pwc-archive/papers-with-abstracts huggingface dataset:
Fast PET Scan Tumor Segmentation using Superpixels, Principal Component Analysis and K-means Clustering
Authors: Yeman B. Hagos, Vu H. Minh, Saed Khawaldeh, Usama Pervaiz, Tajwar A. Aleef
Date: 2017-10-18 00:00:00
Tasks: Clustering, Superpixels, Tumor Segmentation

Positron Emission Tomography scan images are extensively used in radiotherapy
planning, clinical diagnosis, assessment of growth and treatment of a tumor.
These all rely on fidelity and speed of detection and delineation algorithm.
Despite intensive research, segmentation remained a challenging problem due to
the diverse image content, resolution, shape, and noise. This paper presents a
fast positron emission tomography tumor segmentation method in which
superpixels are extracted first from the input image. Principal component
analysis is then applied on the superpixels and also on their average. Distance
vector of each superpixel from the average is computed in principal components
coordinate system. Finally, k-means clustering is applied on distance vector to
recognize tumor and non-tumor superpixels. The proposed approach is implemented
in MATLAB 2016 which resulted in an average Dice similarity of 84.2% on the
dataset. Additionally, a very fast execution time was achieved as the number of
superpixels and the size of distance vector on which clustering was done was
very small compared to the number of raw pixels in dataset images.
SSH: Single Stage Headless Face Detector
Authors: Mahyar Najibi, Pouya Samangouei, Rama Chellappa, Larry Davis
Date: 2017-08-14 00:00:00
Proceeding: ICCV 2017 10
Tasks: General Classification, GPU

We introduce the Single Stage Headless (SSH) face detector. Unlike two stage
proposal-classification detectors, SSH detects faces in a single stage directly
from the early convolutional layers in a classification network. SSH is
headless. That is, it is able to achieve state-of-the-art results while
removing the "head" of its underlying classification network -- i.e. all fully
connected layers in the VGG-16 which contains a large number of parameters.
Additionally, instead of relying on an image pyramid to detect faces with
various scales, SSH is scale-invariant by design. We simultaneously detect
faces with different scales in a single forward pass of the network, but from
different layers. These properties make SSH fast and light-weight.
Surprisingly, with a headless VGG-16, SSH beats the ResNet-101-based
state-of-the-art on the WIDER dataset. Even though, unlike the current
state-of-the-art, SSH does not use an image pyramid and is 5X faster. Moreover,
if an image pyramid is deployed, our light-weight network achieves
state-of-the-art on all subsets of the WIDER dataset, improving the AP by 2.5%.
SSH also reaches state-of-the-art results on the FDDB and Pascal-Faces datasets
while using a small input size, leading to a runtime of 50 ms/image on a GPU.
The code is available at https://github.com/mahyarnajibi/SSH.
Multi-Task Label Embedding for Text Classification
Authors: Honglun Zhang, Liqiang Xiao, Wenqing Chen, Yongkun Wang, Yaohui Jin
Date: 2017-10-17 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Classification, General Classification, Multi-Task Learning, text-classification, Text Classification

Multi-task learning in text classification leverages implicit correlations
among related tasks to extract common features and yield performance gains.
However, most previous works treat labels of each task as independent and
meaningless one-hot vectors, which cause a loss of potential information and
makes it difficult for these models to jointly learn three or more tasks. In
this paper, we propose Multi-Task Label Embedding to convert labels in text
classification into semantic vectors, thereby turning the original tasks into
vector matching tasks. We implement unsupervised, supervised and
semi-supervised models of Multi-Task Label Embedding, all utilizing semantic
correlations among tasks and making it particularly convenient to scale and
transfer as more tasks are involved. Extensive experiments on five benchmark
datasets for text classification show that our models can effectively improve
performances of related tasks with semantic representations of labels and
additional information from each other.
Material Classification using Neural Networks
Authors: Anca Sticlaru
Date: 2017-10-17 00:00:00
Tasks: Classification, General Classification, Material Classification, Transfer Learning

The recognition and classification of the diversity of materials that exist
in the environment around us are a key visual competence that computer vision
systems focus on in recent years. Understanding the identification of materials
in distinct images involves a deep process that has made usage of the recent
progress in neural networks which has brought the potential to train
architectures to extract features for this challenging task. This project uses
state-of-the-art Convolutional Neural Network (CNN) techniques and Support
Vector Machine (SVM) classifiers in order to classify materials and analyze the
results. Building on various widely used material databases collected, a
selection of CNN architectures is evaluated to understand which is the best
approach to extract features in order to achieve outstanding results for the
task. The results gathered over four material datasets and nine CNNs outline
that the best overall performance of a CNN using a linear SVM can achieve up to
~92.5% mean average precision, while applying a new relevant direction in
computer vision, transfer learning. By limiting the amount of information
extracted from the layer before the last fully connected layer, transfer
learning aims at analyzing the contribution of shading information and
reflectance to identify which main characteristics decide the material category
the image belongs to. In addition to the main topic of my project, the
evaluation of the nine different CNN architectures, it is questioned if, by
using the transfer learning instead of extracting the information from the last
convolutional layer, the total accuracy of the system created improves. The
results of the comparison emphasize the fact that the accuracy and performance
of the system improve, especially in the datasets which consist of a large
number of images.
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems
Authors: Trong Nghia Hoang, Yuchen Xiao, Kavinayan Sivakumar, Christopher Amato, Jonathan How
Date: 2017-10-17 00:00:00
Tasks: Decision Making

A key challenge in multi-robot and multi-agent systems is generating
solutions that are robust to other self-interested or even adversarial parties
who actively try to prevent the agents from achieving their goals. The
practicality of existing works addressing this challenge is limited to only
small-scale synchronous decision-making scenarios or a single agent planning
its best response against a single adversary with fixed, procedurally
characterized strategies. In contrast this paper considers a more realistic
class of problems where a team of asynchronous agents with limited observation
and communication capabilities need to compete against multiple strategic
adversaries with changing strategies. This problem necessitates agents that can
coordinate to detect changes in adversary strategies and plan the best response
accordingly. Our approach first optimizes a set of stratagems that represent
these best responses. These optimized stratagems are then integrated into a
unified policy that can detect and respond when the adversaries change their
strategies. The near-optimality of the proposed framework is established
theoretically as well as demonstrated empirically in simulation and hardware.
LASAGNE: Locality And Structure Aware Graph Node Embedding
Authors: Evgeniy Faerman, Felix Borutta, Kimon Fountoulakis, Michael W. Mahoney
Date: 2017-10-17 00:00:00
Tasks: Link Prediction, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

In this work we propose Lasagne, a methodology to learn locality and
structure aware graph node embeddings in an unsupervised way. In particular, we
show that the performance of existing random-walk based approaches depends
strongly on the structural properties of the graph, e.g., the size of the
graph, whether the graph has a flat or upward-sloping Network Community Profile
(NCP), whether the graph is expander-like, whether the classes of interest are
more k-core-like or more peripheral, etc. For larger graphs with flat NCPs that
are strongly expander-like, existing methods lead to random walks that expand
rapidly, touching many dissimilar nodes, thereby leading to lower-quality
vector representations that are less useful for downstream tasks. Rather than
relying on global random walks or neighbors within fixed hop distances, Lasagne
exploits strongly local Approximate Personalized PageRank stationary
distributions to more precisely engineer local information into node
embeddings. This leads, in particular, to more meaningful and more useful
vector representations of nodes in poorly-structured graphs. We show that
Lasagne leads to significant improvement in downstream multi-label
classification for larger graphs with flat NCPs, that it is comparable for
smaller graphs with upward-sloping NCPs, and that is comparable to existing
methods for link prediction tasks.
Sistema de Navegação Autônomo Baseado em Visão Computacional
Authors: Michel Conrado Cardoso Meneses
Date: 2017-10-17 00:00:00
Tasks: Optical Flow Estimation

Autonomous robots are used as the tool to solve many kinds of problems, such
as environmental mapping and monitoring. Either for adverse conditions related
to the human presence or even for the need to reduce costs, it is certain that
many efforts have been made to develop robots with an increasingly high level
of autonomy. They must be capable of locomotion through dynamic environments,
without human operators or assistant systems' help. It is noted, thus, that the
form of perception and modeling of the environment becomes significantly
relevant to navigation. Among the main sensing methods are those based on
vision. Through this, it is possible to create highly-detailed models about the
environment, since many characteristics can be measured, such as texture,
color, and illumination. However, the most accurate vision-based navigation
techniques are computationally expensive to run on low-cost mobile platforms.
Therefore, the goal of this work was to develop a low-cost robot, controlled by
a Raspberry Pi, whose navigation system is based on vision. For this purpose,
the strategy used consisted in identifying obstacles via optical flow pattern
recognition. Through this signal, it is possible to infer the relative
displacement between the robot and other elements in the environment. Its
estimation was done using the Lucas-Kanade algorithm, which can be executed by
the Raspberry Pi without harming its performance. Finally, an SVM based
classifier was used to identify patterns of this signal associated with
obstacles movement. The developed system was evaluated considering its
execution over an optical flow pattern dataset extracted from a real navigation
environment. In the end, it was verified that the processing frequency of the
system was superior to the others. Furthermore, its accuracy and acquisition
cost were, respectively, higher and lower than most of the cited works.
Robust importance-weighted cross-validation under sample selection bias
Authors: Wouter M. Kouw, Jesse H. Krijthe, Marco Loog
Date: 2017-10-17 00:00:00
Tasks: General Classification, Selection bias

Cross-validation under sample selection bias can, in principle, be done by importance-weighting the empirical risk. However, the importance-weighted risk estimator produces sub-optimal hyperparameter estimates in problem settings where large weights arise with high probability. We study its sampling variance as a function of the training data distribution and introduce a control variate to increase its robustness to problematically large weights.
Do Convolutional Neural Networks Learn Class Hierarchy?
Authors: Bilal Alsallakh, Amin Jourabloo, Mao Ye, Xiaoming Liu, Liu Ren
Date: 2017-10-17 00:00:00
Tasks: image-classification, Image Classification

Convolutional Neural Networks (CNNs) currently achieve state-of-the-art
accuracy in image classification. With a growing number of classes, the
accuracy usually drops as the possibilities of confusion increase.
Interestingly, the class confusion patterns follow a hierarchical structure
over the classes. We present visual-analytics methods to reveal and analyze
this hierarchy of similar classes in relation with CNN-internal data. We found
that this hierarchy not only dictates the confusion patterns between the
classes, it furthermore dictates the learning behavior of CNNs. In particular,
the early layers in these networks develop feature detectors that can separate
high-level groups of classes quite well, even after a few training epochs. In
contrast, the latter layers require substantially more epochs to develop
specialized feature detectors that can separate individual classes. We
demonstrate how these insights are key to significant improvement in accuracy
by designing hierarchy-aware CNNs that accelerate model convergence and
alleviate overfitting. We further demonstrate how our methods help in
identifying various quality issues in the training data.
A Line-Point Unified Solution to Relative Camera Pose Estimation
Authors: Ashraf Qadir, Jeremiah Neubert
Date: 2017-10-17 00:00:00
Tasks: Camera Pose Estimation, Pose Estimation

In this work we present a unified method of relative camera pose estimation
from points and lines correspondences. Given a set of 2D points and lines
correspondences in three views, of which two are known, a method has been
developed for estimating the camera pose of the third view. Novelty of this
algorithm is to combine both points and lines correspondences in the camera
pose estimation which enables us to compute relative camera pose with a small
number of feature correspondences. Our central idea is to exploit the
tri-linear relationship between three views and generate a set of linear
equations from the points and lines correspondences in the three views. The
desired solution to the system of equations are expressed as a linear
combination of the singular vectors and the coefficients are computed by
solving a small set of quadratic equations generated by imposing orthonormality
constraints for general camera motion. The advantages of the proposed method
are demonstrated by experimenting on publicly available data set. Results show
the robustness and efficiency of the method in relative camera pose estimation
for both small and large camera motion with a small set of points and line
features.
Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents
Authors: Sun Kim, Nicolas Fiorini, W. John Wilbur, Zhiyong Lu
Date: 2016-08-05 00:00:00
Tasks: Information Retrieval, Learning-To-Rank, Retrieval, Semantic Similarity, Semantic Textual Similarity, Word Embeddings

The main approach of traditional information retrieval (IR) is to examine how
many words from a query appear in a document. A drawback of this approach,
however, is that it may fail to detect relevant documents where no or only few
words from a query are found. The semantic analysis methods such as LSA (latent
semantic analysis) and LDA (latent Dirichlet allocation) have been proposed to
address the issue, but their performance is not superior compared to common IR
approaches. Here we present a query-document similarity measure motivated by
the Word Mover's Distance. Unlike other similarity measures, the proposed
method relies on neural word embeddings to compute the distance between words.
This process helps identify related words when no direct matches are found
between a query and a document. Our method is efficient and straightforward to
implement. The experimental results on TREC Genomics data show that our
approach outperforms the BM25 ranking function by an average of 12% in mean
average precision. Furthermore, for a real-world dataset collected from the
PubMed search logs, we combine the semantic measure with BM25 using a learning
to rank method, which leads to improved ranking scores by up to 25%. This
experiment demonstrates that the proposed approach and BM25 nicely complement
each other and together produce superior performance.
Coded Fourier Transform
Authors: Qian Yu, Mohammad Ali Maddah-Ali, A. Salman Avestimehr
Date: 2017-10-17 00:00:00

We consider the problem of computing the Fourier transform of
high-dimensional vectors, distributedly over a cluster of machines consisting
of a master node and multiple worker nodes, where the worker nodes can only
store and process a fraction of the inputs. We show that by exploiting the
algebraic structure of the Fourier transform operation and leveraging concepts
from coding theory, one can efficiently deal with the straggler effects. In
particular, we propose a computation strategy, named as coded FFT, which
achieves the optimal recovery threshold, defined as the minimum number of
workers that the master node needs to wait for in order to compute the output.
This is the first code that achieves the optimum robustness in terms of
tolerating stragglers or failures for computing Fourier transforms.
Furthermore, the reconstruction process for coded FFT can be mapped to MDS
decoding, which can be solved efficiently. Moreover, we extend coded FFT to
settings including computing general $n$-dimensional Fourier transforms, and
provide the optimal computing strategy for those settings.
Learning Inverse Statics Models Efficiently
Authors: Rania Rayyes, Daniel Kubus, Carsten Hartmann, Jochen Steil
Date: 2017-10-17 00:00:00

Online Goal Babbling and Direction Sampling are recently proposed methods for
direct learning of inverse kinematics mappings from scratch even in
high-dimensional sensorimotor spaces following the paradigm of "learning while
behaving". To learn inverse statics mappings - primarily for gravity
compensation - from scratch and without using any closed-loop controller, we
modify and enhance the Online Goal Babbling and Direction Sampling schemes.
Moreover, we exploit symmetries in the inverse statics mappings to drastically
reduce the number of samples required for learning inverse statics models.
Results for a 2R planar robot, a 3R simplified human arm, and a 4R humanoid
robot arm clearly demonstrate that their inverse statics mappings can be
learned successfully with our modified online Goal Babbling scheme.
Furthermore, we show that the number of samples required for the 2R and 3R arms
can be reduced by a factor of at least 8 and 16 resp. -depending on the number
of discovered symmetries.
MBMF: Model-Based Priors for Model-Free Reinforcement Learning
Authors: Somil Bansal, Roberto Calandra, Kurtland Chua, Sergey Levine, Claire Tomlin
Date: 2017-09-10 00:00:00
Tasks: model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement Learning is divided in two main paradigms: model-free and
model-based. Each of these two paradigms has strengths and limitations, and has
been successfully applied to real world domains that are appropriate to its
corresponding strengths. In this paper, we present a new approach aimed at
bridging the gap between these two paradigms. We aim to take the best of the
two paradigms and combine them in an approach that is at the same time
data-efficient and cost-savvy. We do so by learning a probabilistic dynamics
model and leveraging it as a prior for the intertwined model-free optimization.
As a result, our approach can exploit the generality and structure of the
dynamics model, but is also capable of ignoring its inevitable inaccuracies, by
directly incorporating the evidence provided by the direct observation of the
cost. Preliminary results demonstrate that our approach outperforms purely
model-based and model-free approaches, as well as the approach of simply
switching from a model-based to a model-free setting.
Laying Down the Yellow Brick Road: Development of a Wizard-of-Oz Interface for Collecting Human-Robot Dialogue
Authors: Claire Bonial, Matthew Marge, Ron artstein, Ashley Foots, Felix Gervits, Cory J. Hayes, Cassidy Henry, Susan G. Hill, Anton Leuski, Stephanie M. Lukin, Pooja Moolchandani, Kimberly A. Pollard, David Traum, Clare R. Voss
Date: 2017-10-17 00:00:00
Tasks: Robot Navigation

We describe the adaptation and refinement of a graphical user interface
designed to facilitate a Wizard-of-Oz (WoZ) approach to collecting human-robot
dialogue data. The data collected will be used to develop a dialogue system for
robot navigation. Building on an interface previously used in the development
of dialogue systems for virtual agents and video playback, we add templates
with open parameters which allow the wizard to quickly produce a wide variety
of utterances. Our research demonstrates that this approach to data collection
is viable as an intermediate step in developing a dialogue system for physical
robots in remote locations from their users - a domain in which the human and
robot need to regularly verify and update a shared understanding of the
physical environment. We show that our WoZ interface and the fixed set of
utterances and templates therein provide for a natural pace of dialogue with
good coverage of the navigation domain.
Embedding-Based Speaker Adaptive Training of Deep Neural Networks
Authors: Xiaodong Cui, Vaibhava Goel, George Saon
Date: 2017-10-17 00:00:00
Tasks: speech-recognition, Speech Recognition

An embedding-based speaker adaptive training (SAT) approach is proposed and
investigated in this paper for deep neural network acoustic modeling. In this
approach, speaker embedding vectors, which are a constant given a particular
speaker, are mapped through a control network to layer-dependent element-wise
affine transformations to canonicalize the internal feature representations at
the output of hidden layers of a main network. The control network for
generating the speaker-dependent mappings is jointly estimated with the main
network for the overall speaker adaptive acoustic modeling. Experiments on
large vocabulary continuous speech recognition (LVCSR) tasks show that the
proposed SAT scheme can yield superior performance over the widely-used
speaker-aware training using i-vectors with speaker-adapted input features.
RETUYT in TASS 2017: Sentiment Analysis for Spanish Tweets using SVM and CNN
Authors: Aiala Rosá, Luis Chiruzzo, Mathias Etcheverry, Santiago Castro
Date: 2017-10-17 00:00:00
Tasks: Sentiment Analysis, Word Embeddings

This article presents classifiers based on SVM and Convolutional Neural
Networks (CNN) for the TASS 2017 challenge on tweets sentiment analysis. The
classifier with the best performance in general uses a combination of SVM and
CNN. The use of word embeddings was particularly useful for improving the
classifiers performance.