pwc-archive/papers-with-abstracts huggingface dataset:
Left Ventricle Segmentation in Cardiac MR Images Using Fully Convolutional Network
Authors: Mina Nasr-Esfahani, Majid Mohrekesh, Mojtaba Akbari, S. M. Reza Soroushmehr, Ebrahim Nasr-Esfahani, Nader Karimi, Shadrokh Samavi, Kayvan Najarian
Date: 2018-02-21 00:00:00
Tasks: Left Ventricle Segmentation, Medical Image Analysis

Medical image analysis, especially segmenting a specific organ, has an
important role in developing clinical decision support systems. In cardiac
magnetic resonance (MR) imaging, segmenting the left and right ventricles helps
physicians diagnose different heart abnormalities. There are challenges for
this task, including the intensity and shape similarity between left ventricle
and other organs, inaccurate boundaries and presence of noise in most of the
images. In this paper we propose an automated method for segmenting the left
ventricle in cardiac MR images. We first automatically extract the region of
interest, and then employ it as an input of a fully convolutional network. We
train the network accurately despite the small number of left ventricle pixels
in comparison with the whole image. Thresholding on the output map of the fully
convolutional network and selection of regions based on their roundness are
performed in our proposed post-processing phase. The Dice score of our method
reaches 87.24% by applying this algorithm on the York dataset of heart images.
Counting Motifs with Graph Sampling
Authors: Jason M. Klusowski, Yihong Wu
Date: 2018-02-21 00:00:00
Tasks: Graph Sampling

Applied researchers often construct a network from a random sample of nodes
in order to infer properties of the parent network. Two of the most widely used
sampling schemes are subgraph sampling, where we sample each vertex
independently with probability $p$ and observe the subgraph induced by the
sampled vertices, and neighborhood sampling, where we additionally observe the
edges between the sampled vertices and their neighbors.
  In this paper, we study the problem of estimating the number of motifs as
induced subgraphs under both models from a statistical perspective. We show
that: for any connected $h$ on $k$ vertices, to estimate $s=\mathsf{s}(h,G)$,
the number of copies of $h$ in the parent graph $G$ of maximum degree $d$, with
a multiplicative error of $\epsilon$, (a) For subgraph sampling, the optimal
sampling ratio $p$ is $\Theta_{k}(\max\{ (s\epsilon^2)^{-\frac{1}{k}}, \;
\frac{d^{k-1}}{s\epsilon^{2}} \})$, achieved by Horvitz-Thompson type of
estimators. (b) For neighborhood sampling, we propose a family of estimators,
encompassing and outperforming the Horvitz-Thompson estimator and achieving the
sampling ratio $O_{k}(\min\{ (\frac{d}{s\epsilon^2})^{\frac{1}{k-1}}, \;
\sqrt{\frac{d^{k-2}}{s\epsilon^2}}\})$. This is shown to be optimal for all
motifs with at most $4$ vertices and cliques of all sizes.
  The matching minimax lower bounds are established using certain algebraic
properties of subgraph counts. These results quantify how much more informative
neighborhood sampling is than subgraph sampling, as empirically verified by
experiments on both synthetic and real-world data. We also address the issue of
adaptation to the unknown maximum degree, and study specific problems for
parent graphs with additional structures, e.g., trees or planar graphs.
Learning Combinatorial Optimization Algorithms over Graphs
Authors: Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, Le Song
Date: 2017-04-05 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Combinatorial Optimization, Graph Embedding, Reinforcement Learning

The design of good heuristics or approximation algorithms for NP-hard
combinatorial optimization problems often requires significant specialized
knowledge and trial-and-error. Can we automate this challenging, tedious
process, and learn the algorithms instead? In many real-world applications, it
is typically the case that the same optimization problem is solved again and
again on a regular basis, maintaining the same problem structure but differing
in the data. This provides an opportunity for learning heuristic algorithms
that exploit the structure of such recurring problems. In this paper, we
propose a unique combination of reinforcement learning and graph embedding to
address this challenge. The learned greedy policy behaves like a meta-algorithm
that incrementally constructs a solution, and the action is determined by the
output of a graph embedding network capturing the current state of the
solution. We show that our framework can be applied to a diverse range of
optimization problems over graphs, and learns effective algorithms for the
Minimum Vertex Cover, Maximum Cut and Traveling Salesman problems.
Lossless Compression of Angiogram Foreground with Visual Quality Preservation of Background
Authors: Mahdi Ahmadi, Ali Emami, Mohsen Hajabdollahi, S. M. Reza Soroushmehr, Nader Karimi, Shadrokh Samavi, Kayvan Najarian
Date: 2018-02-21 00:00:00
Tasks: Image Compression

By increasing the volume of telemedicine information, the need for medical
image compression has become more important. In angiographic images, a small
ratio of the entire image usually belongs to the vasculature that provides
crucial information for diagnosis. Other parts of the image are diagnostically
less important and can be compressed with higher compression ratio. However,
the quality of those parts affect the visual perception of the image as well.
Existing methods compress foreground and background of angiographic images
using different techniques. In this paper we first utilize convolutional neural
network to segment vessels and then represent a hierarchical block processing
algorithm capable of both eliminating the background redundancies and
preserving the overall visual quality of angiograms.
Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications
Authors: Qinshi Wang, Wei Chen
Date: 2017-03-05 00:00:00
Proceeding: NeurIPS 2017 12

We study combinatorial multi-armed bandit with probabilistically triggered arms (CMAB-T) and semi-bandit feedback. We resolve a serious issue in the prior CMAB-T studies where the regret bounds contain a possibly exponentially large factor of $1/p^*$, where $p^*$ is the minimum positive probability that an arm is triggered by any action. We address this issue by introducing a triggering probability modulated (TPM) bounded smoothness condition into the general CMAB-T framework, and show that many applications such as influence maximization bandit and combinatorial cascading bandit satisfy this TPM condition. As a result, we completely remove the factor of $1/p^*$ from the regret bounds, achieving significantly better regret bounds for influence maximization and cascading bandits than before. Finally, we provide lower bound results showing that the factor $1/p^*$ is unavoidable for general CMAB-T problems, suggesting that the TPM condition is crucial in removing this factor.
Making "fetch" happen: The influence of social and linguistic context on nonstandard word growth and decline
Authors: Ian Stewart, Jacob Eisenstein
Date: 2017-09-01 00:00:00

In an online community, new words come and go: today's "haha" may be replaced
by tomorrow's "lol." Changes in online writing are usually studied as a social
process, with innovations diffusing through a network of individuals in a
speech community. But unlike other types of innovation, language change is
shaped and constrained by the system in which it takes part. To investigate the
links between social and structural factors in language change, we undertake a
large-scale analysis of nonstandard word growth in the online community Reddit.
We find that dissemination across many linguistic contexts is a sign of growth:
words that appear in more linguistic contexts grow faster and survive longer.
We also find that social dissemination likely plays a less important role in
explaining word growth and decline than previously hypothesized.
Detecting Learning vs Memorization in Deep Neural Networks using Shared Structure Validation Sets
Authors: Elias Chaibub Neto
Date: 2018-02-21 00:00:00
Tasks: Memorization

The roles played by learning and memorization represent an important topic in
deep learning research. Recent work on this subject has shown that the
optimization behavior of DNNs trained on shuffled labels is qualitatively
different from DNNs trained with real labels. Here, we propose a novel
permutation approach that can differentiate memorization from learning in deep
neural networks (DNNs) trained as usual (i.e., using the real labels to guide
the learning, rather than shuffled labels). The evaluation of weather the DNN
has learned and/or memorized, happens in a separate step where we compare the
predictive performance of a shallow classifier trained with the features
learned by the DNN, against multiple instances of the same classifier, trained
on the same input, but using shuffled labels as outputs. By evaluating these
shallow classifiers in validation sets that share structure with the training
set, we are able to tell apart learning from memorization. Application of our
permutation approach to multi-layer perceptrons and convolutional neural
networks trained on image data corroborated many findings from other groups.
Most importantly, our illustrations also uncovered interesting dynamic patterns
about how DNNs memorize over increasing numbers of training epochs, and support
the surprising result that DNNs are still able to learn, rather than only
memorize, when trained with pure Gaussian noise as input.
Algorithmic Collusion in Cournot Duopoly Market: Evidence from Experimental Economics
Authors: Nan Zhou, Li Zhang, Shijian Li, Zhijian Wang
Date: 2018-02-21 00:00:00

Algorithmic collusion is an emerging concept in current artificial
intelligence age. Whether algorithmic collusion is a creditable threat remains
as an argument. In this paper, we propose an algorithm which can extort its
human rival to collude in a Cournot duopoly competing market. In experiments,
we show that, the algorithm can successfully extorted its human rival and gets
higher profit in long run, meanwhile the human rival will fully collude with
the algorithm. As a result, the social welfare declines rapidly and stably.
Both in theory and in experiment, our work confirms that, algorithmic collusion
can be a creditable threat. In application, we hope, the frameworks, the
algorithm design as well as the experiment environment illustrated in this
work, can be an incubator or a test bed for researchers and policymakers to
handle the emerging algorithmic collusion.
Approximation Algorithms for Cascading Prediction Models
Authors: Matthew Streeter
Date: 2018-02-21 00:00:00
Proceeding: ICML 2018 7
Tasks: General Classification, Prediction

We present an approximation algorithm that takes a pool of pre-trained models
as input and produces from it a cascaded model with similar accuracy but lower
average-case cost. Applied to state-of-the-art ImageNet classification models,
this yields up to a 2x reduction in floating point multiplications, and up to a
6x reduction in average-case memory I/O. The auto-generated cascades exhibit
intuitive properties, such as using lower-resolution input for easier images
and requiring higher prediction confidence when using a computationally cheaper
model.
Predicting Natural Hazards with Neuronal Networks
Authors: Matthias Rauter, Daniel Winkler
Date: 2018-02-21 00:00:00
Tasks: General Classification, image-classification, Image Classification, Physical Simulations, Semantic Segmentation

Gravitational mass flows, such as avalanches, debris flows and rockfalls are
common events in alpine regions with high impact on transport routes. Within
the last few decades, hazard zone maps have been developed to systematically
approach this threat. These maps mark vulnerable zones in habitable areas to
allow effective planning of hazard mitigation measures and development of
settlements. Hazard zone maps have shown to be an effective tool to reduce
fatalities during extreme events. They are created in a complex process, based
on experience, empirical models, physical simulations and historical data. The
generation of such maps is therefore expensive and limited to crucially
important regions, e.g. permanently inhabited areas.
  In this work we interpret the task of hazard zone mapping as a classification
problem. Every point in a specific area has to be classified according to its
vulnerability. On a regional scale this leads to a segmentation problem, where
the total area has to be divided in the respective hazard zones. The recent
developments in artificial intelligence, namely convolutional neuronal
networks, have led to major improvement in a very similar task, image
classification and semantic segmentation, i.e. computer vision. We use a
convolutional neuronal network to identify terrain formations with the
potential for catastrophic snow avalanches and label points in their reach as
vulnerable. Repeating this procedure for all points allows us to generate an
artificial hazard zone map. We demonstrate that the approach is feasible and
promising based on the hazard zone map of the Tirolean Oberland. However, more
training data and further improvement of the method is required before such
techniques can be applied reliably.
Learning Multiple Categories on Deep Convolution Networks
Authors: Mohamed Hajaj, Duncan Gillies
Date: 2018-02-21 00:00:00

Deep convolution networks have proved very successful with big datasets such
as the 1000-classes ImageNet. Results show that the error rate increases slowly
as the size of the dataset increases. Experiments presented here may explain
why these networks are very effective in solving big recognition problems. If
the big task is made up of multiple smaller tasks, then the results show the
ability of deep convolution networks to decompose the complex task into a
number of smaller tasks and to learn them simultaneously. The results show that
the performance of solving the big task on a single network is very close to
the average performance of solving each of the smaller tasks on a separate
network. Experiments also show the advantage of using task specific or category
labels in combination with class labels.
Self Paced Deep Learning for Weakly Supervised Object Detection
Authors: Enver Sangineto, Moin Nabi, Dubravko Culibrk, Nicu Sebe
Date: 2016-05-24 00:00:00
Tasks: Deep Learning, Multiple Instance Learning, Object, object-detection, Object Detection, Weakly Supervised Object Detection

In a weakly-supervised scenario object detectors need to be trained using
image-level annotation alone. Since bounding-box-level ground truth is not
available, most of the solutions proposed so far are based on an iterative,
Multiple Instance Learning framework in which the current classifier is used to
select the highest-confidence boxes in each image, which are treated as
pseudo-ground truth in the next training iteration. However, the errors of an
immature classifier can make the process drift, usually introducing many of
false positives in the training dataset. To alleviate this problem, we propose
in this paper a training protocol based on the self-paced learning paradigm.
The main idea is to iteratively select a subset of images and boxes that are
the most reliable, and use them for training. While in the past few years
similar strategies have been adopted for SVMs and other classifiers, we are the
first showing that a self-paced approach can be used with deep-network-based
classifiers in an end-to-end training pipeline. The method we propose is built
on the fully-supervised Fast-RCNN architecture and can be applied to similar
architectures which represent the input image as a bag of boxes. We show
state-of-the-art results on Pascal VOC 2007, Pascal VOC 2010 and ILSVRC 2013.
On ILSVRC 2013 our results based on a low-capacity AlexNet network outperform
even those weakly-supervised approaches which are based on much higher-capacity
networks.
Building Efficient ConvNets using Redundant Feature Pruning
Authors: Babajide O. Ayinde, Jacek M. Zurada
Date: 2018-02-21 00:00:00
Tasks: Network Pruning

This paper presents an efficient technique to prune deep and/or wide
convolutional neural network models by eliminating redundant features (or
filters). Previous studies have shown that over-sized deep neural network
models tend to produce a lot of redundant features that are either shifted
version of one another or are very similar and show little or no variations;
thus resulting in filtering redundancy. We propose to prune these redundant
features along with their connecting feature maps according to their
differentiation and based on their relative cosine distances in the feature
space, thus yielding smaller network size with reduced inference costs and
competitive performance. We empirically show on select models and CIFAR-10
dataset that inference costs can be reduced by 40% for VGG-16, 27% for
ResNet-56, and 39% for ResNet-110.
JESC: Japanese-English Subtitle Corpus
Authors: Reid Pryzant, Yongjoo Chung, Dan Jurafsky, Denny Britz
Date: 2017-10-29 00:00:00
Proceeding: LREC 2018 5
Tasks: Machine Translation, Translation

In this paper we describe the Japanese-English Subtitle Corpus (JESC). JESC
is a large Japanese-English parallel corpus covering the underrepresented
domain of conversational dialogue. It consists of more than 3.2 million
examples, making it the largest freely available dataset of its kind. The
corpus was assembled by crawling and aligning subtitles found on the web. The
assembly process incorporates a number of novel preprocessing elements to
ensure high monolingual fluency and accurate bilingual alignments. We summarize
its contents and evaluate its quality using human experts and baseline machine
translation (MT) systems.
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
Authors: Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Pai-Shun Ting, Karthikeyan Shanmugam, Payel Das
Date: 2018-02-21 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: General Classification

In this paper we propose a novel method that provides contrastive
explanations justifying the classification of an input by a black box
classifier such as a deep neural network. Given an input we find what should be
%necessarily and minimally and sufficiently present (viz. important object
pixels in an image) to justify its classification and analogously what should
be minimally and necessarily \emph{absent} (viz. certain background pixels). We
argue that such explanations are natural for humans and are used commonly in
domains such as health care and criminology. What is minimally but critically
\emph{absent} is an important part of an explanation, which to the best of our
knowledge, has not been explicitly identified by current explanation methods
that explain predictions of neural networks. We validate our approach on three
real datasets obtained from diverse domains; namely, a handwritten digits
dataset MNIST, a large procurement fraud dataset and a brain activity strength
dataset. In all three cases, we witness the power of our approach in generating
precise explanations that are also easy for human experts to understand and
evaluate.
Cooperating with Machines
Authors: Jacob W. Crandall, Mayada Oudah, Tennom, Fatimah Ishowo-Oloko, Sherief Abdallah, Jean-François Bonnefon, Manuel Cebrian, Azim Shariff, Michael A. Goodrich, Iyad Rahwan
Date: 2017-03-17 00:00:00
Tasks: Common Sense Reasoning, Face Recognition

Since Alan Turing envisioned Artificial Intelligence (AI) [1], a major
driving force behind technical progress has been competition with human
cognition. Historical milestones have been frequently associated with computers
matching or outperforming humans in difficult cognitive tasks (e.g. face
recognition [2], personality classification [3], driving cars [4], or playing
video games [5]), or defeating humans in strategic zero-sum encounters (e.g.
Chess [6], Checkers [7], Jeopardy! [8], Poker [9], or Go [10]). In contrast,
less attention has been given to developing autonomous machines that establish
mutually cooperative relationships with people who may not share the machine's
preferences. A main challenge has been that human cooperation does not require
sheer computational power, but rather relies on intuition [11], cultural norms
[12], emotions and signals [13, 14, 15, 16], and pre-evolved dispositions
toward cooperation [17], common-sense mechanisms that are difficult to encode
in machines for arbitrary contexts. Here, we combine a state-of-the-art
machine-learning algorithm with novel mechanisms for generating and acting on
signals to produce a new learning algorithm that cooperates with people and
other machines at levels that rival human cooperation in a variety of
two-player repeated stochastic games. This is the first general-purpose
algorithm that is capable, given a description of a previously unseen game
environment, of learning to cooperate with people within short timescales in
scenarios previously unanticipated by algorithm designers. This is achieved
without complex opponent modeling or higher-order theories of mind, thus
showing that flexible, fast, and general human-machine cooperation is
computationally achievable using a non-trivial, but ultimately simple, set of
algorithmic mechanisms.
Semantic White Balance: Semantic Color Constancy Using Convolutional Neural Network
Authors: Mahmoud Afifi
Date: 2018-02-01 00:00:00
Tasks: Color Constancy, Semantic Segmentation

The goal of computational color constancy is to preserve the perceptive colors of objects under different lighting conditions by removing the effect of color casts caused by the scene's illumination. With the rapid development of deep learning based techniques, significant progress has been made in image semantic segmentation. In this work, we exploit the semantic information together with the color and spatial information of the input image in order to remove color casts. We train a convolutional neural network (CNN) model that learns to estimate the illuminant color and gamma correction parameters based on the semantic information of the given image. Experimental results show that feeding the CNN with the semantic information leads to a significant improvement in the results by reducing the error by more than 40%.
BENGAL: An Automatic Benchmark Generator for Entity Recognition and Linking
Authors: Axel-Cyrille Ngonga Ngomo, Michael Röder, Diego Moussallem, Ricardo Usbeck, René Speck
Date: 2017-10-24 00:00:00
Proceeding: WS 2018 11
Tasks: Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

The manual creation of gold standards for named entity recognition and entity
linking is time- and resource-intensive. Moreover, recent works show that such
gold standards contain a large proportion of mistakes in addition to being
difficult to maintain. We hence present BENGAL, a novel automatic generation of
such gold standards as a complement to manually created benchmarks. The main
advantage of our benchmarks is that they can be readily generated at any time.
They are also cost-effective while being guaranteed to be free of annotation
errors. We compare the performance of 11 tools on benchmarks in English
generated by BENGAL and on 16benchmarks created manually. We show that our
approach can be ported easily across languages by presenting results achieved
by 4 tools on both Brazilian Portuguese and Spanish. Overall, our results
suggest that our automatic benchmark generation approach can create varied
benchmarks that have characteristics similar to those of existing benchmarks.
Our approach is open-source. Our experimental results are available at
http://faturl.com/bengalexpinlg and the code at
https://github.com/dice-group/BENGAL.