pwc-archive/papers-with-abstracts huggingface dataset:
$ν$-net: Deep Learning for Generalized Biventricular Cardiac Mass and Function Parameters
Authors: Hinrich B Winther, Christian Hundt, Bertil Schmidt, Christoph Czerner, Johann Bauersachs, Frank Wacker, Jens Vogel-Claussen
Date: 2017-06-14 00:00:00
Tasks: Anatomy, Image Segmentation, MRI segmentation, Segmentation, Semantic Segmentation

Background: Cardiac MRI derived biventricular mass and function parameters,
such as end-systolic volume (ESV), end-diastolic volume (EDV), ejection
fraction (EF), stroke volume (SV), and ventricular mass (VM) are clinically
well established. Image segmentation can be challenging and time-consuming, due
to the complex anatomy of the human heart.
  Objectives: This study introduces $\nu$-net (/nju:n$\varepsilon$t/) -- a deep
learning approach allowing for fully-automated high quality segmentation of
right (RV) and left ventricular (LV) endocardium and epicardium for extraction
of cardiac function parameters.
  Methods: A set consisting of 253 manually segmented cases has been used to
train a deep neural network. Subsequently, the network has been evaluated on 4
different multicenter data sets with a total of over 1000 cases.
  Results: For LV EF the intraclass correlation coefficient (ICC) is 98, 95,
and 80 % (95 %), and for RV EF 96, and 87 % (80 %) on the respective data sets
(human expert ICCs reported in parenthesis). The LV VM ICC is 95, and 94 % (84
%), and the RV VM ICC is 83, and 83 % (54 %). This study proposes a simple
adjustment procedure, allowing for the adaptation to distinct segmentation
philosophies. $\nu$-net exhibits state of-the-art performance in terms of dice
coefficient.
  Conclusions: Biventricular mass and function parameters can be determined
reliably in high quality by applying a deep neural network for cardiac MRI
segmentation, especially in the anatomically complex right ventricle. Adaption
to individual segmentation styles by applying a simple adjustment procedure is
viable, allowing for the processing of novel data without time-consuming
additional training.
Center of Gravity PSO for Partitioning Clustering
Authors: Shahira Shaaban Azab, Hesham Ahmed Hefny
Date: 2017-06-03 00:00:00
Tasks: Clustering, Position

This paper presents the local best model of PSO for partition-based
clustering. The proposed model gets rid off the drawbacks of gbest PSO for
clustering. The model uses a pre-specified number of clusters K. The LPOSC has
K neighborhoods. Each neighborhood represents one of the clusters. The goal of
the particles in each neighborhood is optimizing the position of the centroid
of the cluster. The performance of the proposed algorithms is measured using
adjusted rand index. The results is compared with k-means and global best model
of PSO.
Fine-grained human evaluation of neural versus phrase-based machine translation
Authors: Filip Klubička, Antonio Toral, Víctor M. Sánchez-Cartagena
Date: 2017-06-14 00:00:00
Tasks: Machine Translation, Translation

We compare three approaches to statistical machine translation (pure
phrase-based, factored phrase-based and neural) by performing a fine-grained
manual evaluation via error annotation of the systems' outputs. The error types
in our annotation are compliant with the multidimensional quality metrics
(MQM), and the annotation is performed by two annotators. Inter-annotator
agreement is high for such a task, and results show that the best performing
system (neural) reduces the errors produced by the worst system (phrase-based)
by 54%.
Alignment Distances on Systems of Bags
Authors: Alexander Sagel, Martin Kleinsteuber
Date: 2017-06-14 00:00:00
Tasks: Descriptive, Dictionary Learning, General Classification, Video Recognition

Recent research in image and video recognition indicates that many visual
processes can be thought of as being generated by a time-varying generative
model. A nearby descriptive model for visual processes is thus a statistical
distribution that varies over time. Specifically, modeling visual processes as
streams of histograms generated by a kernelized linear dynamic system turns out
to be efficient. We refer to such a model as a System of Bags. In this work, we
investigate Systems of Bags with special emphasis on dynamic scenes and dynamic
textures. Parameters of linear dynamic systems suffer from ambiguities. In
order to cope with these ambiguities in the kernelized setting, we develop a
kernelized version of the alignment distance. For its computation, we use a
Jacobi-type method and prove its convergence to a set of critical points. We
employ it as a dissimilarity measure on Systems of Bags. As such, it
outperforms other known dissimilarity measures for kernelized linear dynamic
systems, in particular the Martin Distance and the Maximum Singular Value
Distance, in every tested classification setting. A considerable margin can be
observed in settings, where classification is performed with respect to an
abstract mean of video sets. For this scenario, the presented approach can
outperform state-of-the-art techniques, such as Dynamic Fractal Spectrum or
Orthogonal Tensor Dictionary Learning.
Shape-Color Differential Moment Invariants under Affine Transformations
Authors: Hanlin Mo, Shirui Li, You Hao, Hua Li
Date: 2017-06-14 00:00:00
Tasks: General Classification, image-classification, Image Classification, Retrieval

We propose the general construction formula of shape-color primitives by
using partial differentials of each color channel in this paper. By using all
kinds of shape-color primitives, shape-color differential moment invariants can
be constructed very easily, which are invariant to the shape affine and color
affine transforms. 50 instances of SCDMIs are obtained finally. In experiments,
several commonly used color descriptors and SCDMIs are used in image
classification and retrieval of color images, respectively. By comparing the
experimental results, we find that SCDMIs get better results.
Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection
Authors: Zhe Wang, Yanxin Yin, Jianping Shi, Wei Fang, Hongsheng Li, Xiaogang Wang
Date: 2017-06-14 00:00:00
Tasks: Clustering, Diabetic Retinopathy Detection

We propose a convolution neural network based algorithm for simultaneously
diagnosing diabetic retinopathy and highlighting suspicious regions. Our
contributions are two folds: 1) a network termed Zoom-in-Net which mimics the
zoom-in process of a clinician to examine the retinal images. Trained with only
image-level supervisions, Zoomin-Net can generate attention maps which
highlight suspicious regions, and predicts the disease level accurately based
on both the whole image and its high resolution suspicious patches. 2) Only
four bounding boxes generated from the automatically learned attention maps are
enough to cover 80% of the lesions labeled by an experienced ophthalmologist,
which shows good localization ability of the attention maps. By clustering
features at high response locations on the attention maps, we discover
meaningful clusters which contain potential lesions in diabetic retinopathy.
Experiments show that our algorithm outperform the state-of-the-art methods on
two datasets, EyePACS and Messidor.
Predictive modelling of training loads and injury in Australian football
Authors: David L. Carey, Kok-Leong Ong, Rod Whiteley, Kay M. Crossley, Justin Crow, Meg E. Morris
Date: 2017-06-14 00:00:00
Tasks: Injury Prediction, regression

To investigate whether training load monitoring data could be used to predict
injuries in elite Australian football players, data were collected from elite
athletes over 3 seasons at an Australian football club. Loads were quantified
using GPS devices, accelerometers and player perceived exertion ratings.
Absolute and relative training load metrics were calculated for each player
each day (rolling average, exponentially weighted moving average, acute:chronic
workload ratio, monotony and strain). Injury prediction models (regularised
logistic regression, generalised estimating equations, random forests and
support vector machines) were built for non-contact, non-contact time-loss and
hamstring specific injuries using the first two seasons of data. Injury
predictions were generated for the third season and evaluated using the area
under the receiver operator characteristic (AUC). Predictive performance was
only marginally better than chance for models of non-contact and non-contact
time-loss injuries (AUC$<$0.65). The best performing model was a multivariate
logistic regression for hamstring injuries (best AUC=0.76). Learning curves
suggested logistic regression was underfitting the load-injury relationship and
that using a more complex model or increasing the amount of model building data
may lead to future improvements. Injury prediction models built using training
load data from a single club showed poor ability to predict injuries when
tested on previously unseen data, suggesting they are limited as a daily
decision tool for practitioners. Focusing the modelling approach on specific
injury types and increasing the amount of training data may lead to the
development of improved predictive models for injury prevention.
The Informativeness of K -Means for Learning Mixture Models
Authors: Zhaoqiang Liu, Vincent Y. F. Tan
Date: 2017-03-30 00:00:00
Tasks: Clustering, Dimensionality Reduction, Informativeness

The learning of mixture models can be viewed as a clustering problem. Indeed, given data samples independently generated from a mixture of distributions, we often would like to find the {\it correct target clustering} of the samples according to which component distribution they were generated from. For a clustering problem, practitioners often choose to use the simple $k$-means algorithm. $k$-means attempts to find an {\it optimal clustering} that minimizes the sum-of-squares distance between each point and its cluster center. In this paper, we consider fundamental (i.e., information-theoretic) limits of the solutions (clusterings) obtained by optimizing the sum-of-squares distance. In particular, we provide sufficient conditions for the closeness of any optimal clustering and the correct target clustering assuming that the data samples are generated from a mixture of spherical Gaussian distributions. We also generalize our results to log-concave distributions. Moreover, we show that under similar or even weaker conditions on the mixture model, any optimal clustering for the samples with reduced dimensionality is also close to the correct target clustering. These results provide intuition for the informativeness of $k$-means (with and without dimensionality reduction) as an algorithm for learning mixture models.
Wide Residual Networks
Authors: Sergey Zagoruyko, Nikos Komodakis
Date: 2016-05-23 00:00:00
Tasks: Image Classification

Deep residual networks were shown to be able to scale up to thousands of
layers and still have improving performance. However, each fraction of a
percent of improved accuracy costs nearly doubling the number of layers, and so
training very deep residual networks has a problem of diminishing feature
reuse, which makes these networks very slow to train. To tackle these problems,
in this paper we conduct a detailed experimental study on the architecture of
ResNet blocks, based on which we propose a novel architecture where we decrease
depth and increase width of residual networks. We call the resulting network
structures wide residual networks (WRNs) and show that these are far superior
over their commonly used thin and very deep counterparts. For example, we
demonstrate that even a simple 16-layer-deep wide residual network outperforms
in accuracy and efficiency all previous deep residual networks, including
thousand-layer-deep networks, achieving new state-of-the-art results on CIFAR,
SVHN, COCO, and significant improvements on ImageNet. Our code and models are
available at https://github.com/szagoruyko/wide-residual-networks
Transfer Learning for Neural Semantic Parsing
Authors: Xing Fan, Emilio Monti, Lambert Mathias, Markus Dreyer
Date: 2017-06-14 00:00:00
Proceeding: WS 2017 8
Tasks: Semantic Parsing, Transfer Learning

The goal of semantic parsing is to map natural language to a machine
interpretable meaning representation language (MRL). One of the constraints
that limits full exploration of deep learning technologies for semantic parsing
is the lack of sufficient annotation training data. In this paper, we propose
using sequence-to-sequence in a multi-task setup for semantic parsing with a
focus on transfer learning. We explore three multi-task architectures for
sequence-to-sequence modeling and compare their performance with an
independently trained model. Our experiments show that the multi-task setup
aids transfer learning from an auxiliary task with large labeled data to a
target task with smaller labeled data. We see absolute accuracy gains ranging
from 1.0% to 4.4% in our in- house data set, and we also see good gains ranging
from 2.5% to 7.0% on the ATIS semantic parsing tasks with syntactic and
semantic auxiliary tasks.
Hierarchical Gaussian Descriptors with Application to Person Re-Identification
Authors: Tetsu Matsukawa, Takahiro Okabe, Einoshin Suzuki, Yoichi Sato
Date: 2017-06-14 00:00:00
Tasks: image-classification, Image Classification, Person Re-Identification

Describing the color and textural information of a person image is one of the
most crucial aspects of person re-identification (re-id). In this paper, we
present novel meta-descriptors based on a hierarchical distribution of pixel
features. Although hierarchical covariance descriptors have been successfully
applied to image classification, the mean information of pixel features, which
is absent from the covariance, tends to be the major discriminative information
for person re-id. To solve this problem, we describe a local region in an image
via hierarchical Gaussian distribution in which both means and covariances are
included in their parameters. More specifically, the region is modeled as a set
of multiple Gaussian distributions in which each Gaussian represents the
appearance of a local patch. The characteristics of the set of Gaussians are
again described by another Gaussian distribution. In both steps, we embed the
parameters of the Gaussian into a point of Symmetric Positive Definite (SPD)
matrix manifold. By changing the way to handle mean information in this
embedding, we develop two hierarchical Gaussian descriptors. Additionally, we
develop feature norm normalization methods with the ability to alleviate the
biased trends that exist on the descriptors. The experimental results conducted
on five public datasets indicate that the proposed descriptors achieve
remarkably high performance on person re-id.
Teaching Compositionality to CNNs
Authors: Austin Stone, Huayan Wang, Michael Stark, Yi Liu, D. Scott Phoenix, Dileep George
Date: 2017-06-14 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object Recognition

Convolutional neural networks (CNNs) have shown great success in computer
vision, approaching human-level performance when trained for specific tasks via
application-specific loss functions. In this paper, we propose a method for
augmenting and training CNNs so that their learned features are compositional.
It encourages networks to form representations that disentangle objects from
their surroundings and from each other, thereby promoting better
generalization. Our method is agnostic to the specific details of the
underlying CNN to which it is applied and can in principle be used with any
CNN. As we show in our experiments, the learned representations lead to feature
activations that are more localized and improve performance over
non-compositional baselines in object recognition tasks.
Learning Structured Natural Language Representations for Semantic Parsing
Authors: Jianpeng Cheng, Siva Reddy, Vijay Saraswat, Mirella Lapata
Date: 2017-04-27 00:00:00
Proceeding: ACL 2017 7
Tasks: Semantic Parsing

We introduce a neural semantic parser that converts natural language
utterances to intermediate representations in the form of predicate-argument
structures, which are induced with a transition system and subsequently mapped
to target domains. The semantic parser is trained end-to-end using annotated
logical forms or their denotations. We obtain competitive results on various
datasets. The induced predicate-argument structures shed light on the types of
representations useful for semantic parsing and how these are different from
linguistically motivated ones.
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Authors: Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama
Date: 2017-02-28 00:00:00
Proceeding: ICML 2017 8
Tasks: Clustering, Data Augmentation, Unsupervised Image Classification

Learning discrete representations of data is a central machine learning task
because of the compactness of the representations and ease of interpretation.
The task includes clustering and hash learning as special cases. Deep neural
networks are promising to be used because they can model the non-linearity of
data and scale to large datasets. However, their model complexity is huge, and
therefore, we need to carefully regularize the networks in order to learn
useful representations that exhibit intended invariance for applications of
interest. To this end, we propose a method called Information Maximizing
Self-Augmented Training (IMSAT). In IMSAT, we use data augmentation to impose
the invariance on discrete representations. More specifically, we encourage the
predicted representations of augmented data points to be close to those of the
original data points in an end-to-end fashion. At the same time, we maximize
the information-theoretic dependency between data and their predicted discrete
representations. Extensive experiments on benchmark datasets show that IMSAT
produces state-of-the-art results for both clustering and unsupervised hash
learning.
Photo-realistic Facial Texture Transfer
Authors: Parneet Kaur, Hang Zhang, Kristin J. Dana
Date: 2017-06-14 00:00:00
Tasks: Style Transfer

Style transfer methods have achieved significant success in recent years with
the use of convolutional neural networks. However, many of these methods
concentrate on artistic style transfer with few constraints on the output image
appearance. We address the challenging problem of transferring face texture
from a style face image to a content face image in a photorealistic manner
without changing the identity of the original content image. Our framework for
face texture transfer (FaceTex) augments the prior work of MRF-CNN with a novel
facial semantic regularization that incorporates a face prior regularization
smoothly suppressing the changes around facial meso-structures (e.g eyes, nose
and mouth) and a facial structure loss function which implicitly preserves the
facial structure so that face texture can be transferred without changing the
original identity. We demonstrate results on face images and compare our
approach with recent state-of-the-art methods. Our results demonstrate superior
texture transfer because of the ability to maintain the identity of the
original face image.
Dueling Bandits With Weak Regret
Authors: Bangrui Chen, Peter I. Frazier
Date: 2017-06-14 00:00:00
Proceeding: ICML 2017 8

We consider online content recommendation with implicit feedback through
pairwise comparisons, formalized as the so-called dueling bandit problem. We
study the dueling bandit problem in the Condorcet winner setting, and consider
two notions of regret: the more well-studied strong regret, which is 0 only
when both arms pulled are the Condorcet winner; and the less well-studied weak
regret, which is 0 if either arm pulled is the Condorcet winner. We propose a
new algorithm for this problem, Winner Stays (WS), with variations for each
kind of regret: WS for weak regret (WS-W) has expected cumulative weak regret
that is $O(N^2)$, and $O(N\log(N))$ if arms have a total order; WS for strong
regret (WS-S) has expected cumulative strong regret of $O(N^2 + N \log(T))$,
and $O(N\log(N)+N\log(T))$ if arms have a total order. WS-W is the first
dueling bandit algorithm with weak regret that is constant in time. WS is
simple to compute, even for problems with many arms, and we demonstrate through
numerical experiments on simulated and real data that WS has significantly
smaller regret than existing algorithms in both the weak- and strong-regret
settings.
Faster Greedy MAP Inference for Determinantal Point Processes
Authors: Insu Han, Prabhanjan Kambadur, KyoungSoo Park, Jinwoo Shin
Date: 2017-03-09 00:00:00
Proceeding: ICML 2017 8
Tasks: Point Processes

Determinantal point processes (DPPs) are popular probabilistic models that
arise in many machine learning tasks, where distributions of diverse sets are
characterized by matrix determinants. In this paper, we develop fast algorithms
to find the most likely configuration (MAP) of large-scale DPPs, which is
NP-hard in general. Due to the submodular nature of the MAP objective, greedy
algorithms have been used with empirical success. Greedy implementations
require computation of log-determinants, matrix inverses or solving linear
systems at each iteration. We present faster implementations of the greedy
algorithms by utilizing the complementary benefits of two log-determinant
approximation schemes: (a) first-order expansions to the matrix log-determinant
function and (b) high-order expansions to the scalar log function with
stochastic trace estimators. In our experiments, our algorithms are orders of
magnitude faster than their competitors, while sacrificing marginal accuracy.
Stochastic Bouncy Particle Sampler
Authors: Ari Pakman, Dar Gilboa, David Carlson, Liam Paninski
Date: 2016-09-03 00:00:00
Proceeding: ICML 2017 8

We introduce a novel stochastic version of the non-reversible, rejection-free
Bouncy Particle Sampler (BPS), a Markov process whose sample trajectories are
piecewise linear. The algorithm is based on simulating first arrival times in a
doubly stochastic Poisson process using the thinning method, and allows
efficient sampling of Bayesian posteriors in big datasets. We prove that in the
BPS no bias is introduced by noisy evaluations of the log-likelihood gradient.
On the other hand, we argue that efficiency considerations favor a small,
controllable bias in the construction of the thinning proposals, in exchange
for faster mixing. We introduce a simple regression-based proposal intensity
for the thinning method that controls this trade-off. We illustrate the
algorithm in several examples in which it outperforms both unbiased, but slowly
mixing stochastic versions of BPS, as well as biased stochastic gradient-based
samplers.
Beam Search Strategies for Neural Machine Translation
Authors: Markus Freitag, Yaser Al-Onaizan
Date: 2017-02-06 00:00:00
Proceeding: WS 2017 8
Tasks: Decoder, Machine Translation, NMT, Sentence, Translation

The basic concept in Neural Machine Translation (NMT) is to train a large
Neural Network that maximizes the translation performance on a given parallel
corpus. NMT is then using a simple left-to-right beam-search decoder to
generate new translations that approximately maximize the trained conditional
probability. The current beam search strategy generates the target sentence
word by word from left-to- right while keeping a fixed amount of active
candidates at each time step. First, this simple search is less adaptive as it
also expands candidates whose scores are much worse than the current best.
Secondly, it does not expand hypotheses if they are not within the best scoring
candidates, even if their scores are close to the best one. The latter one can
be avoided by increasing the beam size until no performance improvement can be
observed. While you can reach better performance, this has the draw- back of a
slower decoding speed. In this paper, we concentrate on speeding up the decoder
by applying a more flexible beam search strategy whose candidate size may vary
at each time step depending on the candidate scores. We speed up the original
decoder by up to 43% for the two language pairs German-English and
Chinese-English without losing any translation quality.