pwc-archive/papers-with-abstracts huggingface dataset:
Upping the Ante: Towards a Better Benchmark for Chinese-to-English Machine Translation
Authors: Christian Hadiwinoto, Hwee Tou Ng
Date: 2018-05-04 00:00:00
Proceeding: LREC 2018 5
Tasks: Machine Translation, Translation

There are many machine translation (MT) papers that propose novel approaches
and show improvements over their self-defined baselines. The experimental
setting in each paper often differs from one another. As such, it is hard to
determine if a proposed approach is really useful and advances the state of the
art. Chinese-to-English translation is a common translation direction in MT
papers, although there is not one widely accepted experimental setting in
Chinese-to-English MT. Our goal in this paper is to propose a benchmark in
evaluation setup for Chinese-to-English machine translation, such that the
effectiveness of a new proposed MT approach can be directly compared to
previous approaches. Towards this end, we also built a highly competitive
state-of-the-art MT system trained on a large-scale training set. Our system
outperforms reported results on NIST OpenMT test sets in almost all papers
published in major conferences and journals in computational linguistics and
artificial intelligence in the past 11 years. We argue that a standardized
benchmark on data and performance is important for meaningful comparison.
A Boosting Method to Face Image Super-resolution
Authors: Shanjun Mao, Da Zhou, Yiping Zhang, Zhihong Zhang, Jingjing Cao
Date: 2016-09-07 00:00:00
Tasks: Image Reconstruction, Image Super-Resolution, Super-Resolution

Recently sparse representation has gained great success in face image
super-resolution. The conventional sparsity-based methods enforce sparse coding
on face image patches and the representation fidelity is measured by
$\ell_{2}$-norm. Such a sparse coding model regularizes all facial patches
equally, which however ignores distinct natures of different facial patches for
image reconstruction. In this paper, we propose a new weighted-patch
super-resolution method based on AdaBoost. Specifically, in each iteration of
the AdaBoost operation, each facial patch is weighted automatically according
to the performance of the model on it, so as to highlight those patches that
are more critical for improving the reconstruction power in next step. In this
way, through the AdaBoost training procedure, we can focus more on the patches
(face regions) with richer information. Various experimental results on
standard face database show that our proposed method outperforms
state-of-the-art methods in terms of both objective metrics and visual quality.
Intracranial Error Detection via Deep Learning
Authors: Martin Völker, Jiří Hammer, Robin T. Schirrmeister, Joos Behncke, Lukas D. J. Fiederer, Andreas Schulze-Bonhage, Petr Marusič, Wolfram Burgard, Tonio Ball
Date: 2018-05-04 00:00:00
Tasks: Deep Learning, EEG, Electroencephalogram (EEG)

Deep learning techniques have revolutionized the field of machine learning
and were recently successfully applied to various classification problems in
noninvasive electroencephalography (EEG). However, these methods were so far
only rarely evaluated for use in intracranial EEG. We employed convolutional
neural networks (CNNs) to classify and characterize the error-related brain
response as measured in 24 intracranial EEG recordings. Decoding accuracies of
CNNs were significantly higher than those of a regularized linear discriminant
analysis. Using time-resolved deep decoding, it was possible to classify errors
in various regions in the human brain, and further to decode errors over 200 ms
before the actual erroneous button press, e.g., in the precentral gyrus.
Moreover, deeper networks performed better than shallower networks in
distinguishing correct from error trials in all-channel decoding. In single
recordings, up to 100 % decoding accuracy was achieved. Visualization of the
networks' learned features indicated that multivariate decoding on an ensemble
of channels yields related, albeit non-redundant information compared to
single-channel decoding. In summary, here we show the usefulness of deep
learning for both intracranial error decoding and mapping of the
spatio-temporal structure of the human error processing network.
Sharp convergence rates for Langevin dynamics in the nonconvex setting
Authors: Xiang Cheng, Niladri S. Chatterji, Yasin Abbasi-Yadkori, Peter L. Bartlett, Michael. I. Jordan
Date: 2018-05-04 00:00:00

We study the problem of sampling from a distribution $p^*(x) \propto \exp\left(-U(x)\right)$, where the function $U$ is $L$-smooth everywhere and $m$-strongly convex outside a ball of radius $R$, but potentially nonconvex inside this ball. We study both overdamped and underdamped Langevin MCMC and establish upper bounds on the number of steps required to obtain a sample from a distribution that is within $\epsilon$ of $p^*$ in $1$-Wasserstein distance. For the first-order method (overdamped Langevin MCMC), the iteration complexity is $\tilde{\mathcal{O}}\left(e^{cLR^2}d/\epsilon^2\right)$, where $d$ is the dimension of the underlying space. For the second-order method (underdamped Langevin MCMC), the iteration complexity is $\tilde{\mathcal{O}}\left(e^{cLR^2}\sqrt{d}/\epsilon\right)$ for an explicit positive constant $c$. Surprisingly, the iteration complexity for both these algorithms is only polynomial in the dimension $d$ and the target accuracy $\epsilon$. It is exponential, however, in the problem parameter $LR^2$, which is a measure of non-log-concavity of the target distribution.
Cross-lingual Candidate Search for Biomedical Concept Normalization
Authors: Roland Roller, Madeleine Kittner, Dirk Weissenborn, Ulf Leser
Date: 2018-05-04 00:00:00
Tasks: Translation

Biomedical concept normalization links concept mentions in texts to a
semantically equivalent concept in a biomedical knowledge base. This task is
challenging as concepts can have different expressions in natural languages,
e.g. paraphrases, which are not necessarily all present in the knowledge base.
Concept normalization of non-English biomedical text is even more challenging
as non-English resources tend to be much smaller and contain less synonyms. To
overcome the limitations of non-English terminologies we propose a
cross-lingual candidate search for concept normalization using a
character-based neural translation model trained on a multilingual biomedical
terminology. Our model is trained with Spanish, French, Dutch and German
versions of UMLS. The evaluation of our model is carried out on the French
Quaero corpus, showing that it outperforms most teams of CLEF eHealth 2015 and
2016. Additionally, we compare performance to commercial translators on
Spanish, French, Dutch and German versions of Mantra. Our model performs
similarly well, but is free of charge and can be run locally. This is
particularly important for clinical NLP applications as medical documents
underlay strict privacy restrictions.
Learning Conceptual Space Representations of Interrelated Concepts
Authors: Zied Bouraoui, Steven Schockaert
Date: 2018-05-03 00:00:00
Tasks: Knowledge Base Completion

Several recently proposed methods aim to learn conceptual space
representations from large text collections. These learned representations
asso- ciate each object from a given domain of interest with a point in a
high-dimensional Euclidean space, but they do not model the concepts from this
do- main, and can thus not directly be used for catego- rization and related
cognitive tasks. A natural solu- tion is to represent concepts as Gaussians,
learned from the representations of their instances, but this can only be
reliably done if sufficiently many in- stances are given, which is often not
the case. In this paper, we introduce a Bayesian model which addresses this
problem by constructing informative priors from background knowledge about how
the concepts of interest are interrelated with each other. We show that this
leads to substantially better pre- dictions in a knowledge base completion
task.
Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffe
Authors: Jiong Gong, Haihao Shen, Guoming Zhang, Xiaoli Liu, Shane Li, Ge Jin, Niharika Maheshwari, Evarist Fomenko, Eden Segal
Date: 2018-05-04 00:00:00
Tasks: Deep Learning, Model Optimization

High throughput and low latency inference of deep neural networks are
critical for the deployment of deep learning applications. This paper presents
the efficient inference techniques of IntelCaffe, the first Intel optimized
deep learning framework that supports efficient 8-bit low precision inference
and model optimization techniques of convolutional neural networks on Intel
Xeon Scalable Processors. The 8-bit optimized model is automatically generated
with a calibration process from FP32 model without the need of fine-tuning or
retraining. We show that the inference throughput and latency with ResNet-50,
Inception-v3 and SSD are improved by 1.38X-2.9X and 1.35X-3X respectively with
neglectable accuracy loss from IntelCaffe FP32 baseline and by 56X-75X and
26X-37X from BVLC Caffe. All these techniques have been open-sourced on
IntelCaffe GitHub1, and the artifact is provided to reproduce the result on
Amazon AWS Cloud.
Power Law in Sparsified Deep Neural Networks
Authors: Lu Hou, James T. Kwok
Date: 2018-05-04 00:00:00
Tasks: Continual Learning

The power law has been observed in the degree distributions of many
biological neural networks. Sparse deep neural networks, which learn an
economical representation from the data, resemble biological neural networks in
many ways. In this paper, we study if these artificial networks also exhibit
properties of the power law. Experimental results on two popular deep learning
models, namely, multilayer perceptrons and convolutional neural networks, are
affirmative. The power law is also naturally related to preferential
attachment. To study the dynamical properties of deep networks in continual
learning, we propose an internal preferential attachment model to explain how
the network topology evolves. Experimental results show that with the arrival
of a new task, the new connections made follow this preferential attachment
process.
Efficient Design of Hardware-Enabled Reservoir Computing in FPGAs
Authors: Bogdan Penkovsky, Laurent Larger, Daniel Brunner
Date: 2018-05-04 00:00:00
Tasks: Dimensionality Reduction

In this work, we propose a new approach towards the efficient optimization
and implementation of reservoir computing hardware reducing the required domain
expert knowledge and optimization effort. First, we adapt the reservoir input
mask to the structure of the data via linear autoencoders. We therefore
incorporate the advantages of dimensionality reduction and dimensionality
expansion achieved by conventional algorithmically efficient linear algebra
procedures of principal component analysis. Second, we employ
evolutionary-inspired genetic algorithm techniques resulting in a highly
efficient optimization of reservoir dynamics with dramatically reduced number
of evaluations comparing to exhaustive search. We illustrate the method on the
so-called single-node reservoir computing architecture, especially suitable for
implementation in ultrahigh-speed hardware. The combination of both methods and
the resulting reduction of time required for performance optimization of a
hardware system establish a strategy towards machine learning hardware capable
of self-adaption to optimally solve specific problems. We confirm the validity
of those principles building reservoir computing hardware based on a
field-programmable gate array.
BelMan: Bayesian Bandits on the Belief--Reward Manifold
Authors: Debabrota Basu, Pierre Senellart, Stéphane Bressan
Date: 2018-05-04 00:00:00

We propose a generic, Bayesian, information geometric approach to the exploration--exploitation trade-off in multi-armed bandit problems. Our approach, BelMan, uniformly supports pure exploration, exploration--exploitation, and two-phase bandit problems. The knowledge on bandit arms and their reward distributions is summarised by the barycentre of the joint distributions of beliefs and rewards of the arms, the \emph{pseudobelief-reward}, within the beliefs-rewards manifold. BelMan alternates \emph{information projection} and \emph{reverse information projection}, i.e., projection of the pseudobelief-reward onto beliefs-rewards to choose the arm to play, and projection of the resulting beliefs-rewards onto the pseudobelief-reward. It introduces a mechanism that infuses an exploitative bias by means of a \emph{focal distribution}, i.e., a reward distribution that gradually concentrates on higher rewards. Comparative performance evaluation with state-of-the-art algorithms shows that BelMan is not only competitive but can also outperform other approaches in specific setups, for instance involving many arms and continuous rewards.
Estimating Learnability in the Sublinear Data Regime
Authors: Weihao Kong, Gregory Valiant
Date: 2018-05-04 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Binary Classification

We consider the problem of estimating how well a model class is capable of
fitting a distribution of labeled data. We show that it is often possible to
accurately estimate this "learnability" even when given an amount of data that
is too small to reliably learn any accurate model. Our first result applies to
the setting where the data is drawn from a $d$-dimensional distribution with
isotropic covariance (or known covariance), and the label of each datapoint is
an arbitrary noisy function of the datapoint. In this setting, we show that
with $O(\sqrt{d})$ samples, one can accurately estimate the fraction of the
variance of the label that can be explained via the best linear function of the
data. In contrast to this sublinear sample size, finding an approximation of
the best-fit linear function requires on the order of $d$ samples. Our
sublinear sample results and approach also extend to the non-isotropic setting,
where the data distribution has an (unknown) arbitrary covariance matrix: we
show that, if the label $y$ of point $x$ is a linear function with independent
noise, $y = \langle x , \beta \rangle + noise$ with $\|\beta \|$ bounded, the
variance of the noise can be estimated to error $\epsilon$ with
$O(d^{1-1/\log{1/\epsilon}})$ if the covariance matrix has bounded condition
number, or $O(d^{1-\sqrt{\epsilon}})$ if there are no bounds on the condition
number. We also establish that these sample complexities are optimal, to
constant factors. Finally, we extend these techniques to the setting of binary
classification, where we obtain analogous sample complexities for the problem
of estimating the prediction error of the best linear classifier, in a natural
model of binary labeled data. We demonstrate the practical viability of our
approaches on several real and synthetic datasets.
Recent Progress on Graph Partitioning Problems Using Evolutionary Computation
Authors: Hye-Jin Kim, Yong-Hyuk Kim
Date: 2018-05-04 00:00:00
Tasks: Combinatorial Optimization, graph partitioning, Survey

The graph partitioning problem (GPP) is a representative combinatorial
optimization problem which is NP-hard. Currently, various approaches to solve
GPP have been introduced. Among these, the GPP solution using evolutionary
computation (EC) is an effective approach. There has not been any survey on the
research applying EC to GPP since 2011. In this survey, we introduce various
attempts to apply EC to GPP made in the recent seven years.
Distribution Assertive Regression
Authors: Kumarjit Pathak, Jitin Kapila, Aasheesh Barvey, Nikit Gawande
Date: 2018-05-04 00:00:00
Tasks: Diagnostic, regression

In regression modelling approach, the main step is to fit the regression line
as close as possible to the target variable. In this process most algorithms
try to fit all of the data in a single line and hence fitting all parts of
target variable in one go. It was observed that the error between predicted and
target variable usually have a varying behavior across the various quantiles of
the dependent variable and hence single point diagnostic like MAPE has its
limitation to signify the level of fitness across the distribution of
Y(dependent variable). To address this problem, a novel approach is proposed in
the paper to deal with regression fitting over various quantiles of target
variable. Using this approach we have significantly improved the eccentric
behavior of the distance (error) between predicted and actual value of
regression. Our proposed solution is based on understanding the segmented
behavior of the data with respect to the internal segments within the data and
approach for retrospectively fitting the data based on each quantile behavior.
We believe exploring and using this approach would help in achieving better and
more explainable results in most settings of real world data modelling
problems.
Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13)
Authors: Yuria Utsumi, Ognjen Rudovic, Kelly Peterson, Ricardo Guerrero, Rosalind W. Picard
Date: 2018-02-22 00:00:00
Tasks: Gaussian Processes

In this paper, we introduce the use of a personalized Gaussian Process model
(pGP) to predict per-patient changes in ADAS-Cog13 -- a significant predictor
of Alzheimer's Disease (AD) in the cognitive domain -- using data from each
patient's previous visits, and testing on future (held-out) data. We start by
learning a population-level model using multi-modal data from previously seen
patients using a base Gaussian Process (GP) regression. The personalized GP
(pGP) is formed by adapting the base GP sequentially over time to a new
(target) patient using domain adaptive GPs. We extend this personalized
approach to predict the values of ADAS-Cog13 over the future 6, 12, 18, and 24
months. We compare this approach to a GP model trained only on past data of the
target patients (tGP), as well as to a new approach that combines pGP with tGP.
We find that the new approach, combining pGP with tGP, leads to large
improvements in accurately forecasting future ADAS-Cog13 scores.
Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks
Authors: Alex White, Matthieu Vignes
Date: 2018-05-04 00:00:00

Biological networks are a very convenient modelling and visualisation tool to
discover knowledge from modern high-throughput genomics and postgenomics data
sets. Indeed, biological entities are not isolated, but are components of
complex multi-level systems. We go one step further and advocate for the
consideration of causal representations of the interactions in living
systems.We present the causal formalism and bring it out in the context of
biological networks, when the data is observational. We also discuss its
ability to decipher the causal information flow as observed in gene expression.
We also illustrate our exploration by experiments on small simulated networks
as well as on a real biological data set.
Facebook Reaction-Based Emotion Classifier as Cue for Sarcasm Detection
Authors: Po Chen Kuo, Fernando H. Calderon Alvarado, Yi-Shin Chen
Date: 2018-05-04 00:00:00
Tasks: Sarcasm Detection

Online social media users react to content in them based on context. Emotions
or mood play a significant part of these reactions, which has filled these
platforms with opinionated content. Different approaches and applications to
make better use of this data are continuously being developed. However, due to
the nature of the data, the variety of platforms, and dynamic online user
behavior, there are still many issues to be dealt with. It remains a challenge
to properly obtain a reliable emotional status from a user prior to posting a
comment. This work introduces a methodology that explores semi-supervised
multilingual emotion detection based on the overlap of Facebook reactions and
textual data. With the resulting emotion detection system we evaluate the
possibility of using emotions and user behavior features for the task of
sarcasm detection. More than 1 million English and Chinese comments from over
62,000 public Facebook pages posts have been collected and processed, conducted
experiments show acceptable performance metrics.
Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising
Authors: Chenyu You, Qingsong Yang, Hongming Shan, Lars Gjesteby, Guang Li, Shenghong Ju, Zhuiyang Zhang, Zhen Zhao, Yi Zhang, Wenxiang Cong, Ge Wang
Date: 2018-05-02 00:00:00
Tasks: Computed Tomography (CT), Denoising

Computed tomography (CT) is a popular medical imaging modality in clinical
applications. At the same time, the x-ray radiation dose associated with CT
scans raises public concerns due to its potential risks to the patients. Over
the past years, major efforts have been dedicated to the development of
Low-Dose CT (LDCT) methods. However, the radiation dose reduction compromises
the signal-to-noise ratio (SNR), leading to strong noise and artifacts that
down-grade CT image quality. In this paper, we propose a novel 3D noise
reduction method, called Structure-sensitive Multi-scale Generative Adversarial
Net (SMGAN), to improve the LDCT image quality. Specifically, we incorporate
three-dimensional (3D) volumetric information to improve the image quality.
Also, different loss functions for training denoising models are investigated.
Experiments show that the proposed method can effectively preserve structural
and texture information from normal-dose CT (NDCT) images, and significantly
suppress noise and artifacts. Qualitative visual assessments by three
experienced radiologists demonstrate that the proposed method retrieves more
detailed information, and outperforms competing methods.
A Minimax Surrogate Loss Approach to Conditional Difference Estimation
Authors: Siong Thye Goh, Cynthia Rudin
Date: 2018-03-10 00:00:00

We present a new machine learning approach to estimate personalized treatment
effects in the classical potential outcomes framework with binary outcomes. To
overcome the problem that both treatment and control outcomes for the same unit
are required for supervised learning, we propose surrogate loss functions that
incorporate both treatment and control data. The new surrogates yield tighter
bounds than the sum of losses for treatment and control groups. A specific
choice of loss function, namely a type of hinge loss, yields a minimax support
vector machine formulation. The resulting optimization problem requires the
solution to only a single convex optimization problem, incorporating both
treatment and control units, and it enables the kernel trick to be used to
handle nonlinear (also non-parametric) estimation. Statistical learning bounds
are also presented for the framework, and experimental results.
When Politicians Talk About Politics: Identifying Political Tweets of Brazilian Congressmen
Authors: Lucas S. Oliveira, Pedro O. S. Vaz de Melo, Marcelo S. Amaral, José Antônio. G. Pinho
Date: 2018-05-04 00:00:00

Since June 2013, when Brazil faced the largest and most significant mass
protests in a generation, a political crisis is in course. In midst of this
crisis, Brazilian politicians use social media to communicate with the
electorate in order to retain or to grow their political capital. The problem
is that many controversial topics are in course and deputies may prefer to
avoid such themes in their messages. To characterize this behavior, we propose
a method to accurately identify political and non-political tweets
independently of the deputy who posted it and of the time it was posted.
Moreover, we collected tweets of all congressmen who were active on Twitter and
worked in the Brazilian parliament from October 2013 to October 2017. To
evaluate our method, we used word clouds and a topic model to identify the main
political and non-political latent topics in parliamentarian tweets. Both
results indicate that our proposal is able to accurately distinguish political
from non-political tweets. Moreover, our analyses revealed a striking fact:
more than half of the messages posted by Brazilian deputies are non-political.