pwc-archive/papers-with-abstracts huggingface dataset:
The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations
Authors: Nikita Nangia, Adina Williams, Angeliki Lazaridou, Samuel R. Bowman
Date: 2017-07-25 00:00:00
Proceeding: WS 2017 9
Tasks: Natural Language Inference, Representation Learning, Sentence

This paper presents the results of the RepEval 2017 Shared Task, which
evaluated neural network sentence representation learning models on the
Multi-Genre Natural Language Inference corpus (MultiNLI) recently introduced by
Williams et al. (2017). All of the five participating teams beat the
bidirectional LSTM (BiLSTM) and continuous bag of words baselines reported in
Williams et al.. The best single model used stacked BiLSTMs with residual
connections to extract sentence features and reached 74.5% accuracy on the
genre-matched test set. Surprisingly, the results of the competition were
fairly consistent across the genre-matched and genre-mismatched test sets, and
across subsets of the test data representing a variety of linguistic phenomena,
suggesting that all of the submitted systems learned reasonably
domain-independent representations for sentence meaning.
Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images -- A Cross-Site Robustness Assessment
Authors: Marc Aubreville, Miguel Goncalves, Christian Knipfer, Nicolai Oetter, Tobias Wuerfl, Helmut Neumann, Florian Stelzle, Christopher Bohr, Andreas Maier
Date: 2017-07-25 00:00:00

Deep learning technologies such as convolutional neural networks (CNN) provide powerful methods for image recognition and have recently been employed in the field of automated carcinoma detection in confocal laser endomicroscopy (CLE) images. CLE is a (sub-)surface microscopic imaging technique that reaches magnifications of up to 1000x and is thus suitable for in vivo structural tissue analysis. In this work, we aim to evaluate the prospects of a priorly developed deep learning-based algorithm targeted at the identification of oral squamous cell carcinoma with regard to its generalization to further anatomic locations of squamous cell carcinomas in the area of head and neck. We applied the algorithm on images acquired from the vocal fold area of five patients with histologically verified squamous cell carcinoma and presumably healthy control images of the clinically normal contra-lateral vocal cord. We find that the network trained on the oral cavity data reaches an accuracy of 89.45% and an area-under-the-curve (AUC) value of 0.955, when applied on the vocal cords data. Compared to the state of the art, we achieve very similar results, yet with an algorithm that was trained on a completely disjunct data set. Concatenating both data sets yielded further improvements in cross-validation with an accuracy of 90.81% and AUC of 0.970. In this study, for the first time to our knowledge, a deep learning mechanism for the identification of oral carcinomas using CLE Images could be applied to other disciplines in the area of head and neck. This study shows the prospect of the algorithmic approach to generalize well on other malignant entities of the head and neck, regardless of the anatomical location and furthermore in an examiner-independent manner.
Analogs of Linguistic Structure in Deep Representations
Authors: Jacob Andreas, Dan Klein
Date: 2017-07-25 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Negation

We investigate the compositional structure of message vectors computed by a
deep network trained on a communication game. By comparing truth-conditional
representations of encoder-produced message vectors to human-produced referring
expressions, we are able to identify aligned (vector, utterance) pairs with the
same meaning. We then search for structured relationships among these aligned
pairs to discover simple vector space transformations corresponding to
negation, conjunction, and disjunction. Our results suggest that neural
representations are capable of spontaneously developing a "syntax" with
functional analogues to qualitative properties of natural language.
Coordinating Filters for Faster Deep Neural Networks
Authors: Wei Wen, Cong Xu, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li
Date: 2017-03-28 00:00:00
Proceeding: ICCV 2017 10
Tasks: CPU, GPU

Very large-scale Deep Neural Networks (DNNs) have achieved remarkable
successes in a large variety of computer vision tasks. However, the high
computation intensity of DNNs makes it challenging to deploy these models on
resource-limited systems. Some studies used low-rank approaches that
approximate the filters by low-rank basis to accelerate the testing. Those
works directly decomposed the pre-trained DNNs by Low-Rank Approximations
(LRA). How to train DNNs toward lower-rank space for more efficient DNNs,
however, remains as an open area. To solve the issue, in this work, we propose
Force Regularization, which uses attractive forces to enforce filters so as to
coordinate more weight information into lower-rank space. We mathematically and
empirically verify that after applying our technique, standard LRA methods can
reconstruct filters using much lower basis and thus result in faster DNNs. The
effectiveness of our approach is comprehensively evaluated in ResNets, AlexNet,
and GoogLeNet. In AlexNet, for example, Force Regularization gains 2x speedup
on modern GPU without accuracy loss and 4.05x speedup on CPU by paying small
accuracy degradation. Moreover, Force Regularization better initializes the
low-rank DNNs such that the fine-tuning can converge faster toward higher
accuracy. The obtained lower-rank DNNs can be further sparsified, proving that
Force Regularization can be integrated with state-of-the-art sparsity-based
acceleration methods. Source code is available in
https://github.com/wenwei202/caffe
From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings
Authors: Andrei M. Butnaru, Radu Tudor Ionescu
Date: 2017-07-25 00:00:00
Tasks: Clustering, General Classification, Image to text, Text Categorization, text-classification, Text Classification, Word Embeddings

In this paper, we propose a novel approach for text classification based on
clustering word embeddings, inspired by the bag of visual words model, which is
widely used in computer vision. After each word in a collection of documents is
represented as word vector using a pre-trained word embeddings model, a k-means
algorithm is applied on the word vectors in order to obtain a fixed-size set of
clusters. The centroid of each cluster is interpreted as a super word embedding
that embodies all the semantically related word vectors in a certain region of
the embedding space. Every embedded word in the collection of documents is then
assigned to the nearest cluster centroid. In the end, each document is
represented as a bag of super word embeddings by computing the frequency of
each super word embedding in the respective document. We also diverge from the
idea of building a single vocabulary for the entire collection of documents,
and propose to build class-specific vocabularies for better performance. Using
this kind of representation, we report results on two text mining tasks, namely
text categorization by topic and polarity classification. On both tasks, our
model yields better performance than the standard bag of words.
Line-Circle: A Geometric Filter for Single Camera Edge-Based Object Detection
Authors: Seyed Amir Tafrishi, Vahid E. Kandjani
Date: 2017-07-25 00:00:00
Tasks: object-detection, Object Detection

This paper presents a state-of-the-art approach in object detection for being
applied in future SLAM problems. Although, many SLAM methods are proposed to
create suitable autonomy for mobile robots namely ground vehicles, they still
face overconfidence and large computations during entrance to immense spaces
with many landmarks. In particular, they suffer from impractical applications
via sole reliance on the limited sensors like camera. Proposed method claims
that unmanned ground vehicles without having huge amount of database for object
definition and highly advance prediction parameters can deal with incoming
objects during straight motion of camera in real-time. Line-Circle (LC) filter
tries to apply detection, tracking and learning to each defined experts to
obtain more information for judging scene without over-calculation. In this
filter, circle expert let us summarize edges in groups. The Interactive
feedback learning between each expert creates minimal error that fights against
overwhelming landmark signs in crowded scenes without mapping. Our experts
basically are dependent on trust factors' covariance with geometric definitions
to ignore, emerge and compare detected landmarks. The experiment for validating
the model is taken place utilizing a camera beside an IMU sensor for location
estimation.
ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing
Authors: Andrei M. Butnaru, Radu Tudor Ionescu, Florentina Hristea
Date: 2017-07-25 00:00:00
Proceeding: EACL 2017 4
Tasks: Common Sense Reasoning, Word Sense Disambiguation

In this paper, we present a novel unsupervised algorithm for word sense
disambiguation (WSD) at the document level. Our algorithm is inspired by a
widely-used approach in the field of genetics for whole genome sequencing,
known as the Shotgun sequencing technique. The proposed WSD algorithm is based
on three main steps. First, a brute-force WSD algorithm is applied to short
context windows (up to 10 words) selected from the document in order to
generate a short list of likely sense configurations for each window. In the
second step, these local sense configurations are assembled into longer
composite configurations based on suffix and prefix matching. The resulted
configurations are ranked by their length, and the sense of each word is chosen
based on a voting scheme that considers only the top k configurations in which
the word appears. We compare our algorithm with other state-of-the-art
unsupervised WSD algorithms and demonstrate better performance, sometimes by a
very large margin. We also show that our algorithm can yield better performance
than the Most Common Sense (MCS) baseline on one data set. Moreover, our
algorithm has a very small number of parameters, is robust to parameter tuning,
and, unlike other bio-inspired methods, it gives a deterministic solution (it
does not involve random choices).
Accelerating Approximate Bayesian Computation with Quantile Regression: Application to Cosmological Redshift Distributions
Authors: Tomasz Kacprzak, Jörg Herbel, Adam Amara, Alexandre Réfrégier
Date: 2017-07-24 00:00:00
Tasks: quantile regression, regression

Approximate Bayesian Computation (ABC) is a method to obtain a posterior
distribution without a likelihood function, using simulations and a set of
distance metrics. For that reason, it has recently been gaining popularity as
an analysis tool in cosmology and astrophysics. Its drawback, however, is a
slow convergence rate. We propose a novel method, which we call qABC, to
accelerate ABC with Quantile Regression. In this method, we create a model of
quantiles of distance measure as a function of input parameters. This model is
trained on a small number of simulations and estimates which regions of the
prior space are likely to be accepted into the posterior. Other regions are
then immediately rejected. This procedure is then repeated as more simulations
are available. We apply it to the practical problem of estimation of redshift
distribution of cosmological samples, using forward modelling developed in
previous work. The qABC method converges to nearly same posterior as the basic
ABC. It uses, however, only 20\% of the number of simulations compared to basic
ABC, achieving a fivefold gain in execution time for our problem. For other
problems the acceleration rate may vary; it depends on how close the prior is
to the final posterior. We discuss possible improvements and extensions to this
method.
A comparison of single-trial EEG classification and EEG-informed fMRI across three MR compatible EEG recording systems
Authors: Josef Faller, Linbi Hong, Jennifer Cummings, Paul Sajda
Date: 2017-07-25 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification

Simultaneously recorded electroencephalography (EEG) and functional magnetic
resonance imaging (fMRI) can be used to non-invasively measure the
spatiotemporal dynamics of the human brain. One challenge is dealing with the
artifacts that each modality introduces into the other when the two are
recorded concurrently, for example the ballistocardiogram (BCG). We conducted a
preliminary comparison of three different MR compatible EEG recording systems
and assessed their performance in terms of single-trial classification of the
EEG when simultaneously collecting fMRI. We found tradeoffs across all three
systems, for example varied ease of setup and improved classification accuracy
with reference electrodes (REF) but not for pulse artifact subtraction (PAS) or
reference layer adaptive filtering (RLAF).
SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound
Authors: Christian F. Baumgartner, Konstantinos Kamnitsas, Jacqueline Matthew, Tara P. Fletcher, Sandra Smith, Lisa M. Koch, Bernhard Kainz, Daniel Rueckert
Date: 2016-12-16 00:00:00
Tasks: Anatomy, Retrieval

Identifying and interpreting fetal standard scan planes during 2D ultrasound
mid-pregnancy examinations are highly complex tasks which require years of
training. Apart from guiding the probe to the correct location, it can be
equally difficult for a non-expert to identify relevant structures within the
image. Automatic image processing can provide tools to help experienced as well
as inexperienced operators with these tasks. In this paper, we propose a novel
method based on convolutional neural networks which can automatically detect 13
fetal standard views in freehand 2D ultrasound data as well as provide a
localisation of the fetal structures via a bounding box. An important
contribution is that the network learns to localise the target anatomy using
weak supervision based on image-level labels only. The network architecture is
designed to operate in real-time while providing optimal output for the
localisation task. We present results for real-time annotation, retrospective
frame retrieval from saved videos, and localisation on a very large and
challenging dataset consisting of images and video recordings of full clinical
anomaly screenings. We found that the proposed method achieved an average
F1-score of 0.798 in a realistic classification experiment modelling real-time
detection, and obtained a 90.09% accuracy for retrospective frame retrieval.
Moreover, an accuracy of 77.8% was achieved on the localisation task.
Challenges in Data-to-Document Generation
Authors: Sam Wiseman, Stuart M. Shieber, Alexander M. Rush
Date: 2017-07-25 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Data-to-Text Generation, Descriptive, Text Generation

Recent neural models have shown significant progress on the problem of
generating short descriptive texts conditioned on a small number of database
records. In this work, we suggest a slightly more difficult data-to-text
generation task, and investigate how effective current approaches are on this
task. In particular, we introduce a new, large-scale corpus of data records
paired with descriptive documents, propose a series of extractive evaluation
methods for analyzing performance, and obtain baseline results using current
neural generation methods. Experiments show that these models produce fluent
text, but fail to convincingly approximate human-generated documents. Moreover,
even templated baselines exceed the performance of these neural models on some
metrics, though copy- and reconstruction-based extensions lead to noticeable
improvements.
Cross-Domain Perceptual Reward Functions
Authors: Ashley D. Edwards, Srijan Sood, Charles L. Isbell Jr
Date: 2017-05-25 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In reinforcement learning, we often define goals by specifying rewards within
desirable states. One problem with this approach is that we typically need to
redefine the rewards each time the goal changes, which often requires some
understanding of the solution in the agents environment. When humans are
learning to complete tasks, we regularly utilize alternative sources that guide
our understanding of the problem. Such task representations allow one to
specify goals on their own terms, thus providing specifications that can be
appropriately interpreted across various environments. This motivates our own
work, in which we represent goals in environments that are different from the
agents. We introduce Cross-Domain Perceptual Reward (CDPR) functions, learned
rewards that represent the visual similarity between an agents state and a
cross-domain goal image. We report results for learning the CDPRs with a deep
neural network and using them to solve two tasks with deep reinforcement
learning.
Synthesising Sign Language from semantics, approaching "from the target and back"
Authors: Michael Filhol, Gilles Falquet
Date: 2017-07-25 00:00:00
Tasks: Language Modelling

We present a Sign Language modelling approach allowing to build grammars and
create linguistic input for Sign synthesis through avatars. We comment on the
type of grammar it allows to build, and observe a resemblance between the
resulting expressions and traditional semantic representations. Comparing the
ways in which the paradigms are designed, we name and contrast two essentially
different strategies for building higher-level linguistic input:
"source-and-forward" vs. "target-and-back". We conclude by favouring the
latter, acknowledging the power of being able to automatically generate output
from semantically relevant input straight into articulations of the target
language.
Automatic Liver Segmentation Using an Adversarial Image-to-Image Network
Authors: Dong Yang, Daguang Xu, S. Kevin Zhou, Bogdan Georgescu, Mingqing Chen, Sasa Grbic, Dimitris Metaxas, Dorin Comaniciu
Date: 2017-07-25 00:00:00
Tasks: Decoder, Liver Segmentation, Segmentation

Automatic liver segmentation in 3D medical images is essential in many
clinical applications, such as pathological diagnosis of hepatic diseases,
surgical planning, and postoperative assessment. However, it is still a very
challenging task due to the complex background, fuzzy boundary, and various
appearance of liver. In this paper, we propose an automatic and efficient
algorithm to segment liver from 3D CT volumes. A deep image-to-image network
(DI2IN) is first deployed to generate the liver segmentation, employing a
convolutional encoder-decoder architecture combined with multi-level feature
concatenation and deep supervision. Then an adversarial network is utilized
during training process to discriminate the output of DI2IN from ground truth,
which further boosts the performance of DI2IN. The proposed method is trained
on an annotated dataset of 1000 CT volumes with various different scanning
protocols (e.g., contrast and non-contrast, various resolution and position)
and large variations in populations (e.g., ages and pathology). Our approach
outperforms the state-of-the-art solutions in terms of segmentation accuracy
and computing efficiency.
Price and Profit Awareness in Recommender Systems
Authors: Dietmar Jannach, Gediminas Adomavicius
Date: 2017-07-25 00:00:00
Tasks: Recommendation Systems

Academic research in the field of recommender systems mainly focuses on the
problem of maximizing the users' utility by trying to identify the most
relevant items for each user. However, such items are not necessarily the ones
that maximize the utility of the service provider (e.g., an online retailer) in
terms of the business value, such as profit. One approach to increasing the
providers' utility is to incorporate purchase-oriented information, e.g., the
price, sales probabilities, and the resulting profit, into the recommendation
algorithms. In this paper we specifically focus on price- and profit-aware
recommender systems. We provide a brief overview of the relevant literature and
use numerical simulations to illustrate the potential business benefit of such
approaches.
Predicting Exploitation of Disclosed Software Vulnerabilities Using Open-source Data
Authors: Benjamin L. Bullough, Anna K. Yanchenko, Christopher L. Smith, Joseph R. Zipkin
Date: 2017-07-25 00:00:00

Each year, thousands of software vulnerabilities are discovered and reported
to the public. Unpatched known vulnerabilities are a significant security risk.
It is imperative that software vendors quickly provide patches once
vulnerabilities are known and users quickly install those patches as soon as
they are available. However, most vulnerabilities are never actually exploited.
Since writing, testing, and installing software patches can involve
considerable resources, it would be desirable to prioritize the remediation of
vulnerabilities that are likely to be exploited. Several published research
studies have reported moderate success in applying machine learning techniques
to the task of predicting whether a vulnerability will be exploited. These
approaches typically use features derived from vulnerability databases (such as
the summary text describing the vulnerability) or social media posts that
mention the vulnerability by name. However, these prior studies share multiple
methodological shortcomings that inflate predictive power of these approaches.
We replicate key portions of the prior work, compare their approaches, and show
how selection of training and test data critically affect the estimated
performance of predictive models. The results of this study point to important
methodological considerations that should be taken into account so that results
reflect real-world utility.
Data-Driven Synthesis of Smoke Flows with CNN-based Feature Descriptors
Authors: Mengyu Chu, Nils Thuerey
Date: 2017-05-03 00:00:00

We present a novel data-driven algorithm to synthesize high-resolution flow
simulations with reusable repositories of space-time flow data. In our work, we
employ a descriptor learning approach to encode the similarity between fluid
regions with differences in resolution and numerical viscosity. We use
convolutional neural networks to generate the descriptors from fluid data such
as smoke density and flow velocity. At the same time, we present a deformation
limiting patch advection method which allows us to robustly track deformable
fluid regions. With the help of this patch advection, we generate stable
space-time data sets from detailed fluids for our repositories. We can then use
our learned descriptors to quickly localize a suitable data set when running a
new simulation. This makes our approach very efficient, and resolution
independent. We will demonstrate with several examples that our method yields
volumes with very high effective resolutions, and non-dissipative small scale
details that naturally integrate into the motions of the underlying flow.