pwc-archive/papers-with-abstracts huggingface dataset:
Does Hamiltonian Monte Carlo mix faster than a random walk on multimodal densities?
Authors: Oren Mangoubi, Natesh S. Pillai, Aaron Smith
Date: 2018-08-09 00:00:00

Hamiltonian Monte Carlo (HMC) is a very popular and generic collection of
Markov chain Monte Carlo (MCMC) algorithms. One explanation for the popularity
of HMC algorithms is their excellent performance as the dimension $d$ of the
target becomes large: under conditions that are satisfied for many common
statistical models, optimally-tuned HMC algorithms have a running time that
scales like $d^{0.25}$. In stark contrast, the running time of the usual
Random-Walk Metropolis (RWM) algorithm, optimally tuned, scales like $d$. This
superior scaling of the HMC algorithm with dimension is attributed to the fact
that it, unlike RWM, incorporates the gradient information in the proposal
distribution. In this paper, we investigate a different scaling question: does
HMC beat RWM for highly $\textit{multimodal}$ targets? We find that the answer
is often $\textit{no}$. We compute the spectral gaps for both the algorithms
for a specific class of multimodal target densities, and show that they are
identical. The key reason is that, within one mode, the gradient is effectively
ignorant about other modes, thus negating the advantage the HMC algorithm
enjoys in unimodal targets. We also give heuristic arguments suggesting that
the above observation may hold quite generally. Our main tool for answering
this question is a novel simple formula for the conductance of HMC using
Liouville's theorem. This result allows us to compute the spectral gap of HMC
algorithms, for both the classical HMC with isotropic momentum and the recent
Riemannian HMC, for multimodal targets.
Data-driven polynomial chaos expansion for machine learning regression
Authors: E. Torre, S. Marelli, P. Embrechts, B. Sudret
Date: 2018-08-09 00:00:00
Tasks: BIG-bench Machine Learning, regression, Uncertainty Quantification

We present a regression technique for data-driven problems based on
polynomial chaos expansion (PCE). PCE is a popular technique in the field of
uncertainty quantification (UQ), where it is typically used to replace a
runnable but expensive computational model subject to random inputs with an
inexpensive-to-evaluate polynomial function. The metamodel obtained enables a
reliable estimation of the statistics of the output, provided that a suitable
probabilistic model of the input is available. Machine learning (ML) regression
is a research field that focuses on providing purely data-driven input-output
maps, with the focus on pointwise prediction accuracy. We show that a PCE
metamodel purely trained on data can yield pointwise predictions whose accuracy
is comparable to that of other ML regression models, such as neural networks
and support vector machines. The comparisons are performed on benchmark
datasets available from the literature. The methodology also enables the
quantification of the output uncertainties, and is robust to noise.
Furthermore, it enjoys additional desirable properties, such as good
performance for small training sets and simplicity of construction, with only
little parameter tuning required.
Overcoming Missing and Incomplete Modalities with Generative Adversarial Networks for Building Footprint Segmentation
Authors: Benjamin Bischke, Patrick Helber, Florian König, Damian Borth, Andreas Dengel
Date: 2018-08-09 00:00:00
Tasks: Semantic Segmentation

The integration of information acquired with different modalities, spatial
resolution and spectral bands has shown to improve predictive accuracies. Data
fusion is therefore one of the key challenges in remote sensing. Most prior
work focusing on multi-modal fusion, assumes that modalities are always
available during inference. This assumption limits the applications of
multi-modal models since in practice the data collection process is likely to
generate data with missing, incomplete or corrupted modalities. In this paper,
we show that Generative Adversarial Networks can be effectively used to
overcome the problems that arise when modalities are missing or incomplete.
Focusing on semantic segmentation of building footprints with missing
modalities, our approach achieves an improvement of about 2% on the
Intersection over Union (IoU) against the same network that relies only on the
available modality.
Building a Kannada POS Tagger Using Machine Learning and Neural Network Models
Authors: Ketan Kumar Todi, Pruthwik Mishra, Dipti Misra Sharma
Date: 2018-08-09 00:00:00
Tasks: BIG-bench Machine Learning, Dependency Parsing, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), POS, POS Tagging, Sentiment Analysis, Word Embeddings

POS Tagging serves as a preliminary task for many NLP applications. Kannada
is a relatively poor Indian language with very limited number of quality NLP
tools available for use. An accurate and reliable POS Tagger is essential for
many NLP tasks like shallow parsing, dependency parsing, sentiment analysis,
named entity recognition. We present a statistical POS tagger for Kannada using
different machine learning and neural network models. Our Kannada POS tagger
outperforms the state-of-the-art Kannada POS tagger by 6%. Our contribution in
this paper is three folds - building a generic POS Tagger, comparing the
performances of different modeling techniques, exploring the use of character
and word embeddings together for Kannada POS Tagging.
A Survey on Sentiment and Emotion Analysis for Computational Literary Studies
Authors: Evgeny Kim, Roman Klinger
Date: 2018-08-09 00:00:00
Tasks: Emotion Recognition, Sentiment Analysis

Emotions are a crucial part of compelling narratives: literature tells us about people with goals, desires, passions, and intentions. Emotion analysis is part of the broader and larger field of sentiment analysis, and receives increasing attention in literary studies. In the past, the affective dimension of literature was mainly studied in the context of literary hermeneutics. However, with the emergence of the research field known as Digital Humanities (DH), some studies of emotions in a literary context have taken a computational turn. Given the fact that DH is still being formed as a field, this direction of research can be rendered relatively new. In this survey, we offer an overview of the existing body of research on emotion analysis as applied to literature. The research under review deals with a variety of topics including tracking dramatic changes of a plot development, network analysis of a literary text, and understanding the emotionality of texts, among other topics.
Finite Query Answering in Expressive Description Logics with Transitive Roles
Authors: Tomasz Gogacz, Yazmin Ibáñez-García, Filip Murlak
Date: 2018-08-09 00:00:00

We study the problem of finite ontology mediated query answering (FOMQA), the
variant of OMQA where the represented world is assumed to be finite, and thus
only finite models of the ontology are considered. We adopt the most typical
setting with unions of conjunctive queries and ontologies expressed in
description logics (DLs). The study of FOMQA is relevant in settings that are
not finitely controllable. This is the case not only for DLs without the finite
model property, but also for those allowing transitive role declarations. When
transitive roles are allowed, evaluating queries is challenging: FOMQA is
undecidable for SHOIF and only known to be decidable for the Horn fragment of
ALCIF. We show decidability of FOMQA for three proper fragments of SOIF: SOI,
SOF, and SIF. Our approach is to characterise models relevant for deciding
finite query entailment. Relying on a certain regularity of these models, we
develop automata-based decision procedures with optimal complexity bounds.
Classifier-Guided Visual Correction of Noisy Labels for Image Classification Tasks
Authors: Alex Bäuerle, Heiko Neumann, Timo Ropinski
Date: 2018-08-09 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, image-classification, Image Classification

Training data plays an essential role in modern applications of machine learning. However, gathering labeled training data is time-consuming. Therefore, labeling is often outsourced to less experienced users, or completely automated. This can introduce errors, which compromise valuable training data, and lead to suboptimal training results. We thus propose a novel approach that uses the power of pretrained classifiers to visually guide users to noisy labels, and let them interactively check error candidates, to iteratively improve the training data set. To systematically investigate training data, we propose a categorization of labeling errors into three different types, based on an analysis of potential pitfalls in label acquisition processes. For each of these types, we present approaches to detect, reason about, and resolve error candidates, as we propose measures and visual guidance techniques to support machine learning users. Our approach has been used to spot errors in well-known machine learning benchmark data sets, and we tested its usability during a user evaluation. While initially developed for images, the techniques presented in this paper are independent of the classification algorithm, and can also be extended to many other types of training data.
On feature selection and evaluation of transportation mode prediction strategies
Authors: Mohammad Etemad, Amilcar Soares Junior, Stan Matwin
Date: 2018-08-09 00:00:00
Tasks: Decision Making, feature selection, Information Retrieval, Management, Retrieval

Transportation modes prediction is a fundamental task for decision making in
smart cities and traffic management systems. Traffic policies designed based on
trajectory mining can save money and time for authorities and the public. It
may reduce the fuel consumption and commute time and moreover, may provide more
pleasant moments for residents and tourists. Since the number of features that
may be used to predict a user transportation mode can be substantial, finding a
subset of features that maximizes a performance measure is worth investigating.
In this work, we explore wrapper and information retrieval methods to find the
best subset of trajectory features. After finding the best classifier and the
best feature subset, our results were compared with two related papers that
applied deep learning methods and the results showed that our framework
achieved better performance. Furthermore, two types of cross-validation
approaches were investigated, and the performance results show that the random
cross-validation method provides optimistic results.
Image Inspired Poetry Generation in XiaoIce
Authors: Wen-Feng Cheng, Chao-Chung Wu, Ruihua Song, Jianlong Fu, Xing Xie, Jian-Yun Nie
Date: 2018-08-09 00:00:00

Vision is a common source of inspiration for poetry. The objects and the
sentimental imprints that one perceives from an image may lead to various
feelings depending on the reader. In this paper, we present a system of poetry
generation from images to mimic the process. Given an image, we first extract a
few keywords representing objects and sentiments perceived from the image.
These keywords are then expanded to related ones based on their associations in
human written poems. Finally, verses are generated gradually from the keywords
using recurrent neural networks trained on existing poems. Our approach is
evaluated by human assessors and compared to other generation baselines. The
results show that our method can generate poems that are more artistic than the
baseline methods. This is one of the few attempts to generate poetry from
images. By deploying our proposed approach, XiaoIce has already generated more
than 12 million poems for users since its release in July 2017. A book of its
poems has been published by Cheers Publishing, which claimed that the book is
the first-ever poetry collection written by an AI in human history.
Paired 3D Model Generation with Conditional Generative Adversarial Networks
Authors: Cihan Öngün, Alptekin Temizel
Date: 2018-08-09 00:00:00
Tasks: Image Generation

Generative Adversarial Networks (GANs) are shown to be successful at
generating new and realistic samples including 3D object models. Conditional
GAN, a variant of GANs, allows generating samples in given conditions. However,
objects generated for each condition are different and it does not allow
generation of the same object in different conditions. In this paper, we first
adapt conditional GAN, which is originally designed for 2D image generation, to
the problem of generating 3D models in different rotations. We then propose a
new approach to guide the network to generate the same 3D sample in different
and controllable rotation angles (sample pairs). Unlike previous studies, the
proposed method does not require modification of the standard conditional GAN
architecture and it can be integrated into the training step of any conditional
GAN. Experimental results and visual comparison of 3D models show that the
proposed method is successful at generating model pairs in different
conditions.
Gradient and Newton Boosting for Classification and Regression
Authors: Fabio Sigrist
Date: 2018-08-09 00:00:00
Tasks: Classification, General Classification, regression

Boosting algorithms are frequently used in applied data science and in research. To date, the distinction between boosting with either gradient descent or second-order Newton updates is often not made in both applied and methodological research, and it is thus implicitly assumed that the difference is irrelevant. The goal of this article is to clarify this situation. In particular, we present gradient and Newton boosting, as well as a hybrid variant of the two, in a unified framework. We compare these boosting algorithms with trees as base learners using various datasets and loss functions. Our experiments show that Newton boosting outperforms gradient and hybrid gradient-Newton boosting in terms of predictive accuracy on the majority of datasets. We also present evidence that the reason for this is not faster convergence of Newton boosting. In addition, we introduce a novel tuning parameter for tree-based Newton boosting which is interpretable and important for predictive accuracy.
Optimal conditions for connectedness of discretized sets
Authors: Boris Brimkov, Valentin E. Brimkov
Date: 2018-08-09 00:00:00

Constructing a discretization of a given set is a major problem in various
theoretical and applied disciplines. An offset discretization of a set $X$ is
obtained by taking the integer points inside a closed neighborhood of $X$ of a
certain radius. In this note we determine a minimum threshold for the offset
radius, beyond which the discretization of a disconnected set is always
connected. The results hold for a broad class of disconnected and unbounded
subsets of $R^n$, and generalize several previous results. Algorithmic aspects
and possible applications are briefly discussed.
Hunting for Tractable Languages for Judgment Aggregation
Authors: Ronald de Haan
Date: 2018-08-09 00:00:00
Tasks: Decision Making, Negation

Judgment aggregation is a general framework for collective decision making
that can be used to model many different settings. Due to its general nature,
the worst case complexity of essentially all relevant problems in this
framework is very high. However, these intractability results are mainly due to
the fact that the language to represent the aggregation domain is overly
expressive. We initiate an investigation of representation languages for
judgment aggregation that strike a balance between (1) being limited enough to
yield computational tractability results and (2) being expressive enough to
model relevant applications. In particular, we consider the languages of Krom
formulas, (definite) Horn formulas, and Boolean circuits in decomposable
negation normal form (DNNF). We illustrate the use of the positive complexity
results that we obtain for these languages with a concrete application: voting
on how to spend a budget (i.e., participatory budgeting).
Efficient Outlier Removal in Large Scale Global Structure-from-Motion
Authors: Fei Wen, Danping Zou, Rendong Ying, Peilin Liu
Date: 2018-08-09 00:00:00
Tasks: Dimensionality Reduction

This work addresses the outlier removal problem in large-scale global
structure-from-motion. In such applications, global outlier removal is very
useful to mitigate the deterioration caused by mismatches in the feature point
matching step. Unlike existing outlier removal methods, we exploit the
structure in multiview geometry problems to propose a dimension reduced
formulation, based on which two methods have been developed. The first method
considers a convex relaxed $\ell_1$ minimization and is solved by a single
linear programming (LP), whilst the second one approximately solves the ideal
$\ell_0$ minimization by an iteratively reweighted method. The dimension
reduction results in a significant speedup of the new algorithms. Further, the
iteratively reweighted method can significantly reduce the possibility of
removing true inliers. Realistic multiview reconstruction experiments
demonstrated that, compared with state-of-the-art algorithms, the new
algorithms are much more efficient and meanwhile can give improved solution.
Matlab code for reproducing the results is available at
\textit{https://github.com/FWen/OUTLR.git}.
Policy Optimization as Wasserstein Gradient Flows
Authors: Ruiyi Zhang, Changyou Chen, Chunyuan Li, Lawrence Carin
Date: 2018-08-09 00:00:00
Proceeding: ICML 2018 7
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Policy optimization is a core component of reinforcement learning (RL), and
most existing RL methods directly optimize parameters of a policy based on
maximizing the expected total reward, or its surrogate. Though often achieving
encouraging empirical success, its underlying mathematical principle on {\em
policy-distribution} optimization is unclear. We place policy optimization into
the space of probability measures, and interpret it as Wasserstein gradient
flows. On the probability-measure space, under specified circumstances, policy
optimization becomes a convex problem in terms of distribution optimization. To
make optimization feasible, we develop efficient algorithms by numerically
solving the corresponding discrete gradient flows. Our technique is applicable
to several RL settings, and is related to many state-of-the-art
policy-optimization algorithms. Empirical results verify the effectiveness of
our framework, often obtaining better performance compared to related
algorithms.
Arithmetic Word Problem Solver using Frame Identification
Authors: Pruthwik Mishra, Litton J Kurisinkel, Dipti Misra Sharma
Date: 2018-08-09 00:00:00
Tasks: Sentence

Automatic Word problem solving has always posed a great challenge for the NLP
community. Usually a word problem is a narrative comprising of a few sentences
and a question is asked about a quantity referred in the sentences. Solving
word problem involves reasoning across sentences, identification of operations,
their order, relevant quantities and discarding irrelevant quantities. In this
paper, we present a novel approach for automatic arithmetic word problem
solving. Our approach starts with frame identification. Each frame can either
be classified as a state or an action frame. The frame identification is
dependent on the verb in a sentence. Every frame is unique and is identified by
its slots. The slots are filled using dependency parsed output of a sentence.
The slots are entity holder, entity, quantity of the entity, recipient,
additional information like place, time. The slots and frames helps to identify
the type of question asked and the entity referred. Action frames act on state
frame(s) which causes a change in quantities of the state frames. The frames
are then used to build a graph where any change in quantities can be propagated
to the neighboring nodes. Most of the current solvers can only answer questions
related to the quantity, while our system can answer different kinds of
questions like `who', `what' other than the quantity related questions `how
many'.
  There are three major contributions of this paper. 1. Frame Annotated Corpus
(with a frame annotation tool) 2. Frame Identification Module 3. A new easily
understandable Framework for word problem solving
Sentimental Content Analysis and Knowledge Extraction from News Articles
Authors: Mohammad Kamel, Neda Keyvani, Hadi Sadoghi Yazdi
Date: 2018-08-09 00:00:00
Tasks: Articles, Sentiment Analysis

In web era, since technology has revolutionized mankind life, plenty of data
and information are published on the Internet each day. For instance, news
agencies publish news on their websites all over the world. These raw data
could be an important resource for knowledge extraction. These shared data
contain emotions (i.e., positive, neutral or negative) toward various topics;
therefore, sentimental content extraction could be a beneficial task in many
aspects. Extracting the sentiment of news illustrates highly valuable
information about the events over a period of time, the viewpoint of a media or
news agency to these events. In this paper an attempt is made to propose an
approach for news analysis and extracting useful knowledge from them. Firstly,
we attempt to extract a noise robust sentiment of news documents; therefore,
the news associated to six countries: United State, United Kingdom, Germany,
Canada, France and Australia in 5 different news categories: Politics, Sports,
Business, Entertainment and Technology are downloaded. In this paper we compare
the condition of different countries in each 5 news topics based on the
extracted sentiments and emotional contents in news documents. Moreover, we
propose an approach to reduce the bulky news data to extract the hottest topics
and news titles as a knowledge. Eventually, we generate a word model to map
each word to a fixed-size vector by Word2Vec in order to understand the
relations between words in our collected news database.
Radon Inversion via Deep Learning
Authors: Ji He, Jianhua Ma
Date: 2018-08-09 00:00:00
Tasks: Deep Learning, Image Reconstruction

Radon transform is widely used in physical and life sciences and one of its
major applications is the X-ray computed tomography (X-ray CT), which is
significant in modern health examination. The Radon inversion or image
reconstruction is challenging due to the potentially defective radon
projections. Conventionally, the reconstruction process contains several ad hoc
stages to approximate the corresponding Radon inversion. Each of the stages is
highly dependent on the results of the previous stage. In this paper, we
propose a novel unified framework for Radon inversion via deep learning (DL).
The Radon inversion can be approximated by the proposed framework with an
end-to-end fashion instead of processing step-by-step with multiple stages. For
simplicity, the proposed framework is short as iRadonMap (inverse Radon
transform approximation). Specifically, we implement the iRadonMap as an
appropriative neural network, of which the architecture can be divided into two
segments. In the first segment, a learnable fully-connected filtering layer is
used to filter the radon projections along the view-angle direction, which is
followed by a learnable sinusoidal back-projection layer to transfer the
filtered radon projections into an image. The second segment is a common neural
network architecture to further improve the reconstruction performance in the
image domain. The iRadonMap is overall optimized by training a large number of
generic images from ImageNet database. To evaluate the performance of the
iRadonMap, clinical patient data is used. Qualitative results show promising
reconstruction performance of the iRadonMap.