej, Xavier Lorca, Charlotte Truchet
Date: 2016-06-13 00:00:00
Tasks: ARC, model

This work introduces a probabilistic-based model for binary CSP that provides
a fine grained analysis of its internal structure. Assuming that a domain
modification could occur in the CSP, it shows how to express, in a predictive
way, the probability that a domain value becomes inconsistent, then it express
the expectation of the number of arc-inconsistent values in each domain of the
constraint network. Thus, it express the expectation of the number of
arc-inconsistent values for the whole constraint network. Next, it provides
bounds for each of these three probabilistic indicators. Finally, a polytime
algorithm, which propagates the probabilistic information, is presented.
"Pale as death" or "pâle comme la mort" : Frozen similes used as literary clichés
Authors: Suzanne Mpouli, Jean-Gabriel Ganascia
Date: 2015-11-05 00:00:00

The present study is focused on the automatic identification and description
of frozen similes in British and French novels written between the 19 th
century and the beginning of the 20 th century. Two main patterns of frozen
similes were considered: adjectival ground + simile marker + nominal vehicle
(e.g. happy as a lark) and eventuality + simile marker + nominal vehicle (e.g.
sleep like a top). All potential similes and their components were first
extracted using a rule-based algorithm. Then, frozen similes were identified
based on reference lists of existing similes and semantic distance between the
tenor and the vehicle. The results obtained tend to confirm the fact that
frozen similes are not used haphazardly in literary texts. In addition,
contrary to how they are often presented, frozen similes often go beyond the
ground or the eventuality and the vehicle to also include the tenor.
Laplacian LRR on Product Grassmann Manifolds for Human Activity Clustering in Multi-Camera Video Surveillance
Authors: Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Bao-Cai Yin
Date: 2016-06-13 00:00:00
Tasks: Activity Recognition, Clustering, Human Activity Recognition

In multi-camera video surveillance, it is challenging to represent videos
from different cameras properly and fuse them efficiently for specific
applications such as human activity recognition and clustering. In this paper,
a novel representation for multi-camera video data, namely the Product
Grassmann Manifold (PGM), is proposed to model video sequences as points on the
Grassmann manifold and integrate them as a whole in the product manifold form.
Additionally, with a new geometry metric on the product manifold, the
conventional Low Rank Representation (LRR) model is extended onto PGM and the
new LRR model can be used for clustering non-linear data, such as multi-camera
video data. To evaluate the proposed method, a number of clustering experiments
are conducted on several multi-camera video datasets of human activity,
including Dongzhimen Transport Hub Crowd action dataset, ACT 42 Human action
dataset and SKIG action dataset. The experiment results show that the proposed
method outperforms many state-of-the-art clustering methods.
Evidential Label Propagation Algorithm for Graphs
Authors: Kuang Zhou, Arnaud Martin, Quan Pan, Zhun-Ga Liu
Date: 2016-06-13 00:00:00
Tasks: Community Detection

Community detection has attracted considerable attention crossing many areas
as it can be used for discovering the structure and features of complex
networks. With the increasing size of social networks in real world, community
detection approaches should be fast and accurate. The Label Propagation
Algorithm (LPA) is known to be one of the near-linear solutions and benefits of
easy implementation, thus it forms a good basis for efficient community
detection methods. In this paper, we extend the update rule and propagation
criterion of LPA in the framework of belief functions. A new community
detection approach, called Evidential Label Propagation (ELP), is proposed as
an enhanced version of conventional LPA. The node influence is first defined to
guide the propagation process. The plausibility is used to determine the domain
label of each node. The update order of nodes is discussed to improve the
robustness of the method. ELP algorithm will converge after the domain labels
of all the nodes become unchanged. The mass assignments are calculated finally
as memberships of nodes. The overlapping nodes and outliers can be detected
simultaneously through the proposed method. The experimental results
demonstrate the effectiveness of ELP.
Why and How to Pay Different Attention to Phrase Alignments of Different Intensities
Authors: Wenpeng Yin, Hinrich Schütze
Date: 2016-04-23 00:00:00
Tasks: Answer Selection, Natural Language Inference, Representation Learning, Sentence, Sentence-Pair Classification

This work studies comparatively two typical sentence pair classification
tasks: textual entailment (TE) and answer selection (AS), observing that phrase
alignments of different intensities contribute differently in these tasks. We
address the problems of identifying phrase alignments of flexible granularity
and pooling alignments of different intensities for these tasks. Examples for
flexible granularity are alignments between two single words, between a single
word and a phrase and between a short phrase and a long phrase. By intensity we
roughly mean the degree of match, it ranges from identity over surface-form
co-occurrence, rephrasing and other semantic relatedness to unrelated words as
in lots of parenthesis text. 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 attention mechanism
over alignment intensities without considering the characteristics of specific
tasks, which limits the system'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 focusing of
phrase alignments between two sentences by attention pooling. Experimental
results on TE and AS match our observation and are state-of-the-art.
Tuning-Free Heterogeneity Pursuit in Massive Networks
Authors: Zhao Ren, Yongjian Kang, Yingying Fan, Jinchi Lv
Date: 2016-06-13 00:00:00

Heterogeneity is often natural in many contemporary applications involving
massive data. While posing new challenges to effective learning, it can play a
crucial role in powering meaningful scientific discoveries through the
understanding of important differences among subpopulations of interest. In
this paper, we exploit multiple networks with Gaussian graphs to encode the
connectivity patterns of a large number of features on the subpopulations. To
uncover the heterogeneity of these structures across subpopulations, we suggest
a new framework of tuning-free heterogeneity pursuit (THP) via large-scale
inference, where the number of networks is allowed to diverge. In particular,
two new tests, the chi-based test and the linear functional-based test, are
introduced and their asymptotic null distributions are established. Under mild
regularity conditions, we establish that both tests are optimal in achieving
the testable region boundary and the sample size requirement for the latter
test is minimal. Both theoretical guarantees and the tuning-free feature stem
from efficient multiple-network estimation by our newly suggested approach of
heterogeneous group square-root Lasso (HGSL) for high-dimensional
multi-response regression with heterogeneous noises. To solve this convex
program, we further introduce a tuning-free algorithm that is scalable and
enjoys provable convergence to the global optimum. Both computational and
theoretical advantages of our procedure are elucidated through simulation and
real data examples.
A note on the complexity of the causal ordering problem
Authors: Bernardo Gonçalves, Fabio Porto
Date: 2015-08-24 00:00:00
Tasks: Management

In this note we provide a concise report on the complexity of the causal
ordering problem, originally introduced by Simon to reason about causal
dependencies implicit in systems of mathematical equations. We show that
Simon's classical algorithm to infer causal ordering is NP-Hard---an
intractability previously guessed but never proven. We present then a detailed
account based on Nayak's suggested algorithmic solution (the best available),
which is dominated by computing transitive closure---bounded in time by
$O(|\mathcal V|\cdot |\mathcal S|)$, where $\mathcal S(\mathcal E, \mathcal V)$
is the input system structure composed of a set $\mathcal E$ of equations over
a set $\mathcal V$ of variables with number of variable appearances (density)
$|\mathcal S|$. We also comment on the potential of causal ordering for
emerging applications in large-scale hypothesis management and analytics.
Deep Image Homography Estimation
Authors: Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich
Date: 2016-06-13 00:00:00
Tasks: Homography Estimation

We present a deep convolutional neural network for estimating the relative
homography between a pair of images. Our feed-forward network has 10 layers,
takes two stacked grayscale images as input, and produces an 8 degree of
freedom homography which can be used to map the pixels from the first image to
the second. We present two convolutional neural network architectures for
HomographyNet: a regression network which directly estimates the real-valued
homography parameters, and a classification network which produces a
distribution over quantized homographies. We use a 4-point homography
parameterization which maps the four corners from one image into the second
image. Our networks are trained in an end-to-end fashion using warped MS-COCO
images. Our approach works without the need for separate local feature
detection and transformation estimation stages. Our deep models are compared to
a traditional homography estimator based on ORB features and we highlight the
scenarios where HomographyNet outperforms the traditional technique. We also
describe a variety of applications powered by deep homography estimation, thus
showcasing the flexibility of a deep learning approach.
Unsupervised Non Linear Dimensionality Reduction Machine Learning methods applied to Multiparametric MRI in cerebral ischemia: Preliminary Results
Authors: Vishwa S. Parekh, Jeremy R. Jacobs, Michael A. Jacobs
Date: 2016-06-13 00:00:00
Tasks: Diagnostic, Dimensionality Reduction

The evaluation and treatment of acute cerebral ischemia requires a technique
that can determine the total area of tissue at risk for infarction using
diagnostic magnetic resonance imaging (MRI) sequences. Typical MRI data sets
consist of T1- and T2-weighted imaging (T1WI, T2WI) along with advanced MRI
parameters of diffusion-weighted imaging (DWI) and perfusion weighted imaging
(PWI) methods. Each of these parameters has distinct radiological-pathological
meaning. For example, DWI interrogates the movement of water in the tissue and
PWI gives an estimate of the blood flow, both are critical measures during the
evolution of stroke. In order to integrate these data and give an estimate of
the tissue at risk or damaged, we have developed advanced machine learning
methods based on unsupervised non-linear dimensionality reduction (NLDR)
techniques. NLDR methods are a class of algorithms that uses mathematically
defined manifolds for statistical sampling of multidimensional classes to
generate a discrimination rule of guaranteed statistical accuracy and they can
generate a two- or three-dimensional map, which represents the prominent
structures of the data and provides an embedded image of meaningful
low-dimensional structures hidden in their high-dimensional observations. In
this manuscript, we develop NLDR methods on high dimensional MRI data sets of
preclinical animals and clinical patients with stroke. On analyzing the
performance of these methods, we observed that there was a high of similarity
between multiparametric embedded images from NLDR methods and the ADC map and
perfusion map. It was also observed that embedded scattergram of abnormal
(infarcted or at risk) tissue can be visualized and provides a mechanism for
automatic methods to delineate potential stroke volumes and early tissue at
risk.
MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection
Authors: Guido Zarrella, Amy Marsh
Date: 2016-06-13 00:00:00
Proceeding: SEMEVAL 2016 6
Tasks: Sentence, Stance Detection, Transfer Learning

We describe MITRE's submission to the SemEval-2016 Task 6, Detecting Stance
in Tweets. This effort achieved the top score in Task A on supervised stance
detection, producing an average F1 score of 67.8 when assessing whether a tweet
author was in favor or against a topic. We employed a recurrent neural network
initialized with features learned via distant supervision on two large
unlabeled datasets. We trained embeddings of words and phrases with the
word2vec skip-gram method, then used those features to learn sentence
representations via a hashtag prediction auxiliary task. These sentence vectors
were then fine-tuned for stance detection on several hundred labeled examples.
The result was a high performing system that used transfer learning to maximize
the value of the available training data.
Retrieving and Ranking Similar Questions from Question-Answer Archives Using Topic Modelling and Topic Distribution Regression
Authors: Pedro Chahuara, Thomas Lampert, Pierre Gancarski
Date: 2016-06-12 00:00:00
Tasks: regression, Translation

Presented herein is a novel model for similar question ranking within
collaborative question answer platforms. The presented approach integrates a
regression stage to relate topics derived from questions to those derived from
question-answer pairs. This helps to avoid problems caused by the differences
in vocabulary used within questions and answers, and the tendency for questions
to be shorter than answers. The performance of the model is shown to outperform
translation methods and topic modelling (without regression) on several
real-world datasets.
Human Centred Object Co-Segmentation
Authors: Chenxia Wu, Jiemi Zhang, Ashutosh Saxena, Silvio Savarese
Date: 2016-06-12 00:00:00
Tasks: Human-Object Interaction Detection, Object, Segmentation

Co-segmentation is the automatic extraction of the common semantic regions
given a set of images. Different from previous approaches mainly based on
object visuals, in this paper, we propose a human centred object
co-segmentation approach, which uses the human as another strong evidence. In
order to discover the rich internal structure of the objects reflecting their
human-object interactions and visual similarities, we propose an unsupervised
fully connected CRF auto-encoder incorporating the rich object features and a
novel human-object interaction representation. We propose an efficient learning
and inference algorithm to allow the full connectivity of the CRF with the
auto-encoder, that establishes pairwise relations on all pairs of the object
proposals in the dataset. Moreover, the auto-encoder learns the parameters from
the data itself rather than supervised learning or manually assigned parameters
in the conventional CRF. In the extensive experiments on four datasets, we show
that our approach is able to extract the common objects more accurately than
the state-of-the-art co-segmentation algorithms.
Adaptive Local Window for Level Set Segmentation of CT and MRI Liver Lesions
Authors: Assaf Hoogi, Christopher F. Beaulieu, Guilherme M. Cunha, Elhamy Heba, Claude B. Sirlin, Sandy Napel, Daniel L. Rubin
Date: 2016-06-12 00:00:00
Tasks: Segmentation

We propose a novel method, the adaptive local window, for improving level set
segmentation technique. The window is estimated separately for each contour
point, over iterations of the segmentation process, and for each individual
object. Our method considers the object scale, the spatial texture, and changes
of the energy functional over iterations. Global and local statistics are
considered by calculating several gray level co-occurrence matrices. We
demonstrate the capabilities of the method in the domain of medical imaging for
segmenting 233 images with liver lesions. To illustrate the strength of our
method, those images were obtained by either Computed Tomography or Magnetic
Resonance Imaging. Moreover, we analyzed images using three different energy
models. We compare our method to a global level set segmentation and to local
framework that uses predefined fixed-size square windows. The results indicate
that our proposed method outperforms the other methods in terms of agreement
with the manual marking and dependence on contour initialization or the energy
model used. In case of complex lesions, such as low contrast lesions,
heterogeneous lesions, or lesions with a noisy background, our method shows
significantly better segmentation with an improvement of 0.25+- 0.13 in Dice
similarity coefficient, compared with state of the art fixed-size local windows
(Wilcoxon, p < 0.001).
Human Pose Estimation with Iterative Error Feedback
Authors: Joao Carreira, Pulkit Agrawal, Katerina Fragkiadaki, Jitendra Malik
Date: 2015-07-23 00:00:00
Proceeding: CVPR 2016 6
Tasks: Pose Estimation, Semantic Segmentation

Hierarchical feature extractors such as Convolutional Networks (ConvNets)
have achieved impressive performance on a variety of classification tasks using
purely feedforward processing. Feedforward architectures can learn rich
representations of the input space but do not explicitly model dependencies in
the output spaces, that are quite structured for tasks such as articulated
human pose estimation or object segmentation. Here we propose a framework that
expands the expressive power of hierarchical feature extractors to encompass
both input and output spaces, by introducing top-down feedback. Instead of
directly predicting the outputs in one go, we use a self-correcting model that
progressively changes an initial solution by feeding back error predictions, in
a process we call Iterative Error Feedback (IEF). IEF shows excellent
performance on the task of articulated pose estimation in the challenging MPII
and LSP benchmarks, matching the state-of-the-art without requiring ground
truth scale annotation.