pwc-archive/papers-with-abstracts huggingface dataset:
Feature-based reformulation of entities in triple pattern queries
Authors: Amar Viswanathan, Geeth de Mel, James A. Hendler
Date: 2018-07-04 00:00:00
Tasks: Knowledge Graphs, valid

Knowledge graphs encode uniquely identifiable entities to other entities or
literal values by means of relationships, thus enabling semantically rich
querying over the stored data. Typically, the semantics of such queries are
often crisp thereby resulting in crisp answers. Query log statistics show that
a majority of the queries issued to knowledge graphs are often entity centric
queries. When a user needs additional answers the state-of-the-art in assisting
users is to rewrite the original query resulting in a set of approximations.
Several strategies have been proposed in past to address this. They typically
move up the taxonomy to relax a specific element to a more generic element.
Entities don't have a taxonomy and they end up being generalized. To address
this issue, in this paper, we propose an entity centric reformulation strategy
that utilizes schema information and entity features present in the graph to
suggest rewrites. Once the features are identified, the entity in concern is
reformulated as a set of features. Since entities can have a large number of
features, we introduce strategies that select the top-k most relevant and
{informative ranked features and augment them to the original query to create a
valid reformulation. We then evaluate our approach by showing that our
reformulation strategy produces results that are more informative when compared
with state-of-the-art
Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning
Authors: Duc Minh Nguyen, Evaggelia Tsiligianni, Robert Calderbank, Nikos Deligiannis
Date: 2018-07-04 00:00:00
Tasks: Inductive Bias, Matrix Completion, Multi-Task Learning

Autoencoders are popular among neural-network-based matrix completion models
due to their ability to retrieve potential latent factors from the partially
observed matrices. Nevertheless, when training data is scarce their performance
is significantly degraded due to overfitting. In this paper, we mit- igate
overfitting with a data-dependent regularization technique that relies on the
principles of multi-task learning. Specifically, we propose an
autoencoder-based matrix completion model that performs prediction of the
unknown matrix values as a main task, and manifold learning as an auxiliary
task. The latter acts as an inductive bias, leading to solutions that
generalize better. The proposed model outperforms the existing
autoencoder-based models designed for matrix completion, achieving high
reconstruction accuracy in well-known datasets.
MITOS-RCNN: A Novel Approach to Mitotic Figure Detection in Breast Cancer Histopathology Images using Region Based Convolutional Neural Networks
Authors: Siddhant Rao
Date: 2018-07-04 00:00:00
Tasks: object-detection, Object Detection, Prognosis, Small Object Detection

Studies estimate that there will be 266,120 new cases of invasive breast
cancer and 40,920 breast cancer induced deaths in the year of 2018 alone.
Despite the pervasiveness of this affliction, the current process to obtain an
accurate breast cancer prognosis is tedious and time consuming, requiring a
trained pathologist to manually examine histopathological images in order to
identify the features that characterize various cancer severity levels. We
propose MITOS-RCNN: a novel region based convolutional neural network (RCNN)
geared for small object detection to accurately grade one of the three factors
that characterize tumor belligerence described by the Nottingham Grading
System: mitotic count. Other computational approaches to mitotic figure
counting and detection do not demonstrate ample recall or precision to be
clinically viable. Our models outperformed all previous participants in the
ICPR 2012 challenge, the AMIDA 2013 challenge and the MITOS-ATYPIA-14 challenge
along with recently published works. Our model achieved an F-measure score of
0.955, a 6.11% improvement in accuracy from the most accurate of the previously
proposed models.
Program Language Translation Using a Grammar-Driven Tree-to-Tree Model
Authors: Mehdi Drissi, Olivia Watkins, Aditya Khant, Vivaswat Ojha, Pedro Sandoval, Rakia Segev, Eric Weiner, Robert Keller
Date: 2018-07-04 00:00:00
Tasks: Decoder, Translation

The task of translating between programming languages differs from the
challenge of translating natural languages in that programming languages are
designed with a far more rigid set of structural and grammatical rules.
Previous work has used a tree-to-tree encoder/decoder model to take advantage
of the inherent tree structure of programs during translation. Neural decoders,
however, by default do not exploit known grammar rules of the target language.
In this paper, we describe a tree decoder that leverages knowledge of a
language's grammar rules to exclusively generate syntactically correct
programs. We find that this grammar-based tree-to-tree model outperforms the
state of the art tree-to-tree model in translating between two programming
languages on a previously used synthetic task.
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Authors: Stefan Falkner, Aaron Klein, Frank Hutter
Date: 2018-07-04 00:00:00
Proceeding: ICML 2018 7
Tasks: Bayesian Optimization, Deep Reinforcement Learning, Hyperparameter Optimization, Reinforcement Learning

Modern deep learning methods are very sensitive to many hyperparameters, and,
due to the long training times of state-of-the-art models, vanilla Bayesian
hyperparameter optimization is typically computationally infeasible. On the
other hand, bandit-based configuration evaluation approaches based on random
search lack guidance and do not converge to the best configurations as quickly.
Here, we propose to combine the benefits of both Bayesian optimization and
bandit-based methods, in order to achieve the best of both worlds: strong
anytime performance and fast convergence to optimal configurations. We propose
a new practical state-of-the-art hyperparameter optimization method, which
consistently outperforms both Bayesian optimization and Hyperband on a wide
range of problem types, including high-dimensional toy functions, support
vector machines, feed-forward neural networks, Bayesian neural networks, deep
reinforcement learning, and convolutional neural networks. Our method is robust
and versatile, while at the same time being conceptually simple and easy to
implement.
Direct Uncertainty Prediction for Medical Second Opinions
Authors: Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Robert Kleinberg, Sendhil Mullainathan, Jon Kleinberg
Date: 2018-07-04 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Prediction

The issue of disagreements amongst human experts is a ubiquitous one in both machine learning and medicine. In medicine, this often corresponds to doctor disagreements on a patient diagnosis. In this work, we show that machine learning models can be trained to give uncertainty scores to data instances that might result in high expert disagreements. In particular, they can identify patient cases that would benefit most from a medical second opinion. Our central methodological finding is that Direct Uncertainty Prediction (DUP), training a model to predict an uncertainty score directly from the raw patient features, works better than Uncertainty Via Classification, the two-step process of training a classifier and postprocessing the output distribution to give an uncertainty score. We show this both with a theoretical result, and on extensive evaluations on a large scale medical imaging application.
Seq2RDF: An end-to-end application for deriving Triples from Natural Language Text
Authors: Yue Liu, Tongtao Zhang, Zhicheng Liang, Heng Ji, Deborah L. McGuinness
Date: 2018-07-04 00:00:00
Tasks: Decoder, Knowledge Graph Embeddings, Translation

We present an end-to-end approach that takes unstructured textual input and
generates structured output compliant with a given vocabulary. Inspired by
recent successes in neural machine translation, we treat the triples within a
given knowledge graph as an independent graph language and propose an
encoder-decoder framework with an attention mechanism that leverages knowledge
graph embeddings. Our model learns the mapping from natural language text to
triple representation in the form of subject-predicate-object using the
selected knowledge graph vocabulary. Experiments on three different data sets
show that we achieve competitive F1-Measures over the baselines using our
simple yet effective approach. A demo video is included.
Learning Personalized Representation for Inverse Problems in Medical Imaging Using Deep Neural Network
Authors: Kuang Gong, Kyungsang Kim, Jianan Cui, Ning Guo, Ciprian Catana, Jinyi Qi, Quanzheng Li
Date: 2018-07-04 00:00:00
Tasks: Denoising, Image Denoising, Image Reconstruction, Representation Learning

Recently deep neural networks have been widely and successfully applied in
computer vision tasks and attracted growing interests in medical imaging. One
barrier for the application of deep neural networks to medical imaging is the
need of large amounts of prior training pairs, which is not always feasible in
clinical practice. In this work we propose a personalized representation
learning framework where no prior training pairs are needed, but only the
patient's own prior images. The representation is expressed using a deep neural
network with the patient's prior images as network input. We then applied this
novel image representation to inverse problems in medical imaging in which the
original inverse problem was formulated as a constraint optimization problem
and solved using the alternating direction method of multipliers (ADMM)
algorithm. Anatomically guided brain positron emission tomography (PET) image
reconstruction and image denoising were employed as examples to demonstrate the
effectiveness of the proposed framework. Quantification results based on
simulation and real datasets show that the proposed personalized representation
framework outperform other widely adopted methods.
Understanding and Accelerating Particle-Based Variational Inference
Authors: Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu, Lawrence Carin
Date: 2018-07-04 00:00:00
Tasks: Bayesian Inference, Variational Inference

Particle-based variational inference methods (ParVIs) have gained attention in the Bayesian inference literature, for their capacity to yield flexible and accurate approximations. We explore ParVIs from the perspective of Wasserstein gradient flows, and make both theoretical and practical contributions. We unify various finite-particle approximations that existing ParVIs use, and recognize that the approximation is essentially a compulsory smoothing treatment, in either of two equivalent forms. This novel understanding reveals the assumptions and relations of existing ParVIs, and also inspires new ParVIs. We propose an acceleration framework and a principled bandwidth-selection method for general ParVIs; these are based on the developed theory and leverage the geometry of the Wasserstein space. Experimental results show the improved convergence by the acceleration framework and enhanced sample accuracy by the bandwidth-selection method.
Global Transition-based Non-projective Dependency Parsing
Authors: Carlos Gómez-Rodríguez, Tianze Shi, Lillian Lee
Date: 2018-07-04 00:00:00
Proceeding: ACL 2018 7
Tasks: Chinese Dependency Parsing, Dependency Parsing

Shi, Huang, and Lee (2017) obtained state-of-the-art results for English and
Chinese dependency parsing by combining dynamic-programming implementations of
transition-based dependency parsers with a minimal set of bidirectional LSTM
features. However, their results were limited to projective parsing. In this
paper, we extend their approach to support non-projectivity by providing the
first practical implementation of the MH_4 algorithm, an $O(n^4)$ mildly
nonprojective dynamic-programming parser with very high coverage on
non-projective treebanks. To make MH_4 compatible with minimal transition-based
feature sets, we introduce a transition-based interpretation of it in which
parser items are mapped to sequences of transitions. We thus obtain the first
implementation of global decoding for non-projective transition-based parsing,
and demonstrate empirically that it is more effective than its projective
counterpart in parsing a number of highly non-projective languages
Proximal algorithms for large-scale statistical modeling and sensor/actuator selection
Authors: Armin Zare, Hesameddin Mohammadi, Neil K. Dhingra, Tryphon T. Georgiou, Mihailo R. Jovanović
Date: 2018-07-04 00:00:00

Several problems in modeling and control of stochastically-driven dynamical systems can be cast as regularized semi-definite programs. We examine two such representative problems and show that they can be formulated in a similar manner. The first, in statistical modeling, seeks to reconcile observed statistics by suitably and minimally perturbing prior dynamics. The second seeks to optimally select a subset of available sensors and actuators for control purposes. To address modeling and control of large-scale systems we develop a unified algorithmic framework using proximal methods. Our customized algorithms exploit problem structure and allow handling statistical modeling, as well as sensor and actuator selection, for substantially larger scales than what is amenable to current general-purpose solvers. We establish linear convergence of the proximal gradient algorithm, draw contrast between the proposed proximal algorithms and alternating direction method of multipliers, and provide examples that illustrate the merits and effectiveness of our framework.
Transfer with Model Features in Reinforcement Learning
Authors: Lucas Lehnert, Michael L. Littman
Date: 2018-07-04 00:00:00
Tasks: model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A key question in Reinforcement Learning is which representation an agent can
learn to efficiently reuse knowledge between different tasks. Recently the
Successor Representation was shown to have empirical benefits for transferring
knowledge between tasks with shared transition dynamics. This paper presents
Model Features: a feature representation that clusters behaviourally equivalent
states and that is equivalent to a Model-Reduction. Further, we present a
Successor Feature model which shows that learning Successor Features is
equivalent to learning a Model-Reduction. A novel optimization objective is
developed and we provide bounds showing that minimizing this objective results
in an increasingly improved approximation of a Model-Reduction. Further, we
provide transfer experiments on randomly generated MDPs which vary in their
transition and reward functions but approximately preserve behavioural
equivalence between states. These results demonstrate that Model Features are
suitable for transfer between tasks with varying transition and reward
functions.
LaneNet: Real-Time Lane Detection Networks for Autonomous Driving
Authors: Ze Wang, Weiqiang Ren, Qiang Qiu
Date: 2018-07-04 00:00:00
Tasks: Autonomous Driving, Edge Classification, Lane Detection

Lane detection is to detect lanes on the road and provide the accurate
location and shape of each lane. It severs as one of the key techniques to
enable modern assisted and autonomous driving systems. However, several unique
properties of lanes challenge the detection methods. The lack of distinctive
features makes lane detection algorithms tend to be confused by other objects
with similar local appearance. Moreover, the inconsistent number of lanes on a
road as well as diverse lane line patterns, e.g. solid, broken, single, double,
merging, and splitting lines further hamper the performance. In this paper, we
propose a deep neural network based method, named LaneNet, to break down the
lane detection into two stages: lane edge proposal and lane line localization.
Stage one uses a lane edge proposal network for pixel-wise lane edge
classification, and the lane line localization network in stage two then
detects lane lines based on lane edge proposals. Please note that the goal of
our LaneNet is built to detect lane line only, which introduces more
difficulties on suppressing the false detections on the similar lane marks on
the road like arrows and characters. Despite all the difficulties, our lane
detection is shown to be robust to both highway and urban road scenarios method
without relying on any assumptions on the lane number or the lane line
patterns. The high running speed and low computational cost endow our LaneNet
the capability of being deployed on vehicle-based systems. Experiments validate
that our LaneNet consistently delivers outstanding performances on real world
traffic scenarios.
A Formal Ontology-Based Classification of Lexemes and its Applications
Authors: Sreekavitha Parupalli, Navjyoti Singh
Date: 2018-07-04 00:00:00
Tasks: Classification, General Classification

The paper describes the enrichment of OntoSenseNet - a verb-centric lexical
resource for Indian Languages. A major contribution of this work is
preservation of an authentic Telugu dictionary by developing a computational
version of the same. It is important because native speakers can better
annotate the sense-types when both the word and its meaning are in Telugu.
Hence efforts are made to develop the aforementioned Telugu dictionary and
annotations are done manually. The manually annotated gold standard corpus
consists 8483 verbs, 253 adverbs and 1673 adjectives. Annotations are done by
native speakers according to defined annotation guidelines. In this paper, we
provide an overview of the annotation procedure and present the validation of
the developed resource through inter-annotator agreement. Additional words from
Telugu WordNet are added to our resource and are crowd-sourced for annotation.
The statistics are compared with the sense-annotated lexicon, our resource for
more insights.
BayesGrad: Explaining Predictions of Graph Convolutional Networks
Authors: Hirotaka Akita, Kosuke Nakago, Tomoki Komatsu, Yohei Sugawara, Shin-ichi Maeda, Yukino Baba, Hisashi Kashima
Date: 2018-07-04 00:00:00
Tasks: Property Prediction

Recent advances in graph convolutional networks have significantly improved
the performance of chemical predictions, raising a new research question: "how
do we explain the predictions of graph convolutional networks?" A possible
approach to answer this question is to visualize evidence substructures
responsible for the predictions. For chemical property prediction tasks, the
sample size of the training data is often small and/or a label imbalance
problem occurs, where a few samples belong to a single class and the majority
of samples belong to the other classes. This can lead to uncertainty related to
the learned parameters of the machine learning model. To address this
uncertainty, we propose BayesGrad, utilizing the Bayesian predictive
distribution, to define the importance of each node in an input graph, which is
computed efficiently using the dropout technique. We demonstrate that BayesGrad
successfully visualizes the substructures responsible for the label prediction
in the artificial experiment, even when the sample size is small. Furthermore,
we use a real dataset to evaluate the effectiveness of the visualization. The
basic idea of BayesGrad is not limited to graph-structured data and can be
applied to other data types.
Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks
Authors: Priyanka Gupta, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff
Date: 2018-07-04 00:00:00
Tasks: Mortality Prediction, Time Series, Time Series Analysis, Transfer Learning

Deep neural networks have shown promising results for various clinical
prediction tasks such as diagnosis, mortality prediction, predicting duration
of stay in hospital, etc. However, training deep networks -- such as those
based on Recurrent Neural Networks (RNNs) -- requires large labeled data, high
computational resources, and significant hyperparameter tuning effort. In this
work, we investigate as to what extent can transfer learning address these
issues when using deep RNNs to model multivariate clinical time series. We
consider transferring the knowledge captured in an RNN trained on several
source tasks simultaneously using a large labeled dataset to build the model
for a target task with limited labeled data. An RNN pre-trained on several
tasks provides generic features, which are then used to build simpler linear
models for new target tasks without training task-specific RNNs. For
evaluation, we train a deep RNN to identify several patient phenotypes on time
series from MIMIC-III database, and then use the features extracted using that
RNN to build classifiers for identifying previously unseen phenotypes, and also
for a seemingly unrelated task of in-hospital mortality. We demonstrate that
(i) models trained on features extracted using pre-trained RNN outperform or,
in the worst case, perform as well as task-specific RNNs; (ii) the models using
features from pre-trained models are more robust to the size of labeled data
than task-specific RNNs; and (iii) features extracted using pre-trained RNN are
generic enough and perform better than typical statistical hand-crafted
features.
A Convolutional Neural Network for Aspect Sentiment Classification
Authors: Yongping Xing, Chuangbai Xiao, Yifei Wu, Ziming Ding
Date: 2018-07-04 00:00:00
Tasks: Classification, General Classification, Sentence, Sentiment Analysis, Sentiment Classification

With the development of the Internet, natural language processing (NLP), in
which sentiment analysis is an important task, became vital in information
processing.Sentiment analysis includes aspect sentiment classification. Aspect
sentiment can provide complete and in-depth results with increased attention on
aspect-level. Different context words in a sentence influence the sentiment
polarity of a sentence variably, and polarity varies based on the different
aspects in a sentence. Take the sentence, 'I bought a new camera. The picture
quality is amazing but the battery life is too short.'as an example. If the
aspect is picture quality, then the expected sentiment polarity is 'positive',
if the battery life aspect is considered, then the sentiment polarity should be
'negative'; therefore, aspect is important to consider when we explore aspect
sentiment in the sentence. Recurrent neural network (RNN) is regarded as a good
model to deal with natural language processing, and RNNs has get good
performance on aspect sentiment classification including Target-Dependent LSTM
(TD-LSTM) ,Target-Connection LSTM (TC-LSTM) (Tang, 2015a, b), AE-LSTM, AT-LSTM,
AEAT-LSTM (Wang et al., 2016).There are also extensive literatures on sentiment
classification utilizing convolutional neural network, but there is little
literature on aspect sentiment classification using convolutional neural
network. In our paper, we develop attention-based input layers in which aspect
information is considered by input layer. We then incorporate attention-based
input layers into convolutional neural network (CNN) to introduce context words
information. In our experiment, incorporating aspect information into CNN
improves the latter's aspect sentiment classification performance without using
syntactic parser or external sentiment lexicons in a benchmark dataset from
Twitter but get better performance compared with other models.