pwc-archive/papers-with-abstracts huggingface dataset:
HeteroMed: Heterogeneous Information Network for Medical Diagnosis
Authors: Anahita Hosseini, Ting Chen, Wenjun Wu, Yizhou Sun, Majid Sarrafzadeh
Date: 2018-04-22 00:00:00
Tasks: Medical Diagnosis, Missing Values

With the recent availability of Electronic Health Records (EHR) and great
opportunities they offer for advancing medical informatics, there has been
growing interest in mining EHR for improving quality of care. Disease diagnosis
due to its sensitive nature, huge costs of error, and complexity has become an
increasingly important focus of research in past years. Existing studies model
EHR by capturing co-occurrence of clinical events to learn their latent
embeddings. However, relations among clinical events carry various semantics
and contribute differently to disease diagnosis which gives precedence to a
more advanced modeling of heterogeneous data types and relations in EHR data
than existing solutions. To address these issues, we represent how
high-dimensional EHR data and its rich relationships can be suitably translated
into HeteroMed, a heterogeneous information network for robust medical
diagnosis. Our modeling approach allows for straightforward handling of missing
values and heterogeneity of data. HeteroMed exploits metapaths to capture
higher level and semantically important relations contributing to disease
diagnosis. Furthermore, it employs a joint embedding framework to tailor
clinical event representations to the disease diagnosis goal. To the best of
our knowledge, this is the first study to use Heterogeneous Information Network
for modeling clinical data and disease diagnosis. Experimental results of our
study show superior performance of HeteroMed compared to prior methods in
prediction of exact diagnosis codes and general disease cohorts. Moreover,
HeteroMed outperforms baseline models in capturing similarities of clinical
events which are examined qualitatively through case studies.
Multi-Head Decoder for End-to-End Speech Recognition
Authors: Tomoki Hayashi, Shinji Watanabe, Tomoki Toda, Kazuya Takeda
Date: 2018-04-22 00:00:00
Tasks: Decoder, speech-recognition, Speech Recognition

This paper presents a new network architecture called multi-head decoder for
end-to-end speech recognition as an extension of a multi-head attention model.
In the multi-head attention model, multiple attentions are calculated, and
then, they are integrated into a single attention. On the other hand, instead
of the integration in the attention level, our proposed method uses multiple
decoders for each attention and integrates their outputs to generate a final
output. Furthermore, in order to make each head to capture the different
modalities, different attention functions are used for each head, leading to
the improvement of the recognition performance with an ensemble effect. To
evaluate the effectiveness of our proposed method, we conduct an experimental
evaluation using Corpus of Spontaneous Japanese. Experimental results
demonstrate that our proposed method outperforms the conventional methods such
as location-based and multi-head attention models, and that it can capture
different speech/linguistic contexts within the attention-based encoder-decoder
framework.
First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis
Authors: Julio C. S. Jacques Junior, Yağmur Güçlütürk, Marc Pérez, Umut Güçlü, Carlos Andujar, Xavier Baró, Hugo Jair Escalante, Isabelle Guyon, Marcel A. J. van Gerven, Rob Van Lier, Sergio Escalera
Date: 2018-04-21 00:00:00
Tasks: Personality Trait Recognition

Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.
Bridgeout: stochastic bridge regularization for deep neural networks
Authors: Najeeb Khan, Jawad Shah, Ian Stavness
Date: 2018-04-21 00:00:00

A major challenge in training deep neural networks is overfitting, i.e.
inferior performance on unseen test examples compared to performance on
training examples. To reduce overfitting, stochastic regularization methods
have shown superior performance compared to deterministic weight penalties on a
number of image recognition tasks. Stochastic methods such as Dropout and
Shakeout, in expectation, are equivalent to imposing a ridge and elastic-net
penalty on the model parameters, respectively. However, the choice of the norm
of weight penalty is problem dependent and is not restricted to $\{L_1,L_2\}$.
Therefore, in this paper we propose the Bridgeout stochastic regularization
technique and prove that it is equivalent to an $L_q$ penalty on the weights,
where the norm $q$ can be learned as a hyperparameter from data. Experimental
results show that Bridgeout results in sparse model weights, improved gradients
and superior classification performance compared to Dropout and Shakeout on
synthetic and real datasets.
Study of Residual Networks for Image Recognition
Authors: Mohammad Sadegh Ebrahimi, Hossein Karkeh Abadi
Date: 2018-04-21 00:00:00
Tasks: General Classification, image-classification, Image Classification

Deep neural networks demonstrate to have a high performance on image
classification tasks while being more difficult to train. Due to the complexity
and vanishing gradient problem, it normally takes a lot of time and more
computational power to train deeper neural networks. Deep residual networks
(ResNets) can make the training process faster and attain more accuracy
compared to their equivalent neural networks. ResNets achieve this improvement
by adding a simple skip connection parallel to the layers of convolutional
neural networks. In this project we first design a ResNet model that can
perform the image classification task on the Tiny ImageNet dataset with a high
accuracy, then we compare the performance of this ResNet model with its
equivalent Convolutional Network (ConvNet). Our findings illustrate that
ResNets are more prone to overfitting despite their higher accuracy. Several
methods to prevent overfitting such as adding dropout layers and stochastic
augmentation of the training dataset has been studied in this work.
Cross-lingual Semantic Parsing
Authors: Sheng Zhang, Kevin Duh, Benjamin Van Durme
Date: 2018-04-21 00:00:00
Tasks: Semantic Parsing

We introduce the task of cross-lingual semantic parsing: mapping content
provided in a source language into a meaning representation based on a target
language. We present: (1) a meaning representation designed to allow systems to
target varying levels of structural complexity (shallow to deep analysis), (2)
an evaluation metric to measure the similarity between system output and
reference meaning representations, (3) an end-to-end model with a novel copy
mechanism that supports intrasentential coreference, and (4) an evaluation
dataset where experiments show our model outperforms strong baselines by at
least 1.18 F1 score.
Learning from the experts: From expert systems to machine-learned diagnosis models
Authors: Murali Ravuri, Anitha Kannan, Geoffrey J. Tso, Xavier Amatriain
Date: 2018-04-21 00:00:00
Tasks: Diagnostic, Medical Diagnosis

Expert diagnostic support systems have been extensively studied. The
practical applications of these systems in real-world scenarios have been
somewhat limited due to well-understood shortcomings, such as lack of
extensibility. More recently, machine-learned models for medical diagnosis have
gained momentum, since they can learn and generalize patterns found in very
large datasets like electronic health records. These models also have
shortcomings - in particular, there is no easy way to incorporate prior
knowledge from existing literature or experts. In this paper, we present a
method to merge both approaches by using expert systems as generative models
that create simulated data on which models can be learned. We demonstrate that
such a learned model not only preserves the original properties of the expert
systems but also addresses some of their limitations. Furthermore, we show how
this approach can also be used as the starting point to combine expert
knowledge with knowledge extracted from other data sources, such as electronic
health records.
A Channel-based Exact Inference Algorithm for Bayesian Networks
Authors: Bart Jacobs
Date: 2018-04-21 00:00:00
Tasks: Bayesian Inference

This paper describes a new algorithm for exact Bayesian inference that is
based on a recently proposed compositional semantics of Bayesian networks in
terms of channels. The paper concentrates on the ideas behind this algorithm,
involving a linearisation (`stretching') of the Bayesian network, followed by a
combination of forward state transformation and backward predicate
transformation, while evidence is accumulated along the way. The performance of
a prototype implementation of the algorithm in Python is briefly compared to a
standard implementation (pgmpy): first results show competitive performance.
Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks
Authors: Alexey Shvets, Vladimir Iglovikov, Alexander Rakhlin, Alexandr A. Kalinin
Date: 2018-04-21 00:00:00

Accurate detection and localization for angiodysplasia lesions is an
important problem in early stage diagnostics of gastrointestinal bleeding and
anemia. Gold-standard for angiodysplasia detection and localization is
performed using wireless capsule endoscopy. This pill-like device is able to
produce thousand of high enough resolution images during one passage through
gastrointestinal tract. In this paper we present our winning solution for
MICCAI 2017 Endoscopic Vision SubChallenge: Angiodysplasia Detection and
Localization its further improvements over the state-of-the-art results using
several novel deep neural network architectures. It address the binary
segmentation problem, where every pixel in an image is labeled as an
angiodysplasia lesions or background. Then, we analyze connected component of
each predicted mask. Based on the analysis we developed a classifier that
predict angiodysplasia lesions (binary variable) and a detector for their
localization (center of a component). In this setting, our approach outperforms
other methods in every task subcategory for angiodysplasia detection and
localization thereby providing state-of-the-art results for these problems. The
source code for our solution is made publicly available at
https://github.com/ternaus/angiodysplasia-segmentatio
Brief Notes on Hard Takeoff, Value Alignment, and Coherent Extrapolated Volition
Authors: Gopal P. Sarma
Date: 2017-04-03 00:00:00

I make some basic observations about hard takeoff, value alignment, and
coherent extrapolated volition, concepts which have been central in analyses of
superintelligent AI systems.
AI Safety and Reproducibility: Establishing Robust Foundations for the Neuropsychology of Human Values
Authors: Gopal P. Sarma, Nick J. Hay, Adam Safron
Date: 2017-12-08 00:00:00

We propose the creation of a systematic effort to identify and replicate key
findings in neuropsychology and allied fields related to understanding human
values. Our aim is to ensure that research underpinning the value alignment
problem of artificial intelligence has been sufficiently validated to play a
role in the design of AI systems.
Determining the best classifier for predicting the value of a boolean field on a blood donor database using genetic algorithms
Authors: Ritabrata Maiti
Date: 2018-02-21 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Motivation: Thanks to digitization, we often have access to large databases,
consisting of various fields of information, ranging from numbers to texts and
even boolean values. Such databases lend themselves especially well to machine
learning, classification and big data analysis tasks. We are able to train
classifiers, using already existing data and use them for predicting the values
of a certain field, given that we have information regarding the other fields.
Most specifically, in this study, we look at the Electronic Health Records
(EHRs) that are compiled by hospitals. These EHRs are convenient means of
accessing data of individual patients, but there processing as a whole still
remains a task. However, EHRs that are composed of coherent, well-tabulated
structures lend themselves quite well to the application to machine language,
via the usage of classifiers. In this study, we look at a Blood Transfusion
Service Center Data Set (Data taken from the Blood Transfusion Service Center
in Hsin-Chu City in Taiwan). We used scikit-learn machine learning in python.
From Support Vector Machines(SVM), we use Support Vector Classification(SVC),
from the linear model we import Perceptron. We also used the
K.neighborsclassifier and the decision tree classifiers. Furthermore, we use
the TPOT library to find an optimized pipeline using genetic algorithms. Using
the above classifiers, we score each one of them using k fold cross-validation.
  Contact: ritabratamaiti@hiretrex.com GitHub Repository:
https://github.com/ritabratamaiti/Blooddonorprediction
ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking
Authors: Oliver Groth, Fabian B. Fuchs, Ingmar Posner, Andrea Vedaldi
Date: 2018-04-21 00:00:00
Proceeding: ECCV 2018 9
Tasks: Physical Intuition

Physical intuition is pivotal for intelligent agents to perform complex
tasks. In this paper we investigate the passive acquisition of an intuitive
understanding of physical principles as well as the active utilisation of this
intuition in the context of generalised object stacking. To this end, we
provide: a simulation-based dataset featuring 20,000 stack configurations
composed of a variety of elementary geometric primitives richly annotated
regarding semantics and structural stability. We train visual classifiers for
binary stability prediction on the ShapeStacks data and scrutinise their
learned physical intuition. Due to the richness of the training data our
approach also generalises favourably to real-world scenarios achieving
state-of-the-art stability prediction on a publicly available benchmark of
block towers. We then leverage the physical intuition learned by our model to
actively construct stable stacks and observe the emergence of an intuitive
notion of stackability - an inherent object affordance - induced by the active
stacking task. Our approach performs well even in challenging conditions where
it considerably exceeds the stack height observed during training or in cases
where initially unstable structures must be stabilised via counterbalancing.
Supervised and Unsupervised Transfer Learning for Question Answering
Authors: Yu-An Chung, Hung-Yi Lee, James Glass
Date: 2017-11-14 00:00:00
Proceeding: NAACL 2018 6
Tasks: Question Answering, speech-recognition, Speech Recognition, Transfer Learning

Although transfer learning has been shown to be successful for tasks like
object and speech recognition, its applicability to question answering (QA) has
yet to be well-studied. In this paper, we conduct extensive experiments to
investigate the transferability of knowledge learned from a source QA dataset
to a target dataset using two QA models. The performance of both models on a
TOEFL listening comprehension test (Tseng et al., 2016) and MCTest (Richardson
et al., 2013) is significantly improved via a simple transfer learning
technique from MovieQA (Tapaswi et al., 2016). In particular, one of the models
achieves the state-of-the-art on all target datasets; for the TOEFL listening
comprehension test, it outperforms the previous best model by 7%. Finally, we
show that transfer learning is helpful even in unsupervised scenarios when
correct answers for target QA dataset examples are not available.
Integrating Stance Detection and Fact Checking in a Unified Corpus
Authors: Ramy Baly, Mitra Mohtarami, James Glass, Lluis Marquez, Alessandro Moschitti, Preslav Nakov
Date: 2018-04-21 00:00:00
Proceeding: NAACL 2018 6
Tasks: Fact Checking, Retrieval, Stance Detection

A reasonable approach for fact checking a claim involves retrieving
potentially relevant documents from different sources (e.g., news websites,
social media, etc.), determining the stance of each document with respect to
the claim, and finally making a prediction about the claim's factuality by
aggregating the strength of the stances, while taking the reliability of the
source into account. Moreover, a fact checking system should be able to explain
its decision by providing relevant extracts (rationales) from the documents.
Yet, this setup is not directly supported by existing datasets, which treat
fact checking, document retrieval, source credibility, stance detection and
rationale extraction as independent tasks. In this paper, we support the
interdependencies between these tasks as annotations in the same corpus. We
implement this setup on an Arabic fact checking corpus, the first of its kind.
Multi-Modal Coreference Resolution with the Correlation between Space Structures
Authors: Qibin Zheng, Xingchun Diao, Jianjun Cao, Xiaolei Zhou, Yi Liu, Hongmei Li
Date: 2018-04-21 00:00:00
Tasks: coreference-resolution, Coreference Resolution

Multi-modal data is becoming more common in big data background. Finding the
semantically similar objects from different modality is one of the heart
problems of multi-modal learning. Most of the current methods try to learn the
inter-modal correlation with extrinsic supervised information, while intrinsic
structural information of each modality is neglected. The performance of these
methods heavily depends on the richness of training samples. However, obtaining
the multi-modal training samples is still a labor and cost intensive work. In
this paper, we bring a extrinsic correlation between the space structures of
each modalities in coreference resolution. With this correlation, a
semi-supervised learning model for multi-modal coreference resolution is
proposed. We firstly extract high-level features of images and text, then
compute the distances of each object from some reference points to build the
space structure of each modality. With a shared reference point set, the space
structures of each modality are correlated. We employ the correlation to build
a commonly shared space that the semantic distance between multi-modal objects
can be computed directly. The experiments on two multi-modal datasets show that
our model performs better than the existing methods with insufficient training
data.
Stability of the Stochastic Gradient Method for an Approximated Large Scale Kernel Machine
Authors: Aven Samareh, Mahshid Salemi Parizi
Date: 2018-04-21 00:00:00
Tasks: Binary Classification, General Classification

In this paper we measured the stability of stochastic gradient method (SGM)
for learning an approximated Fourier primal support vector machine. The
stability of an algorithm is considered by measuring the generalization error
in terms of the absolute difference between the test and the training error.
Our problem is to learn an approximated kernel function using random Fourier
features for a binary classification problem via online convex optimization
settings. For a convex, Lipschitz continuous and smooth loss function, given
reasonable number of iterations stochastic gradient method is stable. We showed
that with a high probability SGM generalizes well for an approximated kernel
under given assumptions.We empirically verified the theoretical findings for
different parameters using several data sets.
Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds
Authors: Sheng Zhang, Kevin Duh, Benjamin Van Durme
Date: 2018-04-21 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Entity Typing, General Classification, Sentence

Fine-grained entity typing is the task of assigning fine-grained semantic
types to entity mentions. We propose a neural architecture which learns a
distributional semantic representation that leverages a greater amount of
semantic context -- both document and sentence level information -- than prior
work. We find that additional context improves performance, with further
improvements gained by utilizing adaptive classification thresholds.
Experiments show that our approach without reliance on hand-crafted features
achieves the state-of-the-art results on three benchmark datasets.
Swarm Intelligence: Past, Present and Future
Authors: Xin-She Yang, Suash Deb, Yuxin Zhao, Simon Fong, Xing-Shi He
Date: 2018-04-21 00:00:00

Many optimization problems in science and engineering are challenging to
solve, and the current trend is to use swarm intelligence (SI) and SI-based
algorithms to tackle such challenging problems. Some significant developments
have been made in recent years, though there are still many open problems in
this area. This paper provides a short but timely analysis about SI-based
algorithms and their links with self-organization. Different characteristics
and properties are analyzed here from both mathematical and qualitative
perspectives. Future research directions are outlined and open questions are
also highlighted.
Generating Natural Language Adversarial Examples
Authors: Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, Kai-Wei Chang
Date: 2018-04-21 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Diversity, Natural Language Inference, Sentiment Analysis

Deep neural networks (DNNs) are vulnerable to adversarial examples,
perturbations to correctly classified examples which can cause the model to
misclassify. In the image domain, these perturbations are often virtually
indistinguishable to human perception, causing humans and state-of-the-art
models to disagree. However, in the natural language domain, small
perturbations are clearly perceptible, and the replacement of a single word can
drastically alter the semantics of the document. Given these challenges, we use
a black-box population-based optimization algorithm to generate semantically
and syntactically similar adversarial examples that fool well-trained sentiment
analysis and textual entailment models with success rates of 97% and 70%,
respectively. We additionally demonstrate that 92.3% of the successful
sentiment analysis adversarial examples are classified to their original label
by 20 human annotators, and that the examples are perceptibly quite similar.
Finally, we discuss an attempt to use adversarial training as a defense, but
fail to yield improvement, demonstrating the strength and diversity of our
adversarial examples. We hope our findings encourage researchers to pursue
improving the robustness of DNNs in the natural language domain.