pwc-archive/papers-with-abstracts huggingface dataset:
Improving Explainable Recommendations with Synthetic Reviews
Authors: Sixun Ouyang, Aonghus Lawlor, Felipe Costa, Peter Dolog
Date: 2018-07-18 00:00:00
Tasks: Language Model Evaluation, Language Modeling, Language Modelling, Recommendation Systems, Text Generation

An important task for a recommender system to provide interpretable
explanations for the user. This is important for the credibility of the system.
Current interpretable recommender systems tend to focus on certain features
known to be important to the user and offer their explanations in a structured
form. It is well known that user generated reviews and feedback from reviewers
have strong leverage over the users' decisions. On the other hand, recent text
generation works have been shown to generate text of similar quality to human
written text, and we aim to show that generated text can be successfully used
to explain recommendations.
  In this paper, we propose a framework consisting of popular review-oriented
generation models aiming to create personalised explanations for
recommendations. The interpretations are generated at both character and word
levels. We build a dataset containing reviewers' feedback from the Amazon books
review dataset. Our cross-domain experiments are designed to bridge from
natural language processing to the recommender system domain. Besides language
model evaluation methods, we employ DeepCoNN, a novel review-oriented
recommender system using a deep neural network, to evaluate the recommendation
performance of generated reviews by root mean square error (RMSE). We
demonstrate that the synthetic personalised reviews have better recommendation
performance than human written reviews. To our knowledge, this presents the
first machine-generated natural language explanations for rating prediction.
Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy
Authors: Firat Ozdemir, Zixuan Peng, Christine Tanner, Philipp Fuernstahl, Orcun Goksel
Date: 2018-07-18 00:00:00
Tasks: Active Learning, Medical Image Analysis, Segmentation

Segmentation is essential for medical image analysis tasks such as
intervention planning, therapy guidance, diagnosis, treatment decisions. Deep
learning is becoming increasingly prominent for segmentation, where the lack of
annotations, however, often becomes the main limitation. Due to privacy
concerns and ethical considerations, most medical datasets are created,
curated, and allow access only locally. Furthermore, current deep learning
methods are often suboptimal in translating anatomical knowledge between
different medical imaging modalities. Active learning can be used to select an
informed set of image samples to request for manual annotation, in order to
best utilize the limited annotation time of clinical experts for optimal
outcomes, which we focus on in this work. Our contributions herein are two
fold: (1) we enforce domain-representativeness of selected samples using a
proposed penalization scheme to maximize information at the network abstraction
layer, and (2) we propose a Borda-count based sample querying scheme for
selecting samples for segmentation. Comparative experiments with baseline
approaches show that the samples queried with our proposed method, where both
above contributions are combined, result in significantly improved segmentation
performance for this active learning task.
Method for motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MRI of the liver
Authors: Daiki Tamada, Marie-Luise Kromrey, Hiroshi Onishi, Utaroh Motosugi
Date: 2018-07-18 00:00:00
Tasks: Denoising

Purpose: To improve the quality of images obtained via dynamic
contrast-enhanced MRI (DCE-MRI) that include motion artifacts and blurring
using a deep learning approach. Methods: A multi-channel convolutional neural
network (MARC) based method is proposed for reducing the motion artifacts and
blurring caused by respiratory motion in images obtained via DCE-MRI of the
liver. The training datasets for the neural network included images with and
without respiration-induced motion artifacts or blurring, and the distortions
were generated by simulating the phase error in k-space. Patient studies were
conducted using a multi-phase T1-weighted spoiled gradient echo sequence for
the liver containing breath-hold failures during data acquisition. The trained
network was applied to the acquired images to analyze the filtering
performance, and the intensities and contrast ratios before and after denoising
were compared via Bland-Altman plots. Results: The proposed network was found
to significantly reduce the magnitude of the artifacts and blurring induced by
respiratory motion, and the contrast ratios of the images after processing via
the network were consistent with those of the unprocessed images. Conclusion: A
deep learning based method for removing motion artifacts in images obtained via
DCE-MRI in the liver was demonstrated and validated.
Fake news as we feel it: perception and conceptualization of the term "fake news" in the media
Authors: Evandro Cunha, Gabriel Magno, Josemar Caetano, Douglas Teixeira, Virgilio Almeida
Date: 2018-07-18 00:00:00
Tasks: Misinformation

In this article, we quantitatively analyze how the term "fake news" is being
shaped in news media in recent years. We study the perception and the
conceptualization of this term in the traditional media using eight years of
data collected from news outlets based in 20 countries. Our results not only
corroborate previous indications of a high increase in the usage of the
expression "fake news", but also show contextual changes around this expression
after the United States presidential election of 2016. Among other results, we
found changes in the related vocabulary, in the mentioned entities, in the
surrounding topics and in the contextual polarity around the term "fake news",
suggesting that this expression underwent a change in perception and
conceptualization after 2016. These outcomes expand the understandings on the
usage of the term "fake news", helping to comprehend and more accurately
characterize this relevant social phenomenon linked to misinformation and
manipulation.
Learning Hybrid Sparsity Prior for Image Restoration: Where Deep Learning Meets Sparse Coding
Authors: Fangfang Wu, Weisheng Dong, Guangming Shi, Xin Li
Date: 2018-07-18 00:00:00
Tasks: Image Restoration

State-of-the-art approaches toward image restoration can be classified into
model-based and learning-based. The former - best represented by sparse coding
techniques - strive to exploit intrinsic prior knowledge about the unknown
high-resolution images; while the latter - popularized by recently developed
deep learning techniques - leverage external image prior from some training
dataset. It is natural to explore their middle ground and pursue a hybrid image
prior capable of achieving the best in both worlds. In this paper, we propose a
systematic approach of achieving this goal called Structured Analysis Sparse
Coding (SASC). Specifically, a structured sparse prior is learned from
extrinsic training data via a deep convolutional neural network (in a similar
way to previous learning-based approaches); meantime another structured sparse
prior is internally estimated from the input observation image (similar to
previous model-based approaches). Two structured sparse priors will then be
combined to produce a hybrid prior incorporating the knowledge from both
domains. To manage the computational complexity, we have developed a novel
framework of implementing hybrid structured sparse coding processes by deep
convolutional neural networks. Experimental results show that the proposed
hybrid image restoration method performs comparably with and often better than
the current state-of-the-art techniques.
Backplay: "Man muss immer umkehren"
Authors: Cinjon Resnick, Roberta Raileanu, Sanyam Kapoor, Alexander Peysakhovich, Kyunghyun Cho, Joan Bruna
Date: 2018-07-18 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Model-free reinforcement learning (RL) requires a large number of trials to learn a good policy, especially in environments with sparse rewards. We explore a method to improve the sample efficiency when we have access to demonstrations. Our approach, Backplay, uses a single demonstration to construct a curriculum for a given task. Rather than starting each training episode in the environment's fixed initial state, we start the agent near the end of the demonstration and move the starting point backwards during the course of training until we reach the initial state. Our contributions are that we analytically characterize the types of environments where Backplay can improve training speed, demonstrate the effectiveness of Backplay both in large grid worlds and a complex four player zero-sum game (Pommerman), and show that Backplay compares favorably to other competitive methods known to improve sample efficiency. This includes reward shaping, behavioral cloning, and reverse curriculum generation.
RARD II: The 94 Million Related-Article Recommendation Dataset
Authors: Joeran Beel, Barry Smyth, Andrew Collins
Date: 2018-07-18 00:00:00
Tasks: Management, Meta-Learning, Recommendation Systems

The main contribution of this paper is to introduce and describe a new recommender-systems dataset (RARD II). It is based on data from Mr. DLib, a recommender-system as-a-service in the digital library and reference-management-software domain. As such, RARD II complements datasets from other domains such as books, movies, and music. The dataset encompasses 94m recommendations, delivered in the two years from September 2016 to September 2018. The dataset covers an item-space of 24m unique items. RARD II provides a range of rich recommendation data, beyond conventional ratings. For example, in addition to the usual (implicit) ratings matrices, RARD II includes the original recommendation logs, which provide a unique insight into many aspects of the algorithms that generated the recommendations. The logs enable researchers to conduct various analyses about a real-world recommender system. This includes the evaluation of meta-learning approaches for predicting algorithm performance. In this paper, we summarise the key features of this dataset release, describe how it was generated and discuss some of its unique features. Compared to its predecessor RARD, RARD II contains 64% more recommendations, 187% more features (algorithms, parameters, and statistics), 50% more clicks, 140% more documents, and one additional service partner (JabRef).
Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search
Authors: Arber Zela, Aaron Klein, Stefan Falkner, Frank Hutter
Date: 2018-07-18 00:00:00
Tasks: Bayesian Optimization, Neural Architecture Search

While existing work on neural architecture search (NAS) tunes hyperparameters
in a separate post-processing step, we demonstrate that architectural choices
and other hyperparameter settings interact in a way that can render this
separation suboptimal. Likewise, we demonstrate that the common practice of
using very few epochs during the main NAS and much larger numbers of epochs
during a post-processing step is inefficient due to little correlation in the
relative rankings for these two training regimes. To combat both of these
problems, we propose to use a recent combination of Bayesian optimization and
Hyperband for efficient joint neural architecture and hyperparameter search.
Melanoma Recognition with an Ensemble of Techniques for Segmentation and a Structural Analysis for Classification
Authors: Christoph Rasche
Date: 2018-07-18 00:00:00
Tasks: Classification, General Classification, Lesion Classification

An approach to lesion recognition is described that for lesion localization
uses an ensemble of segmentation techniques and for lesion classification an
exhaustive structural analysis. For localization, candidate regions are
obtained from global thresholding of the chromatic maps and from applying the
K-Means algorithm to the RGB image; the candidate regions are then integrated.
For classification, a relatively exhaustive structural analysis of contours and
regions is carried out.
Distinct patterns of syntactic agreement errors in recurrent networks and humans
Authors: Tal Linzen, Brian Leonard
Date: 2018-07-18 00:00:00
Tasks: Sentence

Determining the correct form of a verb in context requires an understanding
of the syntactic structure of the sentence. Recurrent neural networks have been
shown to perform this task with an error rate comparable to humans, despite the
fact that they are not designed with explicit syntactic representations. To
examine the extent to which the syntactic representations of these networks are
similar to those used by humans when processing sentences, we compare the
detailed pattern of errors that RNNs and humans make on this task. Despite
significant similarities (attraction errors, asymmetry between singular and
plural subjects), the error patterns differed in important ways. In particular,
in complex sentences with relative clauses error rates increased in RNNs but
decreased in humans. Furthermore, RNNs showed a cumulative effect of attractors
but humans did not. We conclude that at least in some respects the syntactic
representations acquired by RNNs are fundamentally different from those used by
humans.
An Information-theoretic Framework for the Lossy Compression of Link Streams
Authors: Robin Lamarche-Perrin
Date: 2018-07-18 00:00:00

Graph compression is a data analysis technique that consists in the
replacement of parts of a graph by more general structural patterns in order to
reduce its description length. It notably provides interesting exploration
tools for the study of real, large-scale, and complex graphs which cannot be
grasped at first glance. This article proposes a framework for the compression
of temporal graphs, that is for the compression of graphs that evolve with
time. This framework first builds on a simple and limited scheme, exploiting
structural equivalence for the lossless compression of static graphs, then
generalises it to the lossy compression of link streams, a recent formalism for
the study of temporal graphs. Such generalisation relies on the natural
extension of (bidimensional) relational data by the addition of a third
temporal dimension. Moreover, we introduce an information-theoretic measure to
quantify and to control the information that is lost during compression, as
well as an algebraic characterisation of the space of possible compression
patterns to enhance the expressiveness of the initial compression scheme. These
contributions lead to the definition of a combinatorial optimisation problem,
that is the Lossy Multistream Compression Problem, for which we provide an
exact algorithm.
Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling
Authors: Carlo Biffi, Ozan Oktay, Giacomo Tarroni, Wenjia Bai, Antonio de Marvao, Georgia Doumou, Martin Rajchl, Reem Bedair, Sanjay Prasad, Stuart Cook, Declan O'Regan, Daniel Rueckert
Date: 2018-07-18 00:00:00
Tasks: Diagnostic, General Classification

Alterations in the geometry and function of the heart define well-established
causes of cardiovascular disease. However, current approaches to the diagnosis
of cardiovascular diseases often rely on subjective human assessment as well as
manual analysis of medical images. Both factors limit the sensitivity in
quantifying complex structural and functional phenotypes. Deep learning
approaches have recently achieved success for tasks such as classification or
segmentation of medical images, but lack interpretability in the feature
extraction and decision processes, limiting their value in clinical diagnosis.
In this work, we propose a 3D convolutional generative model for automatic
classification of images from patients with cardiac diseases associated with
structural remodeling. The model leverages interpretable task-specific anatomic
patterns learned from 3D segmentations. It further allows to visualise and
quantify the learned pathology-specific remodeling patterns in the original
input space of the images. This approach yields high accuracy in the
categorization of healthy and hypertrophic cardiomyopathy subjects when tested
on unseen MR images from our own multi-centre dataset (100%) as well on the
ACDC MICCAI 2017 dataset (90%). We believe that the proposed deep learning
approach is a promising step towards the development of interpretable
classifiers for the medical imaging domain, which may help clinicians to
improve diagnostic accuracy and enhance patient risk-stratification.
News-based trading strategies
Authors: Stefan Feuerriegel, Helmut Prendinger
Date: 2018-07-18 00:00:00
Tasks: Decision Making, Reinforcement Learning, Reinforcement Learning (RL)

The marvel of markets lies in the fact that dispersed information is
instantaneously processed and used to adjust the price of goods, services and
assets. Financial markets are particularly efficient when it comes to
processing information; such information is typically embedded in textual news
that is then interpreted by investors. Quite recently, researchers have started
to automatically determine news sentiment in order to explain stock price
movements. Interestingly, this so-called news sentiment works fairly well in
explaining stock returns. In this paper, we design trading strategies that
utilize textual news in order to obtain profits on the basis of novel
information entering the market. We thus propose approaches for automated
decision-making based on supervised and reinforcement learning. Altogether, we
demonstrate how news-based data can be incorporated into an investment system.
Customer Sharing in Economic Networks with Costs
Authors: Bin Li, Dong Hao, Dengji Zhao, Tao Zhou
Date: 2018-07-18 00:00:00

In an economic market, sellers, infomediaries and customers constitute an
economic network. Each seller has her own customer group and the seller's
private customers are unobservable to other sellers. Therefore, a seller can
only sell commodities among her own customers unless other sellers or
infomediaries share her sale information to their customer groups. However, a
seller is not incentivized to share others' sale information by default, which
leads to inefficient resource allocation and limited revenue for the sale. To
tackle this problem, we develop a novel mechanism called customer sharing
mechanism (CSM) which incentivizes all sellers to share each other's sale
information to their private customer groups. Furthermore, CSM also
incentivizes all customers to truthfully participate in the sale. In the end,
CSM not only allocates the commodities efficiently but also optimizes the
seller's revenue.
Computed Tomography Image Enhancement using 3D Convolutional Neural Network
Authors: Meng Li, Shiwen Shen, Wen Gao, William Hsu, Jason Cong
Date: 2018-07-18 00:00:00
Tasks: Computed Tomography (CT), Image Enhancement, SSIM

Computed tomography (CT) is increasingly being used for cancer screening,
such as early detection of lung cancer. However, CT studies have varying pixel
spacing due to differences in acquisition parameters. Thick slice CTs have
lower resolution, hindering tasks such as nodule characterization during
computer-aided detection due to partial volume effect. In this study, we
propose a novel 3D enhancement convolutional neural network (3DECNN) to improve
the spatial resolution of CT studies that were acquired using lower
resolution/slice thicknesses to higher resolutions. Using a subset of the LIDC
dataset consisting of 20,672 CT slices from 100 scans, we simulated lower
resolution/thick section scans then attempted to reconstruct the original
images using our 3DECNN network. A significant improvement in PSNR (29.3087dB
vs. 28.8769dB, p-value < 2.2e-16) and SSIM (0.8529dB vs. 0.8449dB, p-value <
2.2e-16) compared to other state-of-art deep learning methods is observed.
Self-supervised Knowledge Distillation Using Singular Value Decomposition
Authors: Seung Hyun Lee, Dae Ha Kim, Byung Cheol Song
Date: 2018-07-18 00:00:00
Proceeding: ECCV 2018 9
Tasks: Knowledge Distillation, Transfer Learning

To solve deep neural network (DNN)'s huge training dataset and its high
computation issue, so-called teacher-student (T-S) DNN which transfers the
knowledge of T-DNN to S-DNN has been proposed. However, the existing T-S-DNN
has limited range of use, and the knowledge of T-DNN is insufficiently
transferred to S-DNN. To improve the quality of the transferred knowledge from
T-DNN, we propose a new knowledge distillation using singular value
decomposition (SVD). In addition, we define a knowledge transfer as a
self-supervised task and suggest a way to continuously receive information from
T-DNN. Simulation results show that a S-DNN with a computational cost of 1/5 of
the T-DNN can be up to 1.1\% better than the T-DNN in terms of classification
accuracy. Also assuming the same computational cost, our S-DNN outperforms the
S-DNN driven by the state-of-the-art distillation with a performance advantage
of 1.79\%. code is available on https://github.com/sseung0703/SSKD\_SVD.
Determining ellipses from low-resolution images with a comprehensive image formation model
Authors: Wojciech Chojnacki, Zygmunt L. Szpak
Date: 2018-07-18 00:00:00

When determining the parameters of a parametric planar shape based on a
single low-resolution image, common estimation paradigms lead to inaccurate
parameter estimates. The reason behind poor estimation results is that standard
estimation frameworks fail to model the image formation process at a
sufficiently detailed level of analysis. We propose a new method for estimating
the parameters of a planar elliptic shape based on a single photon-limited,
low-resolution image. Our technique incorporates the effects of several
elements - point-spread function, discretisation step, quantisation step, and
photon noise - into a single cohesive and manageable statistical model. While
we concentrate on the particular task of estimating the parameters of elliptic
shapes, our ideas and methods have a much broader scope and can be used to
address the problem of estimating the parameters of an arbitrary parametrically
representable planar shape. Comprehensive experimental results on simulated and
real imagery demonstrate that our approach yields parameter estimates with
unprecedented accuracy. Furthermore, our method supplies a parameter covariance
matrix as a measure of uncertainty for the estimated parameters, as well as a
planar confidence region as a means for visualising the parameter uncertainty.
The mathematical model developed in this paper may prove useful in a variety of
disciplines which operate with imagery at the limits of resolution.
Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card Game Scopone
Authors: Stefano Di Palma, Pier Luca Lanzi
Date: 2018-07-18 00:00:00

We present the design of a competitive artificial intelligence for Scopone, a
popular Italian card game. We compare rule-based players using the most
established strategies (one for beginners and two for advanced players) against
players using Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo
Tree Search (ISMCTS) with different reward functions and simulation strategies.
MCTS requires complete information about the game state and thus implements a
cheating player while ISMCTS can deal with incomplete information and thus
implements a fair player. Our results show that, as expected, the cheating MCTS
outperforms all the other strategies; ISMCTS is stronger than all the
rule-based players implementing well-known and most advanced strategies and it
also turns out to be a challenging opponent for human players.