pwc-archive/papers-with-abstracts huggingface dataset:
Political Footprints: Political Discourse Analysis using Pre-Trained Word Vectors
Authors: Christophe Bruchansky
Date: 2017-05-17 00:00:00

In this paper, we discuss how machine learning could be used to produce a
systematic and more objective political discourse analysis. Political
footprints are vector space models (VSMs) applied to political discourse. Each
of their vectors represents a word, and is produced by training the English
lexicon on large text corpora. This paper presents a simple implementation of
political footprints, some heuristics on how to use them, and their application
to four cases: the U.N. Kyoto Protocol and Paris Agreement, and two U.S.
presidential elections. The reader will be offered a number of reasons to
believe that political footprints produce meaningful results, along with some
suggestions on how to improve their implementation.
A fuzzy expert system for earthquake prediction, case study: the Zagros range
Authors: Arash Andalib, Mehdi Zare, Farid Atry
Date: 2016-10-13 00:00:00
Tasks: Earthquake prediction, Prediction

A methodology for the development of a fuzzy expert system (FES) with
application to earthquake prediction is presented. The idea is to reproduce the
performance of a human expert in earthquake prediction. To do this, at the
first step, rules provided by the human expert are used to generate a fuzzy
rule base. These rules are then fed into an inference engine to produce a fuzzy
inference system (FIS) and to infer the results. In this paper, we have used a
Sugeno type fuzzy inference system to build the FES. At the next step, the
adaptive network-based fuzzy inference system (ANFIS) is used to refine the FES
parameters and improve its performance. The proposed framework is then employed
to attain the performance of a human expert used to predict earthquakes in the
Zagros area based on the idea of coupled earthquakes. While the prediction
results are promising in parts of the testing set, the general performance
indicates that prediction methodology based on coupled earthquakes needs more
investigation and more complicated reasoning procedure to yield satisfactory
predictions.
Source-LDA: Enhancing probabilistic topic models using prior knowledge sources
Authors: Justin Wood, Patrick Tan, Wei Wang, Corey Arnold
Date: 2016-06-02 00:00:00
Tasks: Topic Models

A popular approach to topic modeling involves extracting co-occurring n-grams
of a corpus into semantic themes. The set of n-grams in a theme represents an
underlying topic, but most topic modeling approaches are not able to label
these sets of words with a single n-gram. Such labels are useful for topic
identification in summarization systems. This paper introduces a novel approach
to labeling a group of n-grams comprising an individual topic. The approach
taken is to complement the existing topic distributions over words with a known
distribution based on a predefined set of topics. This is done by integrating
existing labeled knowledge sources representing known potential topics into the
probabilistic topic model. These knowledge sources are translated into a
distribution and used to set the hyperparameters of the Dirichlet generated
distribution over words. In the inference these modified distributions guide
the convergence of the latent topics to conform with the complementary
distributions. This approach ensures that the topic inference process is
consistent with existing knowledge. The label assignment from the complementary
knowledge sources are then transferred to the latent topics of the corpus. The
results show both accurate label assignment to topics as well as improved topic
generation than those obtained using various labeling approaches based off
Latent Dirichlet allocation (LDA).
Identification and Off-Policy Learning of Multiple Objectives Using Adaptive Clustering
Authors: Thommen George Karimpanal, Erik Wilhelm
Date: 2017-05-17 00:00:00
Tasks: Clustering, Q-Learning

In this work, we present a methodology that enables an agent to make
efficient use of its exploratory actions by autonomously identifying possible
objectives in its environment and learning them in parallel. The identification
of objectives is achieved using an online and unsupervised adaptive clustering
algorithm. The identified objectives are learned (at least partially) in
parallel using Q-learning. Using a simulated agent and environment, it is shown
that the converged or partially converged value function weights resulting from
off-policy learning can be used to accumulate knowledge about multiple
objectives without any additional exploration. We claim that the proposed
approach could be useful in scenarios where the objectives are initially
unknown or in real world scenarios where exploration is typically a time and
energy intensive process. The implications and possible extensions of this work
are also briefly discussed.
Distributed Vector Representation Of Shopping Items, The Customer And Shopping Cart To Build A Three Fold Recommendation System
Authors: Bibek Behera, Manoj Joshi, Abhilash KK, Mohammad Ansari Ismail
Date: 2017-05-17 00:00:00

The main idea of this paper is to represent shopping items through vectors
because these vectors act as the base for building em- beddings for customers
and shopping carts. Also, these vectors are input to the mathematical models
that act as either a recommendation engine or help in targeting potential
customers. We have used exponential family embeddings as the tool to construct
two basic vectors - product embeddings and context vectors. Using the basic
vectors, we build combined embeddings, trip embeddings and customer embeddings.
Combined embeddings mix linguistic properties of product names with their
shopping patterns. The customer embeddings establish an understand- ing of the
buying pattern of customers in a group and help in building customer profile.
For example a customer profile can represent customers frequently buying
pet-food. Identifying such profiles can help us bring out offers and discounts.
Similarly, trip embeddings are used to build trip profiles. People happen to
buy similar set of products in a trip and hence their trip embeddings can be
used to predict the next product they would like to buy. This is a novel
technique and the first of its kind to make recommendation using product, trip
and customer embeddings.
Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
Authors: Muhao Chen, Yingtao Tian, Mohan Yang, Carlo Zaniolo
Date: 2016-11-12 00:00:00
Tasks: Entity Alignment, Knowledge Graph Embeddings, Knowledge Graphs, Translation

Many recent works have demonstrated the benefits of knowledge graph
embeddings in completing monolingual knowledge graphs. Inasmuch as related
knowledge bases are built in several different languages, achieving
cross-lingual knowledge alignment will help people in constructing a coherent
knowledge base, and assist machines in dealing with different expressions of
entity relationships across diverse human languages. Unfortunately, achieving
this highly desirable crosslingual alignment by human labor is very costly and
errorprone. Thus, we propose MTransE, a translation-based model for
multilingual knowledge graph embeddings, to provide a simple and automated
solution. By encoding entities and relations of each language in a separated
embedding space, MTransE provides transitions for each embedding vector to its
cross-lingual counterparts in other spaces, while preserving the
functionalities of monolingual embeddings. We deploy three different techniques
to represent cross-lingual transitions, namely axis calibration, translation
vectors, and linear transformations, and derive five variants for MTransE using
different loss functions. Our models can be trained on partially aligned
graphs, where just a small portion of triples are aligned with their
cross-lingual counterparts. The experiments on cross-lingual entity matching
and triple-wise alignment verification show promising results, with some
variants consistently outperforming others on different tasks. We also explore
how MTransE preserves the key properties of its monolingual counterpart TransE.
Bayer Demosaicking Using Optimized Mean Curvature over RGB channels
Authors: Rui Chen, Huizhu Jia, Xiange Wen, Xiaodong Xie
Date: 2017-05-17 00:00:00
Tasks: Demosaicking

Color artifacts of demosaicked images are often found at contours due to
interpolation across edges and cross-channel aliasing. To tackle this problem,
we propose a novel demosaicking method to reliably reconstruct color channels
of a Bayer image based on two different optimized mean-curvature (MC) models.
The missing pixel values in green (G) channel are first estimated by minimizing
a variational MC model. The curvatures of restored G-image surface are
approximated as a linear MC model which guides the initial reconstruction of
red (R) and blue (B) channels. Then a refinement process is performed to
interpolate accurate full-resolution R and B images. Experiments on benchmark
images have testified to the superiority of the proposed method in terms of
both the objective and subjective quality.
Supervised Machine Learning for Signals Having RRC Shaped Pulses
Authors: Mohammad Bari, Hussain Taher, Syed Saad Sherazi, Milos Doroslovacki
Date: 2017-05-17 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, regression

Classification performances of the supervised machine learning techniques
such as support vector machines, neural networks and logistic regression are
compared for modulation recognition purposes. The simple and robust features
are used to distinguish continuous-phase FSK from QAM-PSK signals. Signals
having root-raised-cosine shaped pulses are simulated in extreme noisy
conditions having joint impurities of block fading, lack of symbol and sampling
synchronization, carrier offset, and additive white Gaussian noise. The
features are based on sample mean and sample variance of the imaginary part of
the product of two consecutive complex signal values.
Transfer Learning for Named-Entity Recognition with Neural Networks
Authors: Ji Young Lee, Franck Dernoncourt, Peter Szolovits
Date: 2017-05-17 00:00:00
Proceeding: LREC 2018 5
Tasks: De-identification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Transfer Learning

Recent approaches based on artificial neural networks (ANNs) have shown
promising results for named-entity recognition (NER). In order to achieve high
performances, ANNs need to be trained on a large labeled dataset. However,
labels might be difficult to obtain for the dataset on which the user wants to
perform NER: label scarcity is particularly pronounced for patient note
de-identification, which is an instance of NER. In this work, we analyze to
what extent transfer learning may address this issue. In particular, we
demonstrate that transferring an ANN model trained on a large labeled dataset
to another dataset with a limited number of labels improves upon the
state-of-the-art results on two different datasets for patient note
de-identification.
A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications
Authors: Ankur Sinha, Pekka Malo, Kalyanmoy Deb
Date: 2017-05-17 00:00:00
Tasks: Bilevel Optimization, Evolutionary Algorithms

Bilevel optimization is defined as a mathematical program, where an optimization problem contains another optimization problem as a constraint. These problems have received significant attention from the mathematical programming community. Only limited work exists on bilevel problems using evolutionary computation techniques; however, recently there has been an increasing interest due to the proliferation of practical applications and the potential of evolutionary algorithms in tackling these problems. This paper provides a comprehensive review on bilevel optimization from the basic principles to solution strategies; both classical and evolutionary. A number of potential application problems are also discussed. To offer the readers insights on the prominent developments in the field of bilevel optimization, we have performed an automated text-analysis of an extended list of papers published on bilevel optimization to date. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area.
Utility of General and Specific Word Embeddings for Classifying Translational Stages of Research
Authors: Vincent Major, Alisa Surkis, Yindalon Aphinyanaphongs
Date: 2017-05-17 00:00:00
Tasks: Articles, Classification, General Classification, text-classification, Text Classification, Word Embeddings

Conventional text classification models make a bag-of-words assumption
reducing text into word occurrence counts per document. Recent algorithms such
as word2vec are capable of learning semantic meaning and similarity between
words in an entirely unsupervised manner using a contextual window and doing so
much faster than previous methods. Each word is projected into vector space
such that similar meaning words such as "strong" and "powerful" are projected
into the same general Euclidean space. Open questions about these embeddings
include their utility across classification tasks and the optimal properties
and source of documents to construct broadly functional embeddings. In this
work, we demonstrate the usefulness of pre-trained embeddings for
classification in our task and demonstrate that custom word embeddings, built
in the domain and for the tasks, can improve performance over word embeddings
learnt on more general data including news articles or Wikipedia.
Learning to Represent Haptic Feedback for Partially-Observable Tasks
Authors: Jaeyong Sung, J. Kenneth Salisbury, Ashutosh Saxena
Date: 2017-05-17 00:00:00
Tasks: Q-Learning

The sense of touch, being the earliest sensory system to develop in a human
body [1], plays a critical part of our daily interaction with the environment.
In order to successfully complete a task, many manipulation interactions
require incorporating haptic feedback. However, manually designing a feedback
mechanism can be extremely challenging. In this work, we consider manipulation
tasks that need to incorporate tactile sensor feedback in order to modify a
provided nominal plan. To incorporate partial observation, we present a new
framework that models the task as a partially observable Markov decision
process (POMDP) and learns an appropriate representation of haptic feedback
which can serve as the state for a POMDP model. The model, that is parametrized
by deep recurrent neural networks, utilizes variational Bayes methods to
optimize the approximate posterior. Finally, we build on deep Q-learning to be
able to select the optimal action in each state without access to a simulator.
We test our model on a PR2 robot for multiple tasks of turning a knob until it
clicks.
Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions
Authors: Kleomenis Katevas, Ilias Leontiadis, Martin Pielot, Joan Serrà
Date: 2017-05-17 00:00:00
Tasks: Deep Learning, Feature Engineering, General Classification, Time Series, Time Series Analysis

We present a practical approach for processing mobile sensor time series data
for continual deep learning predictions. The approach comprises data cleaning,
normalization, capping, time-based compression, and finally classification with
a recurrent neural network. We demonstrate the effectiveness of the approach in
a case study with 279 participants. On the basis of sparse sensor events, the
network continually predicts whether the participants would attend to a
notification within 10 minutes. Compared to a random baseline, the classifier
achieves a 40% performance increase (AUC of 0.702) on a withheld test set. This
approach allows to forgo resource-intensive, domain-specific, error-prone
feature engineering, which may drastically increase the applicability of
machine learning to mobile phone sensor data.
Statistical Query Lower Bounds for Robust Estimation of High-dimensional Gaussians and Gaussian Mixtures
Authors: Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
Date: 2016-11-10 00:00:00

We describe a general technique that yields the first {\em Statistical Query
lower bounds} for a range of fundamental high-dimensional learning problems
involving Gaussian distributions. Our main results are for the problems of (1)
learning Gaussian mixture models (GMMs), and (2) robust (agnostic) learning of
a single unknown Gaussian distribution. For each of these problems, we show a
{\em super-polynomial gap} between the (information-theoretic) sample
complexity and the computational complexity of {\em any} Statistical Query
algorithm for the problem. Our SQ lower bound for Problem (1) is qualitatively
matched by known learning algorithms for GMMs. Our lower bound for Problem (2)
implies that the accuracy of the robust learning algorithm
in~\cite{DiakonikolasKKLMS16} is essentially best possible among all
polynomial-time SQ algorithms.
  Our SQ lower bounds are attained via a unified moment-matching technique that
is useful in other contexts and may be of broader interest. Our technique
yields nearly-tight lower bounds for a number of related unsupervised
estimation problems. Specifically, for the problems of (3) robust covariance
estimation in spectral norm, and (4) robust sparse mean estimation, we
establish a quadratic {\em statistical--computational tradeoff} for SQ
algorithms, matching known upper bounds. Finally, our technique can be used to
obtain tight sample complexity lower bounds for high-dimensional {\em testing}
problems. Specifically, for the classical problem of robustly {\em testing} an
unknown mean (known covariance) Gaussian, our technique implies an
information-theoretic sample lower bound that scales {\em linearly} in the
dimension. Our sample lower bound matches the sample complexity of the
corresponding robust {\em learning} problem and separates the sample complexity
of robust testing from standard (non-robust) testing.
Deep Multimodal Embedding: Manipulating Novel Objects with Point-clouds, Language and Trajectories
Authors: Jaeyong Sung, Ian Lenz, Ashutosh Saxena
Date: 2015-09-25 00:00:00

A robot operating in a real-world environment needs to perform reasoning over
a variety of sensor modalities such as vision, language and motion
trajectories. However, it is extremely challenging to manually design features
relating such disparate modalities. In this work, we introduce an algorithm
that learns to embed point-cloud, natural language, and manipulation trajectory
data into a shared embedding space with a deep neural network. To learn
semantically meaningful spaces throughout our network, we use a loss-based
margin to bring embeddings of relevant pairs closer together while driving
less-relevant cases from different modalities further apart. We use this both
to pre-train its lower layers and fine-tune our final embedding space, leading
to a more robust representation. We test our algorithm on the task of
manipulating novel objects and appliances based on prior experience with other
objects. On a large dataset, we achieve significant improvements in both
accuracy and inference time over the previous state of the art. We also perform
end-to-end experiments on a PR2 robot utilizing our learned embedding space.
Value Directed Exploration in Multi-Armed Bandits with Structured Priors
Authors: Bence Cserna, Marek Petrik, Reazul Hasan Russel, Wheeler Ruml
Date: 2017-04-12 00:00:00
Tasks: Multi-Armed Bandits

Multi-armed bandits are a quintessential machine learning problem requiring
the balancing of exploration and exploitation. While there has been progress in
developing algorithms with strong theoretical guarantees, there has been less
focus on practical near-optimal finite-time performance. In this paper, we
propose an algorithm for Bayesian multi-armed bandits that utilizes
value-function-driven online planning techniques. Building on previous work on
UCB and Gittins index, we introduce linearly-separable value functions that
take both the expected return and the benefit of exploration into consideration
to perform n-step lookahead. The algorithm enjoys a sub-linear performance
guarantee and we present simulation results that confirm its strength in
problems with structured priors. The simplicity and generality of our approach
makes it a strong candidate for analyzing more complex multi-armed bandit
problems.
SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules
Authors: Esben Jannik Bjerrum
Date: 2017-03-21 00:00:00
Tasks: Data Augmentation

Simplified Molecular Input Line Entry System (SMILES) is a single line text
representation of a unique molecule. One molecule can however have multiple
SMILES strings, which is a reason that canonical SMILES have been defined,
which ensures a one to one correspondence between SMILES string and molecule.
Here the fact that multiple SMILES represent the same molecule is explored as a
technique for data augmentation of a molecular QSAR dataset modeled by a long
short term memory (LSTM) cell based neural network. The augmented dataset was
130 times bigger than the original. The network trained with the augmented
dataset shows better performance on a test set when compared to a model built
with only one canonical SMILES string per molecule. The correlation coefficient
R2 on the test set was improved from 0.56 to 0.66 when using SMILES
enumeration, and the root mean square error (RMS) likewise fell from 0.62 to
0.55. The technique also works in the prediction phase. By taking the average
per molecule of the predictions for the enumerated SMILES a further improvement
to a correlation coefficient of 0.68 and a RMS of 0.52 was found.
Robust Registration of Gaussian Mixtures for Colour Transfer
Authors: Mairéad Grogan, Rozenn Dahyot
Date: 2017-05-17 00:00:00

We present a flexible approach to colour transfer inspired by techniques
recently proposed for shape registration. Colour distributions of the palette
and target images are modelled with Gaussian Mixture Models (GMMs) that are
robustly registered to infer a non linear parametric transfer function. We show
experimentally that our approach compares well to current techniques both
quantitatively and qualitatively. Moreover, our technique is computationally
the fastest and can take efficient advantage of parallel processing
architectures for recolouring images and videos. Our transfer function is
parametric and hence can be stored in memory for later usage and also combined
with other computed transfer functions to create interesting visual effects.
Overall this paper provides a fast user friendly approach to recolouring of
image and video materials.
Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery
Authors: Menglong Ye, Edward Johns, Ankur Handa, Lin Zhang, Philip Pratt, Guang-Zhong Yang
Date: 2017-05-17 00:00:00
Tasks: Depth Estimation, Depth Prediction

Robotic surgery has become a powerful tool for performing minimally invasive
procedures, providing advantages in dexterity, precision, and 3D vision, over
traditional surgery. One popular robotic system is the da Vinci surgical
platform, which allows preoperative information to be incorporated into live
procedures using Augmented Reality (AR). Scene depth estimation is a
prerequisite for AR, as accurate registration requires 3D correspondences
between preoperative and intraoperative organ models. In the past decade, there
has been much progress on depth estimation for surgical scenes, such as using
monocular or binocular laparoscopes [1,2]. More recently, advances in deep
learning have enabled depth estimation via Convolutional Neural Networks (CNNs)
[3], but training requires a large image dataset with ground truth depths.
Inspired by [4], we propose a deep learning framework for surgical scene depth
estimation using self-supervision for scalable data acquisition. Our framework
consists of an autoencoder for depth prediction, and a differentiable spatial
transformer for training the autoencoder on stereo image pairs without ground
truth depths. Validation was conducted on stereo videos collected in robotic
partial nephrectomy.