pwc-archive/papers-with-abstracts huggingface dataset:
Yes-Net: An effective Detector Based on Global Information
Authors: Liangzhuang Ma, Xin Kan, Qianjiang Xiao, Wenlong Liu, Peiqin Sun
Date: 2017-06-28 00:00:00
Tasks: object-detection, Object Detection, Real-Time Object Detection

This paper introduces a new real-time object detection approach named
Yes-Net. It realizes the prediction of bounding boxes and class via single
neural network like YOLOv2 and SSD, but owns more efficient and outstanding
features. It combines local information with global information by adding the
RNN architecture as a packed unit in CNN model to form the basic feature
extractor. Independent anchor boxes coming from full-dimension k-means is also
applied in Yes-Net, it brings better average IOU than grid anchor box. In
addition, instead of NMS, Yes-Net uses RNN as a filter to get the final boxes,
which is more efficient. For 416 x 416 input, Yes-Net achieves 79.2% mAP on
VOC2007 test at 39 FPS on an Nvidia Titan X Pascal.
Providing Effective Real-time Feedback in Simulation-based Surgical Training
Authors: Xingjun Ma, Sudanthi Wijewickrema, Yun Zhou, Shuo Zhou, Stephen O'Leary, James Bailey
Date: 2017-06-30 00:00:00

Virtual reality simulation is becoming popular as a training platform in
surgical education. However, one important aspect of simulation-based surgical
training that has not received much attention is the provision of automated
real-time performance feedback to support the learning process. Performance
feedback is actionable advice that improves novice behaviour. In simulation,
automated feedback is typically extracted from prediction models trained using
data mining techniques. Existing techniques suffer from either low
effectiveness or low efficiency resulting in their inability to be used in
real-time. In this paper, we propose a random forest based method that finds a
balance between effectiveness and efficiency. Experimental results in a
temporal bone surgery simulation show that the proposed method is able to
extract highly effective feedback at a high level of efficiency.
Neural Sequence Model Training via $α$-divergence Minimization
Authors: Sotetsu Koyamada, Yuta Kikuchi, Atsunori Kanemura, Shin-ichi Maeda, Shin Ishii
Date: 2017-06-30 00:00:00
Tasks: Machine Translation, model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Translation

We propose a new neural sequence model training method in which the objective
function is defined by $\alpha$-divergence. We demonstrate that the objective
function generalizes the maximum-likelihood (ML)-based and reinforcement
learning (RL)-based objective functions as special cases (i.e., ML corresponds
to $\alpha \to 0$ and RL to $\alpha \to1$). We also show that the gradient of
the objective function can be considered a mixture of ML- and RL-based
objective gradients. The experimental results of a machine translation task
show that minimizing the objective function with $\alpha > 0$ outperforms
$\alpha \to 0$, which corresponds to ML-based methods.
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data
Authors: Cheng Ju, Richard Wyss, Jessica M. Franklin, Sebastian Schneeweiss, Jenny Häggström, Mark J. Van Der Laan
Date: 2017-06-30 00:00:00
Tasks: Causal Inference, Model Selection

Propensity score (PS) based estimators are increasingly used for causal
inference in observational studies. However, model selection for PS estimation
in high-dimensional data has received little attention. In these settings, PS
models have traditionally been selected based on the goodness-of-fit for the
treatment mechanism itself, without consideration of the causal parameter of
interest. Collaborative minimum loss-based estimation (C-TMLE) is a novel
methodology for causal inference that takes into account information on the
causal parameter of interest when selecting a PS model. This "collaborative
learning" considers variable associations with both treatment and outcome when
selecting a PS model in order to minimize a bias-variance trade off in the
estimated treatment effect. In this study, we introduce a novel approach for
collaborative model selection when using the LASSO estimator for PS estimation
in high-dimensional covariate settings. To demonstrate the importance of
selecting the PS model collaboratively, we designed quasi-experiments based on
a real electronic healthcare database, where only the potential outcomes were
manually generated, and the treatment and baseline covariates remained
unchanged. Results showed that the C-TMLE algorithm outperformed other
competing estimators for both point estimation and confidence interval
coverage. In addition, the PS model selected by C-TMLE could be applied to
other PS-based estimators, which also resulted in substantive improvement for
both point estimation and confidence interval coverage. We illustrate the
discussed concepts through an empirical example comparing the effects of
non-selective nonsteroidal anti-inflammatory drugs with selective COX-2
inhibitors on gastrointestinal complications in a population of Medicare
beneficiaries.
Improvement of training set structure in fusion data cleaning using Time-Domain Global Similarity method
Authors: Jian Liu, Ting Lan, Hong Qin
Date: 2017-06-30 00:00:00
Tasks: Classification, Diagnostic, General Classification

Traditional data cleaning identifies dirty data by classifying original data
sequences, which is a class$-$imbalanced problem since the proportion of
incorrect data is much less than the proportion of correct ones for most
diagnostic systems in Magnetic Confinement Fusion (MCF) devices. When using
machine learning algorithms to classify diagnostic data based on
class$-$imbalanced training set, most classifiers are biased towards the major
class and show very poor classification rates on the minor class. By
transforming the direct classification problem about original data sequences
into a classification problem about the physical similarity between data
sequences, the class$-$balanced effect of Time$-$Domain Global Similarity
(TDGS) method on training set structure is investigated in this paper.
Meanwhile, the impact of improved training set structure on data cleaning
performance of TDGS method is demonstrated with an application example in EAST
POlarimetry$-$INTerferometry (POINT) system.
How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution
Authors: Shuang Wang, Bo Yue, Xuefeng Liang, Peiyuan Ji, Licheng Jiao
Date: 2016-04-06 00:00:00
Tasks: Super-Resolution

Wisely utilizing the internal and external learning methods is a new
challenge in super-resolution problem. To address this issue, we analyze the
attributes of two methodologies and find two observations of their recovered
details: 1) they are complementary in both feature space and image plane, 2)
they distribute sparsely in the spatial space. These inspire us to propose a
low-rank solution which effectively integrates two learning methods and then
achieves a superior result. To fit this solution, the internal learning method
and the external learning method are tailored to produce multiple preliminary
results. Our theoretical analysis and experiment prove that the proposed
low-rank solution does not require massive inputs to guarantee the performance,
and thereby simplifying the design of two learning methods for the solution.
Intensive experiments show the proposed solution improves the single learning
method in both qualitative and quantitative assessments. Surprisingly, it shows
more superior capability on noisy images and outperforms state-of-the-art
methods.
Link Mining for Kernel-based Compound-Protein Interaction Predictions Using a Chemogenomics Approach
Authors: Masahito Ohue, Takuro Yamazaki, Tomohiro Ban, Yutaka Akiyama
Date: 2017-05-04 00:00:00
Tasks: Drug Discovery, Prediction

Virtual screening (VS) is widely used during computational drug discovery to
reduce costs. Chemogenomics-based virtual screening (CGBVS) can be used to
predict new compound-protein interactions (CPIs) from known CPI network data
using several methods, including machine learning and data mining. Although
CGBVS facilitates highly efficient and accurate CPI prediction, it has poor
performance for prediction of new compounds for which CPIs are unknown. The
pairwise kernel method (PKM) is a state-of-the-art CGBVS method and shows high
accuracy for prediction of new compounds. In this study, on the basis of link
mining, we improved the PKM by combining link indicator kernel (LIK) and
chemical similarity and evaluated the accuracy of these methods. The proposed
method obtained an average area under the precision-recall curve (AUPR) value
of 0.562, which was higher than that achieved by the conventional Gaussian
interaction profile (GIP) method (0.425), and the calculation time was only
increased by a few percent.
Genetic Algorithm for Solving Simple Mathematical Equality Problem
Authors: Denny Hermawanto
Date: 2013-08-16 00:00:00
Proceeding: Neural and Evolutionary Computing 2017 6
Tasks: Philosophy

This paper explains genetic algorithm for novice in this field. Basic
philosophy of genetic algorithm and its flowchart are described. Step by step
numerical computation of genetic algorithm for solving simple mathematical
equality problem will be briefly explained
Hypothesis Testing For Densities and High-Dimensional Multinomials: Sharp Local Minimax Rates
Authors: Sivaraman Balakrishnan, Larry Wasserman
Date: 2017-06-30 00:00:00
Tasks: Two-sample testing

We consider the goodness-of-fit testing problem of distinguishing whether the
data are drawn from a specified distribution, versus a composite alternative
separated from the null in the total variation metric. In the discrete case, we
consider goodness-of-fit testing when the null distribution has a possibly
growing or unbounded number of categories. In the continuous case, we consider
testing a Lipschitz density, with possibly unbounded support, in the
low-smoothness regime where the Lipschitz parameter is not assumed to be
constant. In contrast to existing results, we show that the minimax rate and
critical testing radius in these settings depend strongly, and in a precise
way, on the null distribution being tested and this motivates the study of the
(local) minimax rate as a function of the null distribution. For multinomials
the local minimax rate was recently studied in the work of Valiant and Valiant.
We re-visit and extend their results and develop two modifications to the
chi-squared test whose performance we characterize. For testing Lipschitz
densities, we show that the usual binning tests are inadequate in the
low-smoothness regime and we design a spatially adaptive partitioning scheme
that forms the basis for our locally minimax optimal tests. Furthermore, we
provide the first local minimax lower bounds for this problem which yield a
sharp characterization of the dependence of the critical radius on the null
hypothesis being tested. In the low-smoothness regime we also provide adaptive
tests, that adapt to the unknown smoothness parameter. We illustrate our
results with a variety of simulations that demonstrate the practical utility of
our proposed tests.
An Optimization Framework with Flexible Inexact Inner Iterations for Nonconvex and Nonsmooth Programming
Authors: Yiyang Wang, Risheng Liu, Xiaoliang Song, Zhixun Su
Date: 2017-02-28 00:00:00

In recent years, numerous vision and learning tasks have been (re)formulated
as nonconvex and nonsmooth programmings(NNPs). Although some algorithms have
been proposed for particular problems, designing fast and flexible optimization
schemes with theoretical guarantee is a challenging task for general NNPs. It
has been investigated that performing inexact inner iterations often benefit to
special applications case by case, but their convergence behaviors are still
unclear. Motivated by these practical experiences, this paper designs a novel
algorithmic framework, named inexact proximal alternating direction method
(IPAD) for solving general NNPs. We demonstrate that any numerical algorithms
can be incorporated into IPAD for solving subproblems and the convergence of
the resulting hybrid schemes can be consistently guaranteed by a series of
simple error conditions. Beyond the guarantee in theory, numerical experiments
on both synthesized and real-world data further demonstrate the superiority and
flexibility of our IPAD framework for practical use.
Generalized notions of sparsity and restricted isometry property. Part II: Applications
Authors: Marius Junge, Kiryung Lee
Date: 2017-06-28 00:00:00

The restricted isometry property (RIP) is a universal tool for data recovery.
We explore the implication of the RIP in the framework of generalized sparsity
and group measurements introduced in the Part I paper. It turns out that for a
given measurement instrument the number of measurements for RIP can be improved
by optimizing over families of Banach spaces. Second, we investigate the
preservation of difference of two sparse vectors, which is not trivial in
generalized models. Third, we extend the RIP of partial Fourier measurements at
optimal scaling of number of measurements with random sign to far more general
group structured measurements. Lastly, we also obtain RIP in infinite dimension
in the context of Fourier measurement concepts with sparsity naturally replaced
by smoothness assumptions.
Generalized notions of sparsity and restricted isometry property. Part I: A unified framework
Authors: Marius Junge, Kiryung Lee
Date: 2017-06-28 00:00:00
Tasks: compressed sensing, Dimensionality Reduction

The restricted isometry property (RIP) is an integral tool in the analysis of
various inverse problems with sparsity models. Motivated by the applications of
compressed sensing and dimensionality reduction of low-rank tensors, we propose
generalized notions of sparsity and provide a unified framework for the
corresponding RIP, in particular when combined with isotropic group actions.
Our results extend an approach by Rudelson and Vershynin to a much broader
context including commutative and noncommutative function spaces. Moreover, our
Banach space notion of sparsity applies to affine group actions. The
generalized approach in particular applies to high order tensor products.
Towards Bursting Filter Bubble via Contextual Risks and Uncertainties
Authors: Rikiya Takahashi, Shunan Zhang
Date: 2017-06-30 00:00:00
Tasks: Articles, News Recommendation

A rising topic in computational journalism is how to enhance the diversity in
news served to subscribers to foster exploration behavior in news reading.
Despite the success of preference learning in personalized news recommendation,
their over-exploitation causes filter bubble that isolates readers from
opposing viewpoints and hurts long-term user experiences with lack of
serendipity. Since news providers can recommend neither opposite nor
diversified opinions if unpopularity of these articles is surely predicted,
they can only bet on the articles whose forecasts of click-through rate involve
high variability (risks) or high estimation errors (uncertainties). We propose
a novel Bayesian model of uncertainty-aware scoring and ranking for news
articles. The Bayesian binary classifier models probability of success (defined
as a news click) as a Beta-distributed random variable conditional on a vector
of the context (user features, article features, and other contextual
features). The posterior of the contextual coefficients can be computed
efficiently using a low-rank version of Laplace's method via thin Singular
Value Decomposition. Efficiencies in personalized targeting of exceptional
articles, which are chosen by each subscriber in test period, are evaluated on
real-world news datasets. The proposed estimator slightly outperformed existing
training and scoring algorithms, in terms of efficiency in identifying
successful outliers.
Online and Linear-Time Attention by Enforcing Monotonic Alignments
Authors: Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck
Date: 2017-04-03 00:00:00
Proceeding: ICML 2017 8
Tasks: Machine Translation, Sentence, Sentence Summarization, speech-recognition, Speech Recognition, Text Summarization, Translation

Recurrent neural network models with an attention mechanism have proven to be
extremely effective on a wide variety of sequence-to-sequence problems.
However, the fact that soft attention mechanisms perform a pass over the entire
input sequence when producing each element in the output sequence precludes
their use in online settings and results in a quadratic time complexity. Based
on the insight that the alignment between input and output sequence elements is
monotonic in many problems of interest, we propose an end-to-end differentiable
method for learning monotonic alignments which, at test time, enables computing
attention online and in linear time. We validate our approach on sentence
summarization, machine translation, and online speech recognition problems and
achieve results competitive with existing sequence-to-sequence models.
Sparse Approximation via Generating Point Sets
Authors: Avrim Blum, Sariel Har-Peled, Benjamin Raichel
Date: 2015-07-09 00:00:00

$ \newcommand{\kalg}{{k_{\mathrm{alg}}}}
  \newcommand{\kopt}{{k_{\mathrm{opt}}}}
  \newcommand{\algset}{{T}} \renewcommand{\Re}{\mathbb{R}}
  \newcommand{\eps}{\varepsilon} \newcommand{\pth}[2][\!]{#1\left({#2}\right)}
\newcommand{\npoints}{n} \newcommand{\ballD}{\mathsf{b}}
\newcommand{\dataset}{{P}} $ For a set $\dataset$ of $\npoints$ points in the
unit ball $\ballD \subseteq \Re^d$, consider the problem of finding a small
subset $\algset \subseteq \dataset$ such that its convex-hull
$\eps$-approximates the convex-hull of the original set. We present an
efficient algorithm to compute such a $\eps'$-approximation of size $\kalg$,
where $\eps'$ is function of $\eps$, and $\kalg$ is a function of the minimum
size $\kopt$ of such an $\eps$-approximation. Surprisingly, there is no
dependency on the dimension $d$ in both bounds. Furthermore, every point of
$\dataset$ can be $\eps$-approximated by a convex-combination of points of
$\algset$ that is $O(1/\eps^2)$-sparse.
  Our result can be viewed as a method for sparse, convex autoencoding:
approximately representing the data in a compact way using sparse combinations
of a small subset $\algset$ of the original data. The new algorithm can be
kernelized, and it preserves sparsity in the original input.
Deriving Probability Density Functions from Probabilistic Functional Programs
Authors: Sooraj Bhat, Johannes Borgström, Andrew D. Gordon, Claudio Russo
Date: 2017-04-04 00:00:00
Tasks: BIG-bench Machine Learning

The probability density function of a probability distribution is a
fundamental concept in probability theory and a key ingredient in various
widely used machine learning methods. However, the necessary framework for
compiling probabilistic functional programs to density functions has only
recently been developed. In this work, we present a density compiler for a
probabilistic language with failure and both discrete and continuous
distributions, and provide a proof of its soundness. The compiler greatly
reduces the development effort of domain experts, which we demonstrate by
solving inference problems from various scientific applications, such as
modelling the global carbon cycle, using a standard Markov chain Monte Carlo
framework.
Automatic Face Image Quality Prediction
Authors: Lacey Best-Rowden, Anil K. Jain
Date: 2017-06-29 00:00:00
Tasks: Face Image Quality, Face Recognition, Prediction

Face image quality can be defined as a measure of the utility of a face image
to automatic face recognition. In this work, we propose (and compare) two
methods for automatic face image quality based on target face quality values
from (i) human assessments of face image quality (matcher-independent), and
(ii) quality values computed from similarity scores (matcher-dependent). A
support vector regression model trained on face features extracted using a deep
convolutional neural network (ConvNet) is used to predict the quality of a face
image. The proposed methods are evaluated on two unconstrained face image
databases, LFW and IJB-A, which both contain facial variations with multiple
quality factors. Evaluation of the proposed automatic face image quality
measures shows we are able to reduce the FNMR at 1% FMR by at least 13% for two
face matchers (a COTS matcher and a ConvNet matcher) by using the proposed face
quality to select subsets of face images and video frames for matching
templates (i.e., multiple faces per subject) in the IJB-A protocol. To our
knowledge, this is the first work to utilize human assessments of face image
quality in designing a predictor of unconstrained face quality that is shown to
be effective in cross-database evaluation.
Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study
Authors: Samuel Ritter, David G. T. Barrett, Adam Santoro, Matt M. Botvinick
Date: 2017-06-26 00:00:00
Proceeding: ICML 2017 8
Tasks: One-Shot Learning

Deep neural networks (DNNs) have achieved unprecedented performance on a wide
range of complex tasks, rapidly outpacing our understanding of the nature of
their solutions. This has caused a recent surge of interest in methods for
rendering modern neural systems more interpretable. In this work, we propose to
address the interpretability problem in modern DNNs using the rich history of
problem descriptions, theories and experimental methods developed by cognitive
psychologists to study the human mind. To explore the potential value of these
tools, we chose a well-established analysis from developmental psychology that
explains how children learn word labels for objects, and applied that analysis
to DNNs. Using datasets of stimuli inspired by the original cognitive
psychology experiments, we find that state-of-the-art one shot learning models
trained on ImageNet exhibit a similar bias to that observed in humans: they
prefer to categorize objects according to shape rather than color. The
magnitude of this shape bias varies greatly among architecturally identical,
but differently seeded models, and even fluctuates within seeds throughout
training, despite nearly equivalent classification performance. These results
demonstrate the capability of tools from cognitive psychology for exposing
hidden computational properties of DNNs, while concurrently providing us with a
computational model for human word learning.
Scale-Aware Face Detection
Authors: Zekun Hao, Yu Liu, Hongwei Qin, Junjie Yan, Xiu Li, Xiaolin Hu
Date: 2017-06-29 00:00:00
Proceeding: CVPR 2017 7
Tasks: Face Detection

Convolutional neural network (CNN) based face detectors are inefficient in
handling faces of diverse scales. They rely on either fitting a large single
model to faces across a large scale range or multi-scale testing. Both are
computationally expensive. We propose Scale-aware Face Detector (SAFD) to
handle scale explicitly using CNN, and achieve better performance with less
computation cost. Prior to detection, an efficient CNN predicts the scale
distribution histogram of the faces. Then the scale histogram guides the
zoom-in and zoom-out of the image. Since the faces will be approximately in
uniform scale after zoom, they can be detected accurately even with much
smaller CNN. Actually, more than 99% of the faces in AFW can be covered with
less than two zooms per image. Extensive experiments on FDDB, MALF and AFW show
advantages of SAFD.