pwc-archive/papers-with-abstracts huggingface dataset:
A bootstrap test to detect prominent Granger-causalities across frequencies
Authors: Matteo Farné, Angela Montanari
Date: 2018-03-01 00:00:00
Tasks: Time Series, Time Series Analysis

Granger-causality in the frequency domain is an emerging tool to analyze the
causal relationship between two time series. We propose a bootstrap test on
unconditional and conditional Granger-causality spectra, as well as on their
difference, to catch particularly prominent causality cycles in relative terms.
In particular, we consider a stochastic process derived applying independently
the stationary bootstrap to the original series. Our null hypothesis is that
each causality or causality difference is equal to the median across
frequencies computed on that process. In this way, we are able to disambiguate
causalities which depart significantly from the median one obtained ignoring
the causality structure. Our test shows power one as the process tends to
non-stationarity, thus being more conservative than parametric alternatives. As
an example, we infer about the relationship between money stock and GDP in the
Euro Area via our approach, considering inflation, unemployment and interest
rates as conditioning variables. We point out that during the period 1999-2017
the money stock aggregate M1 had a significant impact on economic output at all
frequencies, while the opposite relationship is significant only at high
frequencies.
Mainumby: un Ayudante para la Traducción Castellano-Guaraní
Authors: Michael Gasser
Date: 2018-10-19 00:00:00
Tasks: Translation

A wide range of applications play an important role in the daily work of the
modern human translator. However, the computational tools designed to aid in
the process of translation only benefit translation from or to a small minority
of the 7,000 languages of the world, those that we may call "privileged
languages". As for those translators who work with the remaining languages, the
marginalized languages in the digital world, they cannot benefit from the tools
that are speeding up the production of translation in the privileged languages.
We may ask whether it is possible to bridge the gap between what is available
for these languages and for the marginalized ones. This paper proposes a
framework for computer-assisted translation into marginalized languages and its
implementation in a web application for Spanish-Guarani translation. The
proposed system is based on a new theory for phrase-level translation in
contexts where adequate bilingual corpora are not available: Translation by
Generalized Segments (referred to as Minimal Dependency Translation in previous
work).
Detecting cities in aerial night-time images by learning structural invariants using single reference augmentation
Authors: Philipp Sadler
Date: 2018-10-19 00:00:00
Tasks: Data Augmentation

This paper examines, if it is possible to learn structural invariants of city
images by using only a single reference picture when producing transformations
along the variants in the dataset. Previous work explored the problem of
learning from only a few examples and showed that data augmentation techniques
benefit performance and generalization for machine learning approaches. First a
principal component analysis in conjunction with a Fourier transform is trained
on a single reference augmentation training dataset using the city images.
Secondly a convolutional neural network is trained on a similar dataset with
more samples. The findings are that the convolutional neural network is capable
of finding images of the same category whereas the applied principal component
analysis in conjunction with a Fourier transform failed to solve this task.
A Modern Take on the Bias-Variance Tradeoff in Neural Networks
Authors: Brady Neal, Sarthak Mittal, Aristide Baratin, Vinayak Tantia, Matthew Scicluna, Simon Lacoste-Julien, Ioannis Mitliagkas
Date: 2018-10-19 00:00:00

The bias-variance tradeoff tells us that as model complexity increases, bias falls and variances increases, leading to a U-shaped test error curve. However, recent empirical results with over-parameterized neural networks are marked by a striking absence of the classic U-shaped test error curve: test error keeps decreasing in wider networks. This suggests that there might not be a bias-variance tradeoff in neural networks with respect to network width, unlike was originally claimed by, e.g., Geman et al. (1992). Motivated by the shaky evidence used to support this claim in neural networks, we measure bias and variance in the modern setting. We find that both bias and variance can decrease as the number of parameters grows. To better understand this, we introduce a new decomposition of the variance to disentangle the effects of optimization and data sampling. We also provide theoretical analysis in a simplified setting that is consistent with our empirical findings.
Learning to Recognize Discontiguous Entities
Authors: Aldrian Obaja Muis, Wei Lu
Date: 2018-10-19 00:00:00
Proceeding: EMNLP 2016 11

This paper focuses on the study of recognizing discontiguous entities. Motivated by a previous work, we propose to use a novel hypergraph representation to jointly encode discontiguous entities of unbounded length, which can overlap with one another. To compare with existing approaches, we first formally introduce the notion of model ambiguity, which defines the difficulty level of interpreting the outputs of a model, and then formally analyze the theoretical advantages of our model over previous existing approaches based on linear-chain CRFs. Our empirical results also show that our model is able to achieve significantly better results when evaluated on standard data with many discontiguous entities.
Leveraging Product as an Activation Function in Deep Networks
Authors: Luke B. Godfrey, Michael S. Gashler
Date: 2018-10-19 00:00:00

Product unit neural networks (PUNNs) are powerful representational models
with a strong theoretical basis, but have proven to be difficult to train with
gradient-based optimizers. We present windowed product unit neural networks
(WPUNNs), a simple method of leveraging product as a nonlinearity in a neural
network. Windowing the product tames the complex gradient surface and enables
WPUNNs to learn effectively, solving the problems faced by PUNNs. WPUNNs use
product layers between traditional sum layers, capturing the representational
power of product units and using the product itself as a nonlinearity. We find
the result that this method works as well as traditional nonlinearities like
ReLU on the MNIST dataset. We demonstrate that WPUNNs can also generalize gated
units in recurrent neural networks, yielding results comparable to LSTM
networks.
Conceptual Organization is Revealed by Consumer Activity Patterns
Authors: Adam N. Hornsby, Thomas Evans, Peter Riefer, Rosie Prior, Bradley C. Love
Date: 2018-10-19 00:00:00

Meaning may arise from an element's role or interactions within a larger
system. For example, hitting nails is more central to people's concept of a
hammer than its particular material composition or other intrinsic features.
Likewise, the importance of a web page may result from its links with other
pages rather than solely from its content. One example of meaning arising from
extrinsic relationships are approaches that extract the meaning of word
concepts from co-occurrence patterns in large, text corpora. The success of
these methods suggest that human activity patterns may reveal conceptual
organization. However, texts do not directly reflect human activity, but
instead serve a communicative function and are usually highly curated or edited
to suit an audience. Here, we apply methods devised for text to a data source
that directly reflects thousands of individuals' activity patterns, namely
supermarket purchases. Using product co-occurrence data from nearly 1.3m
shopping baskets, we trained a topic model to learn 25 high-level concepts (or
"topics"). These topics were found to be comprehensible and coherent by both
retail experts and consumers. Topics ranged from specific (e.g., ingredients
for a stir-fry) to general (e.g., cooking from scratch). Topics tended to be
goal-directed and situational, consistent with the notion that human conceptual
knowledge is tailored to support action. Individual differences in the topics
sampled predicted basic demographic characteristics. These results suggest that
human activity patterns reveal conceptual organization and may give rise to it.
Supervising strong learners by amplifying weak experts
Authors: Paul Christiano, Buck Shlegeris, Dario Amodei
Date: 2018-10-19 00:00:00

Many real world learning tasks involve complex or hard-to-specify objectives,
and using an easier-to-specify proxy can lead to poor performance or misaligned
behavior. One solution is to have humans provide a training signal by
demonstrating or judging performance, but this approach fails if the task is
too complicated for a human to directly evaluate. We propose Iterated
Amplification, an alternative training strategy which progressively builds up a
training signal for difficult problems by combining solutions to easier
subproblems. Iterated Amplification is closely related to Expert Iteration
(Anthony et al., 2017; Silver et al., 2017), except that it uses no external
reward function. We present results in algorithmic environments, showing that
Iterated Amplification can efficiently learn complex behaviors.
Weak Semi-Markov CRFs for NP Chunking in Informal Text
Authors: Aldrian Obaja Muis, Wei Lu
Date: 2018-10-19 00:00:00
Tasks: Chunking

This paper introduces a new annotated corpus based on an existing informal
text corpus: the NUS SMS Corpus (Chen and Kan, 2013). The new corpus includes
76,490 noun phrases from 26,500 SMS messages, annotated by university students.
We then explored several graphical models, including a novel variant of the
semi-Markov conditional random fields (semi-CRF) for the task of noun phrase
chunking. We demonstrated through empirical evaluations on the new dataset that
the new variant yielded similar accuracy but ran in significantly lower running
time compared to the conventional semi-CRF.
Deep Person Re-identification for Probabilistic Data Association in Multiple Pedestrian Tracking
Authors: Brian H. Wang, Yan Wang, Kilian Q. Weinberger, Mark Campbell
Date: 2018-10-19 00:00:00
Tasks: Person Re-Identification, Translation

We present a data association method for vision-based multiple pedestrian
tracking, using deep convolutional features to distinguish between different
people based on their appearances. These re-identification (re-ID) features are
learned such that they are invariant to transformations such as rotation,
translation, and changes in the background, allowing consistent identification
of a pedestrian moving through a scene. We incorporate re-ID features into a
general data association likelihood model for multiple person tracking,
experimentally validate this model by using it to perform tracking in two
evaluation video sequences, and examine the performance improvements gained as
compared to several baseline approaches. Our results demonstrate that using
deep person re-ID for data association greatly improves tracking robustness to
challenges such as occlusions and path crossings.
Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks
Authors: Quan Zhang, Mingyuan Zhou
Date: 2018-10-19 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Data Augmentation, Survival Analysis

We propose Lomax delegate racing (LDR) to explicitly model the mechanism of
survival under competing risks and to interpret how the covariates accelerate
or decelerate the time to event. LDR explains non-monotonic covariate effects
by racing a potentially infinite number of sub-risks, and consequently relaxes
the ubiquitous proportional-hazards assumption which may be too restrictive.
Moreover, LDR is naturally able to model not only censoring, but also missing
event times or event types. For inference, we develop a Gibbs sampler under
data augmentation for moderately sized data, along with a stochastic gradient
descent maximum a posteriori inference algorithm for big data applications.
Illustrative experiments are provided on both synthetic and real datasets, and
comparison with various benchmark algorithms for survival analysis with
competing risks demonstrates distinguished performance of LDR.
Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data
Authors: Santiago Silva, Boris Gutman, Eduardo Romero, Paul M. Thompson, Andre Altmann, Marco Lorenzi
Date: 2018-10-19 00:00:00
Tasks: Federated Learning

At this moment, databanks worldwide contain brain images of previously unimaginable numbers. Combined with developments in data science, these massive data provide the potential to better understand the genetic underpinnings of brain diseases. However, different datasets, which are stored at different institutions, cannot always be shared directly due to privacy and legal concerns, thus limiting the full exploitation of big data in the study of brain disorders. Here we propose a federated learning framework for securely accessing and meta-analyzing any biomedical data without sharing individual information. We illustrate our framework by investigating brain structural relationships across diseases and clinical cohorts. The framework is first tested on synthetic data and then applied to multi-centric, multi-database studies including ADNI, PPMI, MIRIAD and UK Biobank, showing the potential of the approach for further applications in distributed analysis of multi-centric cohorts
Nonlinear integro-differential operator regression with neural networks
Authors: Ravi G. Patel, Olivier Desjardins
Date: 2018-10-19 00:00:00
Tasks: regression

This note introduces a regression technique for finding a class of nonlinear
integro-differential operators from data. The method parametrizes the spatial
operator with neural networks and Fourier transforms such that it can fit a
class of nonlinear operators without needing a library of a priori selected
operators. We verify that this method can recover the spatial operators in the
fractional heat equation and the Kuramoto-Sivashinsky equation from numerical
solutions of the equations.
Fairness for Whom? Critically reframing fairness with Nash Welfare Product
Authors: Ansh Patel
Date: 2018-10-19 00:00:00
Tasks: Binary Classification, Fairness, Prediction

Recent studies on disparate impact in machine learning applications have
sparked a debate around the concept of fairness along with attempts to
formalize its different criteria. Many of these approaches focus on reducing
prediction errors while maximizing sole utility of the institution. This work
seeks to reconceptualize and critically frame the existing discourse on
fairness by underlining the implicit biases embedded in common understandings
of fairness in the literature and how they contrast with its corresponding
economic and legal definitions. This paper expands the concept of utility and
fairness by bringing in concepts from established literature in welfare
economics and game theory. We then translate these concepts for the algorithmic
prediction domain by defining a formalization of Nash Welfare Product that
seeks to expand utility by collapsing that of the institution using the
prediction tool and the individual subject to the prediction into one function.
We then apply a modulating function that makes the fairness and welfare
trade-offs explicit based on designated policy goals and then apply it to a
temporal model to take into account the effects of decisions beyond the scope
of one-shot predictions. We apply this on a binary classification problem and
present results of a multi-epoch simulation based on the UCI Adult Income
dataset and a test case analysis of the ProPublica recidivism dataset that show
that expanding the concept of utility results in a fairer distribution
correcting for the embedded biases in the dataset without sacrificing the
classifier accuracy.
Bayesian Distance Clustering
Authors: Leo L. Duan, David B. Dunson
Date: 2018-10-19 00:00:00
Tasks: Clustering

Model-based clustering is widely-used in a variety of application areas. However, fundamental concerns remain about robustness. In particular, results can be sensitive to the choice of kernel representing the within-cluster data density. Leveraging on properties of pairwise differences between data points, we propose a class of Bayesian distance clustering methods, which rely on modeling the likelihood of the pairwise distances in place of the original data. Although some information in the data is discarded, we gain substantial robustness to modeling assumptions. The proposed approach represents an appealing middle ground between distance- and model-based clustering, drawing advantages from each of these canonical approaches. We illustrate dramatic gains in the ability to infer clusters that are not well represented by the usual choices of kernel. A simulation study is included to assess performance relative to competitors, and we apply the approach to clustering of brain genome expression data. Keywords: Distance-based clustering; Mixture model; Model-based clustering; Model misspecification; Pairwise distance matrix; Partial likelihood; Robustness.
MsCGAN: Multi-scale Conditional Generative Adversarial Networks for Person Image Generation
Authors: Wei Tang, Gui Li, Xinyuan Bao, Teng Li
Date: 2018-10-19 00:00:00
Tasks: Image Generation

To synthesize high-quality person images with arbitrary poses is challenging. In this paper, we propose a novel Multi-scale Conditional Generative Adversarial Networks (MsCGAN), aiming to convert the input conditional person image to a synthetic image of any given target pose, whose appearance and the texture are consistent with the input image. MsCGAN is a multi-scale adversarial network consisting of two generators and two discriminators. One generator transforms the conditional person image into a coarse image of the target pose globally, and the other is to enhance the detailed quality of the synthetic person image through a local reinforcement network. The outputs of the two generators are then merged into a synthetic, discriminant and high-resolution image. On the other hand, the synthetic image is downsampled to multiple resolutions as the input to multi-scale discriminator networks. The proposed multi-scale generators and discriminators handling different levels of visual features can benefit to synthesizing high-resolution person images with realistic appearance and texture. Experiments are conducted on the Market-1501 and DeepFashion datasets to evaluate the proposed model, and both qualitative and quantitative results demonstrate the superior performance of the proposed MsCGAN.
Transfer Learning versus Multi-agent Learning regarding Distributed Decision-Making in Highway Traffic
Authors: Mark Schutera, Niklas Goby, Dirk Neumann, Markus Reischl
Date: 2018-10-19 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Reinforcement Learning, Transfer Learning

Transportation and traffic are currently undergoing a rapid increase in terms
of both scale and complexity. At the same time, an increasing share of traffic
participants are being transformed into agents driven or supported by
artificial intelligence resulting in mixed-intelligence traffic. This work
explores the implications of distributed decision-making in mixed-intelligence
traffic. The investigations are carried out on the basis of an online-simulated
highway scenario, namely the MIT \emph{DeepTraffic} simulation. In the first
step traffic agents are trained by means of a deep reinforcement learning
approach, being deployed inside an elitist evolutionary algorithm for
hyperparameter search. The resulting architectures and training parameters are
then utilized in order to either train a single autonomous traffic agent and
transfer the learned weights onto a multi-agent scenario or else to conduct
multi-agent learning directly. Both learning strategies are evaluated on
different ratios of mixed-intelligence traffic. The strategies are assessed
according to the average speed of all agents driven by artificial intelligence.
Traffic patterns that provoke a reduction in traffic flow are analyzed with
respect to the different strategies.
Probabilistic Matrix Factorization with Personalized Differential Privacy
Authors: Shun Zhang, Laixiang Liu, Zhili Chen, Hong Zhong
Date: 2018-10-19 00:00:00
Tasks: Recommendation Systems

Probabilistic matrix factorization (PMF) plays a crucial role in
recommendation systems. It requires a large amount of user data (such as user
shopping records and movie ratings) to predict personal preferences, and
thereby provides users high-quality recommendation services, which expose the
risk of leakage of user privacy. Differential privacy, as a provable privacy
protection framework, has been applied widely to recommendation systems. It is
common that different individuals have different levels of privacy requirements
on items. However, traditional differential privacy can only provide a uniform
level of privacy protection for all users.
  In this paper, we mainly propose a probabilistic matrix factorization
recommendation scheme with personalized differential privacy (PDP-PMF). It aims
to meet users' privacy requirements specified at the item-level instead of
giving the same level of privacy guarantees for all. We then develop a modified
sampling mechanism (with bounded differential privacy) for achieving PDP. We
also perform a theoretical analysis of the PDP-PMF scheme and demonstrate the
privacy of the PDP-PMF scheme. In addition, we implement the probabilistic
matrix factorization schemes both with traditional and with personalized
differential privacy (DP-PMF, PDP-PMF) and compare them through a series of
experiments. The results show that the PDP-PMF scheme performs well on
protecting the privacy of each user and its recommendation quality is much
better than the DP-PMF scheme.
Hybrid deep neural networks for all-cause Mortality Prediction from LDCT Images
Authors: Pingkun Yan, Hengtao Guo, Ge Wang, Ruben De Man, Mannudeep K. Kalra
Date: 2018-10-19 00:00:00
Tasks: All, Lung Cancer Diagnosis, Mortality Prediction

Known for its high morbidity and mortality rates, lung cancer poses a
significant threat to human health and well-being. However, the same population
is also at high risk for other deadly diseases, such as cardiovascular disease.
Since Low-Dose CT (LDCT) has been shown to significantly improve the lung
cancer diagnosis accuracy, it will be very useful for clinical practice to
predict the all-cause mortality for lung cancer patients to take corresponding
actions. In this paper, we propose a deep learning based method, which takes
both chest LDCT image patches and coronary artery calcification risk scores as
input, for direct prediction of mortality risk of lung cancer subjects. The
proposed method is called Hybrid Risk Network (HyRiskNet) for mortality risk
prediction, which is an end-to-end framework utilizing hybrid imaging features,
instead of completely relying on automatic feature extraction. Our work
demonstrates the feasibility of using deep learning techniques for all-cause
lung cancer mortality prediction from chest LDCT images. The experimental
results show that the proposed HyRiskNet can achieve superior performance
compared with the neural networks with only image input and with other
traditional semi-automatic scoring methods. The study also indicates that
radiologist defined features can well complement convolutional neural networks
for more comprehensive feature extraction.