pwc-archive/papers-with-abstracts huggingface dataset:
Deep Dynamic Boosted Forest
Authors: Haixin Wang, Xingzhang Ren, Jinan Sun, Wei Ye, Long Chen, Muzhi Yu, Shikun Zhang
Date: 2018-04-19 00:00:00
Tasks: Ensemble Learning

Random forest is widely exploited as an ensemble learning method. In many practical applications, however, there is still a significant challenge to learn from imbalanced data. To alleviate this limitation, we propose a deep dynamic boosted forest (DDBF), a novel ensemble algorithm that incorporates the notion of hard example mining into random forest. Specically, we propose to measure the quality of each leaf node of every decision tree in the random forest to determine hard examples. By iteratively training and then removing easy examples from training data, we evolve the random forest to focus on hard examples dynamically so as to balance the proportion of samples and learn decision boundaries better. Data can be cascaded through these random forests learned in each iteration in sequence to generate more accurate predictions. Our DDBF outperforms random forest on 5 UCI datasets, MNIST and SATIMAGE, and achieved state-of-the-art results compared to other deep models. Moreover, we show that DDBF is also a new way of sampling and can be very useful and efficient when learning from imbalanced data.
Thoracic Disease Identification and Localization with Limited Supervision
Authors: Zhe Li, Chong Wang, Mei Han, Yuan Xue, Wei Wei, Li-Jia Li, Li Fei-Fei
Date: 2017-11-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: General Classification

Accurate identification and localization of abnormalities from radiology
images play an integral part in clinical diagnosis and treatment planning.
Building a highly accurate prediction model for these tasks usually requires a
large number of images manually annotated with labels and finding sites of
abnormalities. In reality, however, such annotated data are expensive to
acquire, especially the ones with location annotations. We need methods that
can work well with only a small amount of location annotations. To address this
challenge, we present a unified approach that simultaneously performs disease
identification and localization through the same underlying model for all
images. We demonstrate that our approach can effectively leverage both class
information as well as limited location annotation, and significantly
outperforms the comparative reference baseline in both classification and
localization tasks.
Structure Aware SLAM using Quadrics and Planes
Authors: Mehdi Hosseinzadeh, Yasir Latif, Trung Pham, Niko Suenderhauf, Ian Reid
Date: 2018-04-24 00:00:00
Tasks: Camera Localization, Object, object-detection, Object Detection, Simultaneous Localization and Mapping

Simultaneous Localization And Mapping (SLAM) is a fundamental problem in
mobile robotics. While point-based SLAM methods provide accurate camera
localization, the generated maps lack semantic information. On the other hand,
state of the art object detection methods provide rich information about
entities present in the scene from a single image. This work marries the two
and proposes a method for representing generic objects as quadrics which allows
object detections to be seamlessly integrated in a SLAM framework. For scene
coverage, additional dominant planar structures are modeled as infinite planes.
Experiments show that the proposed points-planes-quadrics representation can
easily incorporate Manhattan and object affordance constraints, greatly
improving camera localization and leading to semantically meaningful maps. The
performance of our SLAM system is demonstrated in https://youtu.be/dR-rB9keF8M .
Structure Learning of $H$-colorings
Authors: Antonio Blanca, Zongchen Chen, Daniel Štefankovič, Eric Vigoda
Date: 2017-08-17 00:00:00

We study the structure learning problem for $H$-colorings, an important class
of Markov random fields that capture key combinatorial structures on graphs,
including proper colorings and independent sets, as well as spin systems from
statistical physics. The learning problem is as follows: for a fixed (and
known) constraint graph $H$ with $q$ colors and an unknown graph $G=(V,E)$ with
$n$ vertices, given uniformly random $H$-colorings of $G$, how many samples are
required to learn the edges of the unknown graph $G$? We give a
characterization of $H$ for which the problem is identifiable for every $G$,
i.e., we can learn $G$ with an infinite number of samples. We also show that
there are identifiable constraint graphs for which one cannot hope to learn
every graph $G$ efficiently.
  We focus particular attention on the case of proper vertex $q$-colorings of
graphs of maximum degree $d$ where intriguing connections to statistical
physics phase transitions appear. We prove that in the tree uniqueness region
(when $q>d$) the problem is identifiable and we can learn $G$ in ${\rm
poly}(d,q) \times O(n^2\log{n})$ time. In contrast for soft-constraint systems,
such as the Ising model, the best possible running time is exponential in $d$.
In the tree non-uniqueness region (when $q\leq d$) we prove that the problem is
not identifiable and thus $G$ cannot be learned. Moreover, when $q<d-\sqrt{d} +
\Theta(1)$ we prove that even learning an equivalent graph (any graph with the
same set of $H$-colorings) is computationally hard---sample complexity is
exponential in $n$ in the worst case. We further explore the connection between
the efficiency/hardness of the structure learning problem and the
uniqueness/non-uniqueness phase transition for general $H$-colorings and prove
that under the well-known Dobrushin uniqueness condition, we can learn $G$ in
${\rm poly}(d,q)\times O(n^2\log{n})$ time.
Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks
Authors: Matthew Sinclair, Christian F. Baumgartner, Jacqueline Matthew, Wenjia Bai, Juan Cerrolaza Martinez, Yuanwei Li, Sandra Smith, Caroline L. Knight, Bernhard Kainz, Jo Hajnal, Andrew P. King, Daniel Rueckert
Date: 2018-04-24 00:00:00
Tasks: GPU, Semantic Segmentation

Measurement of head biometrics from fetal ultrasonography images is of key
importance in monitoring the healthy development of fetuses. However, the
accurate measurement of relevant anatomical structures is subject to large
inter-observer variability in the clinic. To address this issue, an automated
method utilizing Fully Convolutional Networks (FCN) is proposed to determine
measurements of fetal head circumference (HC) and biparietal diameter (BPD). An
FCN was trained on approximately 2000 2D ultrasound images of the head with
annotations provided by 45 different sonographers during routine screening
examinations to perform semantic segmentation of the head. An ellipse is fitted
to the resulting segmentation contours to mimic the annotation typically
produced by a sonographer. The model's performance was compared with
inter-observer variability, where two experts manually annotated 100 test
images. Mean absolute model-expert error was slightly better than
inter-observer error for HC (1.99mm vs 2.16mm), and comparable for BPD (0.61mm
vs 0.59mm), as well as Dice coefficient (0.980 vs 0.980). Our results
demonstrate that the model performs at a level similar to a human expert, and
learns to produce accurate predictions from a large dataset annotated by many
sonographers. Additionally, measurements are generated in near real-time at
15fps on a GPU, which could speed up clinical workflow for both skilled and
trainee sonographers.
Word and Phrase Translation with word2vec
Authors: Stefan Jansen
Date: 2017-05-09 00:00:00
Tasks: Translation

Word and phrase tables are key inputs to machine translations, but costly to
produce. New unsupervised learning methods represent words and phrases in a
high-dimensional vector space, and these monolingual embeddings have been shown
to encode syntactic and semantic relationships between language elements. The
information captured by these embeddings can be exploited for bilingual
translation by learning a transformation matrix that allows matching relative
positions across two monolingual vector spaces. This method aims to identify
high-quality candidates for word and phrase translation more cost-effectively
from unlabeled data.
  This paper expands the scope of previous attempts of bilingual translation to
four languages (English, German, Spanish, and French). It shows how to process
the source data, train a neural network to learn the high-dimensional
embeddings for individual languages and expands the framework for testing their
quality beyond the English language. Furthermore, it shows how to learn
bilingual transformation matrices and obtain candidates for word and phrase
translation, and assess their quality.
Semi-supervised Content-based Detection of Misinformation via Tensor Embeddings
Authors: Gisel Bastidas Guacho, Sara Abdali, Neil Shah, Evangelos E. Papalexakis
Date: 2018-04-24 00:00:00
Tasks: Articles, Misinformation, Tensor Decomposition

Fake news may be intentionally created to promote economic, political and
social interests, and can lead to negative impacts on humans beliefs and
decisions. Hence, detection of fake news is an emerging problem that has become
extremely prevalent during the last few years. Most existing works on this
topic focus on manual feature extraction and supervised classification models
leveraging a large number of labeled (fake or real) articles. In contrast, we
focus on content-based detection of fake news articles, while assuming that we
have a small amount of labels, made available by manual fact-checkers or
automated sources. We argue this is a more realistic setting in the presence of
massive amounts of content, most of which cannot be easily factchecked. To that
end, we represent collections of news articles as multi-dimensional tensors,
leverage tensor decomposition to derive concise article embeddings that capture
spatial/contextual information about each news article, and use those
embeddings to create an article-by-article graph on which we propagate limited
labels. Results on three real-world datasets show that our method performs on
par or better than existing models that are fully supervised, in that we
achieve better detection accuracy using fewer labels. In particular, our
proposed method achieves 75.43% of accuracy using only 30% of labels of a
public dataset while an SVM-based classifier achieved 67.43%. Furthermore, our
method achieves 70.92% of accuracy in a large dataset using only 2% of labels.
End-to-end weakly-supervised semantic alignment
Authors: Ignacio Rocco, Relja Arandjelović, Josef Sivic
Date: 2017-12-19 00:00:00
Proceeding: CVPR 2018 6
Tasks: Semantic correspondence

We tackle the task of semantic alignment where the goal is to compute dense
semantic correspondence aligning two images depicting objects of the same
category. This is a challenging task due to large intra-class variation,
changes in viewpoint and background clutter. We present the following three
principal contributions. First, we develop a convolutional neural network
architecture for semantic alignment that is trainable in an end-to-end manner
from weak image-level supervision in the form of matching image pairs. The
outcome is that parameters are learnt from rich appearance variation present in
different but semantically related images without the need for tedious manual
annotation of correspondences at training time. Second, the main component of
this architecture is a differentiable soft inlier scoring module, inspired by
the RANSAC inlier scoring procedure, that computes the quality of the alignment
based on only geometrically consistent correspondences thereby reducing the
effect of background clutter. Third, we demonstrate that the proposed approach
achieves state-of-the-art performance on multiple standard benchmarks for
semantic alignment.
Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution
Authors: Thomas Elsken, Jan Hendrik Metzen, Frank Hutter
Date: 2018-04-24 00:00:00
Proceeding: ICLR 2019 5
Tasks: Neural Architecture Search

Neural Architecture Search aims at automatically finding neural architectures
that are competitive with architectures designed by human experts. While recent
approaches have achieved state-of-the-art predictive performance for image
recognition, they are problematic under resource constraints for two reasons:
(1)the neural architectures found are solely optimized for high predictive
performance, without penalizing excessive resource consumption, (2) most
architecture search methods require vast computational resources. We address
the first shortcoming by proposing LEMONADE, an evolutionary algorithm for
multi-objective architecture search that allows approximating the entire
Pareto-front of architectures under multiple objectives, such as predictive
performance and number of parameters, in a single run of the method. We address
the second shortcoming by proposing a Lamarckian inheritance mechanism for
LEMONADE which generates children networks that are warmstarted with the
predictive performance of their trained parents. This is accomplished by using
(approximate) network morphism operators for generating children. The
combination of these two contributions allows finding models that are on par or
even outperform both hand-crafted as well as automatically-designed networks.
Robust Video Content Alignment and Compensation for Clear Vision Through the Rain
Authors: Jie Chen, Cheen-Hau Tan, Junhui Hou, Lap-Pui Chau, He Li
Date: 2018-04-24 00:00:00
Tasks: Rain Removal

Outdoor vision-based systems suffer from atmospheric turbulences, and rain is
one of the worst factors for vision degradation. Current rain removal methods
show limitations either for complex dynamic scenes, or under torrential rain
with opaque occlusions. We propose a novel derain framework which applies
superpixel (SP) segmentation to decompose the scene into depth consistent
units. Alignment of scene contents are done at the SP level, which proves to be
robust against rain occlusion interferences and fast camera motion. Two
alignment output tensors, i.e., optimal temporal match tensor and sorted
spatial-temporal match tensor, provide informative clues for the location of
rain streaks and the occluded background contents. Different classical and
novel methods such as Robust Principle Component Analysis and Convolutional
Neural Networks are applied and compared for their respective advantages in
efficiently exploiting the rich spatial-temporal features provided by the two
tensors. Extensive evaluations show that advantage of up to 5dB is achieved on
the scene restoration PSNR over state-of-the-art methods, and the advantage is
especially obvious with highly complex and dynamic scenes. Visual evaluations
show that the proposed framework is not only able to suppress heavy and opaque
occluding rain streaks, but also large semi-transparent regional fluctuations
and distortions.
ISIS at its apogee: the Arabic discourse on Twitter and what we can learn from that about ISIS support and Foreign Fighters
Authors: A. Ceron, L. Curini, S. M. Iacus
Date: 2018-03-14 00:00:00

We analyze 26.2 million comments published in Arabic language on Twitter,
from July 2014 to January 2015, when ISIS' strength reached its peak and the
group was prominently expanding the territorial area under its control. By
doing that, we are able to measure the share of support and aversion toward the
Islamic State within the online Arab communities. We then investigate two
specific topics. First, by exploiting the time-granularity of the tweets, we
link the opinions with daily events to understand the main determinants of the
changing trend in support toward ISIS. Second, by taking advantage of the
geographical locations of tweets, we explore the relationship between online
opinions across countries and the number of foreign fighters joining ISIS.
Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
Authors: Qingsong Yang, Pingkun Yan, Yanbo Zhang, Hengyong Yu, Yongyi Shi, Xuanqin Mou, Mannudeep K. Kalra, Ge Wang
Date: 2017-08-03 00:00:00
Tasks: Denoising, Generative Adversarial Network, Image Denoising

In this paper, we introduce a new CT image denoising method based on the
generative adversarial network (GAN) with Wasserstein distance and perceptual
similarity. The Wasserstein distance is a key concept of the optimal transform
theory, and promises to improve the performance of the GAN. The perceptual loss
compares the perceptual features of a denoised output against those of the
ground truth in an established feature space, while the GAN helps migrate the
data noise distribution from strong to weak. Therefore, our proposed method
transfers our knowledge of visual perception to the image denoising task, is
capable of not only reducing the image noise level but also keeping the
critical information at the same time. Promising results have been obtained in
our experiments with clinical CT images.
Unsupervised Neural Machine Translation with Weight Sharing
Authors: Zhen Yang, Wei Chen, Feng Wang, Bo Xu
Date: 2018-04-24 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, NMT, Sentence, Translation

Unsupervised neural machine translation (NMT) is a recently proposed approach
for machine translation which aims to train the model without using any labeled
data. The models proposed for unsupervised NMT often use only one shared
encoder to map the pairs of sentences from different languages to a
shared-latent space, which is weak in keeping the unique and internal
characteristics of each language, such as the style, terminology, and sentence
structure. To address this issue, we introduce an extension by utilizing two
independent encoders but sharing some partial weights which are responsible for
extracting high-level representations of the input sentences. Besides, two
different generative adversarial networks (GANs), namely the local GAN and
global GAN, are proposed to enhance the cross-language translation. With this
new approach, we achieve significant improvements on English-German,
English-French and Chinese-to-English translation tasks.
Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data
Authors: Sina Keller, Felix M. Riese, Johanna Stötzer, Philipp M. Maier, Stefan Hinz
Date: 2018-04-24 00:00:00
Tasks: BIG-bench Machine Learning, feature selection, regression

In this paper, we investigate the potential of estimating the soil-moisture
content based on VNIR hyperspectral data combined with LWIR data. Measurements
from a multi-sensor field campaign represent the benchmark dataset which
contains measured hyperspectral, LWIR, and soil-moisture data conducted on
grassland site. We introduce a regression framework with three steps consisting
of feature selection, preprocessing, and well-chosen regression models. The
latter are mainly supervised machine learning models. An exception are the
self-organizing maps which combine unsupervised and supervised learning. We
analyze the impact of the distinct preprocessing methods on the regression
results. Of all regression models, the extremely randomized trees model without
preprocessing provides the best estimation performance. Our results reveal the
potential of the respective regression framework combined with the VNIR
hyperspectral data to estimate soil moisture measured under real-world
conditions. In conclusion, the results of this paper provide a basis for
further improvements in different research directions.
Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization
Authors: Aryan Mokhtari, Hamed Hassani, Amin Karbasi
Date: 2018-04-24 00:00:00
Tasks: Stochastic Optimization

This paper considers stochastic optimization problems for a large class of
objective functions, including convex and continuous submodular. Stochastic
proximal gradient methods have been widely used to solve such problems;
however, their applicability remains limited when the problem dimension is
large and the projection onto a convex set is costly. Instead, stochastic
conditional gradient methods are proposed as an alternative solution relying on
(i) Approximating gradients via a simple averaging technique requiring a single
stochastic gradient evaluation per iteration; (ii) Solving a linear program to
compute the descent/ascent direction. The averaging technique reduces the noise
of gradient approximations as time progresses, and replacing projection step in
proximal methods by a linear program lowers the computational complexity of
each iteration. We show that under convexity and smoothness assumptions, our
proposed method converges to the optimal objective function value at a
sublinear rate of $O(1/t^{1/3})$. Further, for a monotone and continuous
DR-submodular function and subject to a general convex body constraint, we
prove that our proposed method achieves a $((1-1/e)OPT-\eps)$ guarantee with
$O(1/\eps^3)$ stochastic gradient computations. This guarantee matches the
known hardness results and closes the gap between deterministic and stochastic
continuous submodular maximization. Additionally, we obtain $((1/e)OPT -\eps)$
guarantee after using $O(1/\eps^3)$ stochastic gradients for the case that the
objective function is continuous DR-submodular but non-monotone and the
constraint set is down-closed. By using stochastic continuous optimization as
an interface, we provide the first $(1-1/e)$ tight approximation guarantee for
maximizing a monotone but stochastic submodular set function subject to a
matroid constraint and $(1/e)$ approximation guarantee for the non-monotone
case.
Complex energy landscapes in spiked-tensor and simple glassy models: ruggedness, arrangements of local minima and phase transitions
Authors: Valentina Ros, Gerard Ben Arous, Giulio Biroli, Chiara Cammarota
Date: 2018-04-08 00:00:00

We study rough high-dimensional landscapes in which an increasingly stronger
preference for a given configuration emerges. Such energy landscapes arise in
glass physics and inference. In particular we focus on random Gaussian
functions, and on the spiked-tensor model and generalizations. We thoroughly
analyze the statistical properties of the corresponding landscapes and
characterize the associated geometrical phase transitions. In order to perform
our study, we develop a framework based on the Kac-Rice method that allows to
compute the complexity of the landscape, i.e. the logarithm of the typical
number of stationary points and their Hessian. This approach generalizes the
one used to compute rigorously the annealed complexity of mean-field glass
models. We discuss its advantages with respect to previous frameworks, in
particular the thermodynamical replica method which is shown to lead to
partially incorrect predictions.
Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications
Authors: Guy Hadash, Einat Kermany, Boaz Carmeli, Ofer Lavi, George Kour, Alon Jacovi
Date: 2018-04-24 00:00:00

Existing applications include a huge amount of knowledge that is out of reach
for deep neural networks. This paper presents a novel approach for integrating
calls to existing applications into deep learning architectures. Using this
approach, we estimate each application's functionality with an estimator, which
is implemented as a deep neural network (DNN). The estimator is then embedded
into a base network that we direct into complying with the application's
interface during an end-to-end optimization process. At inference time, we
replace each estimator with its existing application counterpart and let the
base network solve the task by interacting with the existing application. Using
this 'Estimate and Replace' method, we were able to train a DNN end-to-end with
less data and outperformed a matching DNN that did not interact with the
external application.