pwc-archive/papers-with-abstracts huggingface dataset:
Reinforcement Learning for Relation Classification from Noisy Data
Authors: Jun Feng, Minlie Huang, Li Zhao, Yang Yang, Xiaoyan Zhu
Date: 2018-08-24 00:00:00
Tasks: Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Relation, Sentence

Existing relation classification methods that rely on distant supervision
assume that a bag of sentences mentioning an entity pair are all describing a
relation for the entity pair. Such methods, performing classification at the
bag level, cannot identify the mapping between a relation and a sentence, and
largely suffers from the noisy labeling problem. In this paper, we propose a
novel model for relation classification at the sentence level from noisy data.
The model has two modules: an instance selector and a relation classifier. The
instance selector chooses high-quality sentences with reinforcement learning
and feeds the selected sentences into the relation classifier, and the relation
classifier makes sentence level prediction and provides rewards to the instance
selector. The two modules are trained jointly to optimize the instance
selection and relation classification processes. Experiment results show that
our model can deal with the noise of data effectively and obtains better
performance for relation classification at the sentence level.
Approximate Distribution Matching for Sequence-to-Sequence Learning
Authors: Wenhu Chen, Guanlin Li, Shujie Liu, Zhirui Zhang, Mu Li, Ming Zhou
Date: 2018-08-24 00:00:00
Tasks: Image Captioning, Machine Translation, Translation

Sequence-to-Sequence models were introduced to tackle many real-life problems
like machine translation, summarization, image captioning, etc. The standard
optimization algorithms are mainly based on example-to-example matching like
maximum likelihood estimation, which is known to suffer from data sparsity
problem. Here we present an alternate view to explain sequence-to-sequence
learning as a distribution matching problem, where each source or target
example is viewed to represent a local latent distribution in the source or
target domain. Then, we interpret sequence-to-sequence learning as learning a
transductive model to transform the source local latent distributions to match
their corresponding target distributions. In our framework, we approximate both
the source and target latent distributions with recurrent neural networks
(augmenter). During training, the parallel augmenters learn to better
approximate the local latent distributions, while the sequence prediction model
learns to minimize the KL-divergence of the transformed source distributions
and the approximated target distributions. This algorithm can alleviate the
data sparsity issues in sequence learning by locally augmenting more unseen
data pairs and increasing the model's robustness. Experiments conducted on
machine translation and image captioning consistently demonstrate the
superiority of our proposed algorithm over the other competing algorithms.
Features of word similarity
Authors: Arthur M. Jacobs, Annette Kinder
Date: 2018-08-24 00:00:00
Tasks: regression, Word Similarity

In this theoretical note we compare different types of computational models
of word similarity and association in their ability to predict a set of about
900 rating data. Using regression and predictive modeling tools (neural net,
decision tree) the performance of a total of 28 models using different
combinations of both surface and semantic word features is evaluated. The
results present evidence for the hypothesis that word similarity ratings are
based on more than only semantic relatedness. The limited cross-validated
performance of the models asks for the development of psychological process
models of the word similarity rating task.
Non-asymptotic bounds for percentiles of independent non-identical random variables
Authors: Dong Xia
Date: 2018-08-24 00:00:00

This note displays an interesting phenomenon for percentiles of independent
but non-identical random variables. Let $X_1,\cdots,X_n$ be independent random
variables obeying non-identical continuous distributions and $X^{(1)}\geq
\cdots\geq X^{(n)}$ be the corresponding order statistics. For any $p\in(0,1)$,
we investigate the $100(1-p)$%-th percentile $X^{(pn)}$ and prove
non-asymptotic bounds for $X^{(pn)}$. In particular, for a wide class of
distributions, we discover an intriguing connection between their median and
the harmonic mean of the associated standard deviations. For example, if
$X_k\sim\mathcal{N}(0,\sigma_k^2)$ for $k=1,\cdots,n$ and $p=\frac{1}{2}$, we
show that its median $\big|{\rm Med}\big(X_1,\cdots,X_n\big)\big|=
O_P\Big(n^{1/2}\cdot\big(\sum_{k=1}^n\sigma_k^{-1}\big)^{-1}\Big)$ as long as
$\{\sigma_k\}_{k=1}^n$ satisfy certain mild non-dispersion property.
Deep Feature Pyramid Reconfiguration for Object Detection
Authors: Tao Kong, Fuchun Sun, Wenbing Huang, Huaping Liu
Date: 2018-08-24 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object, object-detection, Object Detection

State-of-the-art object detectors usually learn multi-scale representations
to get better results by employing feature pyramids. However, the current
designs for feature pyramids are still inefficient to integrate the semantic
information over different scales. In this paper, we begin by investigating
current feature pyramids solutions, and then reformulate the feature pyramid
construction as the feature reconfiguration process. Finally, we propose a
novel reconfiguration architecture to combine low-level representations with
high-level semantic features in a highly-nonlinear yet efficient way. In
particular, our architecture which consists of global attention and local
reconfigurations, is able to gather task-oriented features across different
spatial locations and scales, globally and locally. Both the global attention
and local reconfiguration are lightweight, in-place, and end-to-end trainable.
Using this method in the basic SSD system, our models achieve consistent and
significant boosts compared with the original model and its other variations,
without losing real-time processing speed.
Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants
Authors: Lara J. Kanbar, Charles C. Onu, Wissam Shalish, Karen A. Brown, Guilherme M. Sant'Anna, Robert E. Kearney, Doina Precup
Date: 2018-08-24 00:00:00

Extremely preterm infants often require endotracheal intubation and
mechanical ventilation during the first days of life. Due to the detrimental
effects of prolonged invasive mechanical ventilation (IMV), clinicians aim to
extubate infants as soon as they deem them ready. Unfortunately, existing
strategies for prediction of extubation readiness vary across clinicians and
institutions, and lead to high reintubation rates. We present an approach using
Random Forest classifiers for the analysis of cardiorespiratory variability to
predict extubation readiness. We address the issue of data imbalance by
employing random undersampling of examples from the majority class before
training each Decision Tree in a bag. By incorporating clinical domain
knowledge, we further demonstrate that our classifier could have identified 71%
of infants who failed extubation, while maintaining a success detection rate of
78%.
Predicting Extubation Readiness in Extreme Preterm Infants based on Patterns of Breathing
Authors: Charles C. Onu, Lara J. Kanbar, Wissam Shalish, Karen A. Brown, Guilherme M. Sant'Anna, Robert E. Kearney, Doina Precup
Date: 2018-08-24 00:00:00
Tasks: Time Series, Time Series Analysis

Extremely preterm infants commonly require intubation and invasive mechanical
ventilation after birth. While the duration of mechanical ventilation should be
minimized in order to avoid complications, extubation failure is associated
with increases in morbidities and mortality. As part of a prospective
observational study aimed at developing an accurate predictor of extubation
readiness, Markov and semi-Markov chain models were applied to gain insight
into the respiratory patterns of these infants, with more robust time-series
modeling using semi-Markov models. This model revealed interesting similarities
and differences between newborns who succeeded extubation and those who failed.
The parameters of the model were further applied to predict extubation
readiness via generative (joint likelihood) and discriminative (support vector
machine) approaches. Results showed that up to 84\% of infants who failed
extubation could have been accurately identified prior to extubation.
A Semi-Markov Chain Approach to Modeling Respiratory Patterns Prior to Extubation in Preterm Infants
Authors: Charles C. Onu, Lara J. Kanbar, Wissam Shalish, Karen A. Brown, Guilherme M. Sant'Anna, Robert E. Kearney, Doina Precup
Date: 2018-08-24 00:00:00
Tasks: Management

After birth, extremely preterm infants often require specialized respiratory
management in the form of invasive mechanical ventilation (IMV). Protracted IMV
is associated with detrimental outcomes and morbidities. Premature extubation,
on the other hand, would necessitate reintubation which is risky, technically
challenging and could further lead to lung injury or disease. We present an
approach to modeling respiratory patterns of infants who succeeded extubation
and those who required reintubation which relies on Markov models. We compare
the use of traditional Markov chains to semi-Markov models which emphasize
cross-pattern transitions and timing information, and to multi-chain Markov
models which can concisely represent non-stationarity in respiratory behavior
over time. The models we developed expose specific, unique similarities as well
as vital differences between the two populations.
Analysis of Noise Contrastive Estimation from the Perspective of Asymptotic Variance
Authors: Masatoshi Uehara, Takeru Matsuda, Fumiyasu Komaki
Date: 2018-08-24 00:00:00

There are many models, often called unnormalized models, whose normalizing
constants are not calculated in closed form. Maximum likelihood estimation is
not directly applicable to unnormalized models. Score matching, contrastive
divergence method, pseudo-likelihood, Monte Carlo maximum likelihood, and noise
contrastive estimation (NCE) are popular methods for estimating parameters of
such models. In this paper, we focus on NCE. The estimator derived from NCE is
consistent and asymptotically normal because it is an M-estimator. NCE
characteristically uses an auxiliary distribution to calculate the normalizing
constant in the same spirit of the importance sampling. In addition, there are
several candidates as objective functions of NCE.
  We focus on how to reduce asymptotic variance. First, we propose a method for
reducing asymptotic variance by estimating the parameters of the auxiliary
distribution. Then, we determine the form of the objective functions, where the
asymptotic variance takes the smallest values in the original estimator class
and the proposed estimator classes. We further analyze the robustness of the
estimator.
Proximal Policy Optimization and its Dynamic Version for Sequence Generation
Authors: Yi-Lin Tuan, Jinzhi Zhang, Yujia Li, Hung-Yi Lee
Date: 2018-08-24 00:00:00
Tasks: Chatbot, Model Optimization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In sequence generation task, many works use policy gradient for model
optimization to tackle the intractable backpropagation issue when maximizing
the non-differentiable evaluation metrics or fooling the discriminator in
adversarial learning. In this paper, we replace policy gradient with proximal
policy optimization (PPO), which is a proved more efficient reinforcement
learning algorithm, and propose a dynamic approach for PPO (PPO-dynamic). We
demonstrate the efficacy of PPO and PPO-dynamic on conditional sequence
generation tasks including synthetic experiment and chit-chat chatbot. The
results show that PPO and PPO-dynamic can beat policy gradient by stability and
performance.
Ontology Reasoning with Deep Neural Networks
Authors: Patrick Hohenecker, Thomas Lukasiewicz
Date: 2018-08-24 00:00:00
Tasks: Logical Reasoning

The ability to conduct logical reasoning is a fundamental aspect of intelligent human behavior, and thus an important problem along the way to human-level artificial intelligence. Traditionally, logic-based symbolic methods from the field of knowledge representation and reasoning have been used to equip agents with capabilities that resemble human logical reasoning qualities. More recently, however, there has been an increasing interest in using machine learning rather than logic-based symbolic formalisms to tackle these tasks. In this paper, we employ state-of-the-art methods for training deep neural networks to devise a novel model that is able to learn how to effectively perform logical reasoning in the form of basic ontology reasoning. This is an important and at the same time very natural logical reasoning task, which is why the presented approach is applicable to a plethora of important real-world problems. We present the outcomes of several experiments, which show that our model is able to learn to perform highly accurate ontology reasoning on very large, diverse, and challenging benchmarks. Furthermore, it turned out that the suggested approach suffers much less from different obstacles that prohibit logic-based symbolic reasoning, and, at the same time, is surprisingly plausible from a biological point of view.
Left ventricle quantification through spatio-temporal CNNs
Authors: Alejandro Debus, Enzo Ferrante
Date: 2018-08-23 00:00:00

Cardiovascular diseases are among the leading causes of death globally.
Cardiac left ventricle (LV) quantification is known to be one of the most
important tasks for the identification and diagnosis of such pathologies. In
this paper, we propose a deep learning method that incorporates 3D
spatio-temporal convolutions to perform direct left ventricle quantification
from cardiac MR sequences. Instead of analysing slices independently, we
process stacks of temporally adjacent slices by means of 3D convolutional
kernels which fuse the spatio-temporal information, incorporating the temporal
dynamics of the heart to the learned model. We show that incorporating such
information by means of spatio-temporal convolutions into standard LV
quantification architectures improves the accuracy of the predictions when
compared with single-slice models, achieving competitive results for all
cardiac indices and significantly breaking the state of the art (Xue et al.,
2018, MedIA) for cardiac phase estimation.
Learning Human-Object Interactions by Graph Parsing Neural Networks
Authors: Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, Song-Chun Zhu
Date: 2018-08-23 00:00:00
Proceeding: ECCV 2018 9
Tasks: Human-Object Interaction Detection, Object

This paper addresses the task of detecting and recognizing human-object
interactions (HOI) in images and videos. We introduce the Graph Parsing Neural
Network (GPNN), a framework that incorporates structural knowledge while being
differentiable end-to-end. For a given scene, GPNN infers a parse graph that
includes i) the HOI graph structure represented by an adjacency matrix, and ii)
the node labels. Within a message passing inference framework, GPNN iteratively
computes the adjacency matrices and node labels. We extensively evaluate our
model on three HOI detection benchmarks on images and videos: HICO-DET, V-COCO,
and CAD-120 datasets. Our approach significantly outperforms state-of-art
methods, verifying that GPNN is scalable to large datasets and applies to
spatial-temporal settings. The code is available at
https://github.com/SiyuanQi/gpnn.
From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities
Authors: Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis, Anastasia Oikonomou, Habib Benali
Date: 2018-08-23 00:00:00

Recent advancements in signal processing and machine learning coupled with
developments of electronic medical record keeping in hospitals and the
availability of extensive set of medical images through internal/external
communication systems, have resulted in a recent surge of significant interest
in "Radiomics". Radiomics is an emerging and relatively new research field,
which refers to extracting semi-quantitative and/or quantitative features from
medical images with the goal of developing predictive and/or prognostic models,
and is expected to become a critical component for integration of image-derived
information for personalized treatment in the near future. The conventional
Radiomics workflow is typically based on extracting pre-designed features (also
referred to as hand-crafted or engineered features) from a segmented region of
interest. Nevertheless, recent advancements in deep learning have caused trends
towards deep learning-based Radiomics (also referred to as discovery
Radiomics). Considering the advantages of these two approaches, there are also
hybrid solutions developed to exploit the potentials of multiple data sources.
Considering the variety of approaches to Radiomics, further improvements
require a comprehensive and integrated sketch, which is the goal of this
article. This manuscript provides a unique interdisciplinary perspective on
Radiomics by discussing state-of-the-art signal processing solutions in the
context of Radiomics.
Cross-Modal Health State Estimation
Authors: Nitish Nag, Vaibhav Pandey, Preston J. Putzel, Hari Bhimaraju, Srikanth Krishnan, Ramesh C. Jain
Date: 2018-08-07 00:00:00
Tasks: Decision Making, Rhythm, State Estimation

Individuals create and consume more diverse data about themselves today than
any time in history. Sources of this data include wearable devices, images,
social media, geospatial information and more. A tremendous opportunity rests
within cross-modal data analysis that leverages existing domain knowledge
methods to understand and guide human health. Especially in chronic diseases,
current medical practice uses a combination of sparse hospital based biological
metrics (blood tests, expensive imaging, etc.) to understand the evolving
health status of an individual. Future health systems must integrate data
created at the individual level to better understand health status perpetually,
especially in a cybernetic framework. In this work we fuse multiple user
created and open source data streams along with established biomedical domain
knowledge to give two types of quantitative state estimates of cardiovascular
health. First, we use wearable devices to calculate cardiorespiratory fitness
(CRF), a known quantitative leading predictor of heart disease which is not
routinely collected in clinical settings. Second, we estimate inherent genetic
traits, living environmental risks, circadian rhythm, and biological metrics
from a diverse dataset. Our experimental results on 24 subjects demonstrate how
multi-modal data can provide personalized health insight. Understanding the
dynamic nature of health status will pave the way for better health based
recommendation engines, better clinical decision making and positive lifestyle
changes.
Maximal Jacobian-based Saliency Map Attack
Authors: Rey Wiyatno, Anqi Xu
Date: 2018-08-23 00:00:00
Tasks: Adversarial Attack, Classification, General Classification, image-classification, Image Classification

The Jacobian-based Saliency Map Attack is a family of adversarial attack
methods for fooling classification models, such as deep neural networks for
image classification tasks. By saturating a few pixels in a given image to
their maximum or minimum values, JSMA can cause the model to misclassify the
resulting adversarial image as a specified erroneous target class. We propose
two variants of JSMA, one which removes the requirement to specify a target
class, and another that additionally does not need to specify whether to only
increase or decrease pixel intensities. Our experiments highlight the
competitive speeds and qualities of these variants when applied to datasets of
hand-written digits and natural scenes.
Deconvolutional Networks for Point-Cloud Vehicle Detection and Tracking in Driving Scenarios
Authors: Victor Vaquero, Ivan del Pino, Francesc Moreno-Noguer, Joan Solà, Alberto Sanfeliu, Juan Andrade-Cetto
Date: 2018-08-23 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Multi-Object Tracking, Object Tracking, vehicle detection

Vehicle detection and tracking is a core ingredient for developing autonomous
driving applications in urban scenarios. Recent image-based Deep Learning (DL)
techniques are obtaining breakthrough results in these perceptive tasks.
However, DL research has not yet advanced much towards processing 3D point
clouds from lidar range-finders. These sensors are very common in autonomous
vehicles since, despite not providing as semantically rich information as
images, their performance is more robust under harsh weather conditions than
vision sensors. In this paper we present a full vehicle detection and tracking
system that works with 3D lidar information only. Our detection step uses a
Convolutional Neural Network (CNN) that receives as input a featured
representation of the 3D information provided by a Velodyne HDL-64 sensor and
returns a per-point classification of whether it belongs to a vehicle or not.
The classified point cloud is then geometrically processed to generate
observations for a multi-object tracking system implemented via a number of
Multi-Hypothesis Extended Kalman Filters (MH-EKF) that estimate the position
and velocity of the surrounding vehicles. The system is thoroughly evaluated on
the KITTI tracking dataset, and we show the performance boost provided by our
CNN-based vehicle detector over a standard geometric approach. Our lidar-based
approach uses about a 4% of the data needed for an image-based detector with
similarly competitive results.
Financial Aspect-Based Sentiment Analysis using Deep Representations
Authors: Steve Yang, Jason Rosenfeld, Jacques Makutonin
Date: 2018-08-23 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Domain Adaptation, General Classification, Sentiment Analysis, Transfer Learning

The topic of aspect-based sentiment analysis (ABSA) has been explored for a
variety of industries, but it still remains much unexplored in finance. The
recent release of data for an open challenge (FiQA) from the companion
proceedings of WWW '18 has provided valuable finance-specific annotations. FiQA
contains high quality labels, but it still lacks data quantity to apply
traditional ABSA deep learning architecture. In this paper, we employ
high-level semantic representations and methods of inductive transfer learning
for NLP. We experiment with extensions of recently developed domain adaptation
methods and target task fine-tuning that significantly improve performance on a
small dataset. Our results show an 8.7% improvement in the F1 score for
classification and an 11% improvement over the MSE for regression on current
state-of-the-art results.