pwc-archive/papers-with-abstracts huggingface dataset:
Signal reconstruction via operator guiding
Authors: Andrew Knyazev, Alexander Malyshev
Date: 2017-05-09 00:00:00
Tasks: Super-Resolution

Signal reconstruction from a sample using an orthogonal projector onto a
guiding subspace is theoretically well justified, but may be difficult to
practically implement. We propose more general guiding operators, which
increase signal components in the guiding subspace relative to those in a
complementary subspace, e.g., iterative low-pass edge-preserving filters for
super-resolution of images. Two examples of super-resolution illustrate our
technology: a no-flash RGB photo guided using a high resolution flash RGB
photo, and a depth image guided using a high resolution RGB photo.
Influence Function and Robust Variant of Kernel Canonical Correlation Analysis
Authors: Md. Ashad Alam, Kenji Fukumizu, Yu-Ping Wang
Date: 2017-05-09 00:00:00

Many unsupervised kernel methods rely on the estimation of the kernel
covariance operator (kernel CO) or kernel cross-covariance operator (kernel
CCO). Both kernel CO and kernel CCO are sensitive to contaminated data, even
when bounded positive definite kernels are used. To the best of our knowledge,
there are few well-founded robust kernel methods for statistical unsupervised
learning. In addition, while the influence function (IF) of an estimator can
characterize its robustness, asymptotic properties and standard error, the IF
of a standard kernel canonical correlation analysis (standard kernel CCA) has
not been derived yet. To fill this gap, we first propose a robust kernel
covariance operator (robust kernel CO) and a robust kernel cross-covariance
operator (robust kernel CCO) based on a generalized loss function instead of
the quadratic loss function. Second, we derive the IF for robust kernel CCO and
standard kernel CCA. Using the IF of the standard kernel CCA, we can detect
influential observations from two sets of data. Finally, we propose a method
based on the robust kernel CO and the robust kernel CCO, called {\bf robust
kernel CCA}, which is less sensitive to noise than the standard kernel CCA. The
introduced principles can also be applied to many other kernel methods
involving kernel CO or kernel CCO. Our experiments on synthesized data and
imaging genetics analysis demonstrate that the proposed IF of standard kernel
CCA can identify outliers. It is also seen that the proposed robust kernel CCA
method performs better for ideal and contaminated data than the standard kernel
CCA.
Predicting Audience's Laughter Using Convolutional Neural Network
Authors: Lei Chen, Chong MIn Lee
Date: 2017-02-08 00:00:00
Tasks: General Classification, text-classification, Text Classification

For the purpose of automatically evaluating speakers' humor usage, we build a
presentation corpus containing humorous utterances based on TED talks. Compared
to previous data resources supporting humor recognition research, ours has
several advantages, including (a) both positive and negative instances coming
from a homogeneous data set, (b) containing a large number of speakers, and (c)
being open. Focusing on using lexical cues for humor recognition, we
systematically compare a newly emerging text classification method based on
Convolutional Neural Networks (CNNs) with a well-established conventional
method using linguistic knowledge. The advantages of the CNN method are both
getting higher detection accuracies and being able to learn essential features
automatically.
Deep Projective 3D Semantic Segmentation
Authors: Felix Järemo Lawin, Martin Danelljan, Patrik Tosteberg, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg
Date: 2017-05-09 00:00:00
Tasks: Segmentation, Semantic Segmentation

Semantic segmentation of 3D point clouds is a challenging problem with
numerous real-world applications. While deep learning has revolutionized the
field of image semantic segmentation, its impact on point cloud data has been
limited so far. Recent attempts, based on 3D deep learning approaches
(3D-CNNs), have achieved below-expected results. Such methods require
voxelizations of the underlying point cloud data, leading to decreased spatial
resolution and increased memory consumption. Additionally, 3D-CNNs greatly
suffer from the limited availability of annotated datasets.
  In this paper, we propose an alternative framework that avoids the
limitations of 3D-CNNs. Instead of directly solving the problem in 3D, we first
project the point cloud onto a set of synthetic 2D-images. These images are
then used as input to a 2D-CNN, designed for semantic segmentation. Finally,
the obtained prediction scores are re-projected to the point cloud to obtain
the segmentation results. We further investigate the impact of multiple
modalities, such as color, depth and surface normals, in a multi-stream network
architecture. Experiments are performed on the recent Semantic3D dataset. Our
approach sets a new state-of-the-art by achieving a relative gain of 7.9 %,
compared to the previous best approach.
Compressive Estimation of a Stochastic Process with Unknown Autocorrelation Function
Authors: Mahdi Barzegar Khalilsarai, Saeid Haghighatshoar, Giuseppe Caire, Gerhard Wunder
Date: 2017-05-09 00:00:00
Tasks: Quantization

In this paper, we study the prediction of a circularly symmetric zero-mean
stationary Gaussian process from a window of observations consisting of
finitely many samples. This is a prevalent problem in a wide range of
applications in communication theory and signal processing. Due to
stationarity, when the autocorrelation function or equivalently the power
spectral density (PSD) of the process is available, the Minimum Mean Squared
Error (MMSE) predictor is readily obtained. In particular, it is given by a
linear operator that depends on autocorrelation of the process as well as the
noise power in the observed samples. The prediction becomes, however, quite
challenging when the PSD of the process is unknown. In this paper, we propose a
blind predictor that does not require the a priori knowledge of the PSD of the
process and compare its performance with that of an MMSE predictor that has a
full knowledge of the PSD. To design such a blind predictor, we use the random
spectral representation of a stationary Gaussian process. We apply the
well-known atomic-norm minimization technique to the observed samples to obtain
a discrete quantization of the underlying random spectrum, which we use to
predict the process. Our simulation results show that this estimator has a good
performance comparable with that of the MMSE estimator.
Learning Deep Networks from Noisy Labels with Dropout Regularization
Authors: Ishan Jindal, Matthew Nokleby, Xue-wen Chen
Date: 2017-05-09 00:00:00

Large datasets often have unreliable labels-such as those obtained from
Amazon's Mechanical Turk or social media platforms-and classifiers trained on
mislabeled datasets often exhibit poor performance. We present a simple,
effective technique for accounting for label noise when training deep neural
networks. We augment a standard deep network with a softmax layer that models
the label noise statistics. Then, we train the deep network and noise model
jointly via end-to-end stochastic gradient descent on the (perhaps mislabeled)
dataset. The augmented model is overdetermined, so in order to encourage the
learning of a non-trivial noise model, we apply dropout regularization to the
weights of the noise model during training. Numerical experiments on noisy
versions of the CIFAR-10 and MNIST datasets show that the proposed dropout
technique outperforms state-of-the-art methods.
People on Media: Jointly Identifying Credible News and Trustworthy Citizen Journalists in Online Communities
Authors: Subhabrata Mukherjee, Gerhard Weikum
Date: 2017-05-07 00:00:00
Tasks: Articles, Fairness

Media seems to have become more partisan, often providing a biased coverage
of news catering to the interest of specific groups. It is therefore essential
to identify credible information content that provides an objective narrative
of an event. News communities such as digg, reddit, or newstrust offer
recommendations, reviews, quality ratings, and further insights on journalistic
works. However, there is a complex interaction between different factors in
such online communities: fairness and style of reporting, language clarity and
objectivity, topical perspectives (like political viewpoint), expertise and
bias of community members, and more. This paper presents a model to
systematically analyze the different interactions in a news community between
users, news, and sources. We develop a probabilistic graphical model that
leverages this joint interaction to identify 1) highly credible news articles,
2) trustworthy news sources, and 3) expert users who perform the role of
"citizen journalists" in the community. Our method extends CRF models to
incorporate real-valued ratings, as some communities have very fine-grained
scales that cannot be easily discretized without losing information. To the
best of our knowledge, this paper is the first full-fledged analysis of
credibility, trust, and expertise in news communities.
Learning Where to Attend Like a Human Driver
Authors: Andrea Palazzi, Francesco Solera, Simone Calderara, Stefano Alletto, Rita Cucchiara
Date: 2016-11-24 00:00:00

Despite the advent of autonomous cars, it's likely - at least in the near
future - that human attention will still maintain a central role as a guarantee
in terms of legal responsibility during the driving task. In this paper we
study the dynamics of the driver's gaze and use it as a proxy to understand
related attentional mechanisms. First, we build our analysis upon two
questions: where and what the driver is looking at? Second, we model the
driver's gaze by training a coarse-to-fine convolutional network on short
sequences extracted from the DR(eye)VE dataset. Experimental comparison against
different baselines reveal that the driver's gaze can indeed be learnt to some
extent, despite i) being highly subjective and ii) having only one driver's
gaze available for each sequence due to the irreproducibility of the scene.
Eventually, we advocate for a new assisted driving paradigm which suggests to
the driver, with no intervention, where she should focus her attention.
Logical Parsing from Natural Language Based on a Neural Translation Model
Authors: Liang Li, Pengyu Li, Yifan Liu, Tao Wan, Zengchang Qin
Date: 2017-05-09 00:00:00
Tasks: Question Answering, Semantic Parsing, Translation

Semantic parsing has emerged as a significant and powerful paradigm for
natural language interface and question answering systems. Traditional methods
of building a semantic parser rely on high-quality lexicons, hand-crafted
grammars and linguistic features which are limited by applied domain or
representation. In this paper, we propose a general approach to learn from
denotations based on Seq2Seq model augmented with attention mechanism. We
encode input sequence into vectors and use dynamic programming to infer
candidate logical forms. We utilize the fact that similar utterances should
have similar logical forms to help reduce the searching space. Under our
learning policy, the Seq2Seq model can learn mappings gradually with noises.
Curriculum learning is adopted to make the learning smoother. We test our
method on the arithmetic domain which shows our model can successfully infer
the correct logical forms and learn the word meanings, compositionality and
operation orders simultaneously.
Cell Tracking via Proposal Generation and Selection
Authors: Saad Ullah Akram, Juho Kannala, Lauri Eklund, Janne Heikkilä
Date: 2017-05-09 00:00:00
Tasks: Cell Detection, Cell Tracking

Microscopy imaging plays a vital role in understanding many biological
processes in development and disease. The recent advances in automation of
microscopes and development of methods and markers for live cell imaging has
led to rapid growth in the amount of image data being captured. To efficiently
and reliably extract useful insights from these captured sequences, automated
cell tracking is essential. This is a challenging problem due to large
variation in the appearance and shapes of cells depending on many factors
including imaging methodology, biological characteristics of cells, cell matrix
composition, labeling methodology, etc. Often cell tracking methods require a
sequence-specific segmentation method and manual tuning of many tracking
parameters, which limits their applicability to sequences other than those they
are designed for. In this paper, we propose 1) a deep learning based cell
proposal method, which proposes candidates for cells along with their scores,
and 2) a cell tracking method, which links proposals in adjacent frames in a
graphical model using edges representing different cellular events and poses
joint cell detection and tracking as the selection of a subset of cell and edge
proposals. Our method is completely automated and given enough training data
can be applied to a wide variety of microscopy sequences. We evaluate our
method on multiple fluorescence and phase contrast microscopy sequences
containing cells of various shapes and appearances from ISBI cell tracking
challenge, and show that our method outperforms existing cell tracking methods.
  Code is available at: https://github.com/SaadUllahAkram/CellTracker
A note on the uniqueness of models in social abstract argumentation
Authors: Leila Amgoud, Elise Bonzon, Marco Correia, Jorge Cruz, Jérôme Delobelle, Sébastien Konieczny, João Leite, Alexis Martin, Nicolas Maudet, Srdjan Vesic
Date: 2017-05-09 00:00:00
Tasks: Abstract Argumentation

Social abstract argumentation is a principled way to assign values to
conflicting (weighted) arguments. In this note we discuss the important
property of the uniqueness of the model.
Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation
Authors: Zhiyuan Shi, Timothy M. Hospedales, Tao Xiang
Date: 2017-05-09 00:00:00
Tasks: Object

We address the problem of localisation of objects as bounding boxes in images
with weak labels. This weakly supervised object localisation problem has been
tackled in the past using discriminative models where each object class is
localised independently from other classes. We propose a novel framework based
on Bayesian joint topic modelling. Our framework has three distinctive
advantages over previous works: (1) All object classes and image backgrounds
are modelled jointly together in a single generative model so that "explaining
away" inference can resolve ambiguity and lead to better learning and
localisation. (2) The Bayesian formulation of the model enables easy
integration of prior knowledge about object appearance to compensate for
limited supervision. (3) Our model can be learned with a mixture of weakly
labelled and unlabelled data, allowing the large volume of unlabelled images on
the Internet to be exploited for learning. Extensive experiments on the
challenging VOC dataset demonstrate that our approach outperforms the
state-of-the-art competitors.
Skin lesion detection based on an ensemble of deep convolutional neural network
Authors: Balazs Harangi
Date: 2017-05-09 00:00:00
Tasks: General Classification, Lesion Detection

Skin cancer is a major public health problem, with over 5 million newly
diagnosed cases in the United States each year. Melanoma is the deadliest form
of skin cancer, responsible for over 9,000 deaths each year. In this paper, we
propose an ensemble of deep convolutional neural networks to classify
dermoscopy images into three classes. To achieve the highest classification
accuracy, we fuse the outputs of the softmax layers of four different neural
architectures. For aggregation, we consider the individual accuracies of the
networks weighted by the confidence values provided by their final softmax
layers. This fusion-based approach outperformed all the individual neural
networks regarding classification accuracy.
Making Contextual Decisions with Low Technical Debt
Authors: Alekh Agarwal, Sarah Bird, Markus Cozowicz, Luong Hoang, John Langford, Stephen Lee, Jiaji Li, Dan Melamed, Gal Oshri, Oswaldo Ribas, Siddhartha Sen, Alex Slivkins
Date: 2016-06-13 00:00:00
Tasks: Multi-Armed Bandits

Applications and systems are constantly faced with decisions that require
picking from a set of actions based on contextual information.
Reinforcement-based learning algorithms such as contextual bandits can be very
effective in these settings, but applying them in practice is fraught with
technical debt, and no general system exists that supports them completely. We
address this and create the first general system for contextual learning,
called the Decision Service.
  Existing systems often suffer from technical debt that arises from issues
like incorrect data collection and weak debuggability, issues we systematically
address through our ML methodology and system abstractions. The Decision
Service enables all aspects of contextual bandit learning using four system
abstractions which connect together in a loop: explore (the decision space),
log, learn, and deploy. Notably, our new explore and log abstractions ensure
the system produces correct, unbiased data, which our learner uses for online
learning and to enable real-time safeguards, all in a fully reproducible
manner.
  The Decision Service has a simple user interface and works with a variety of
applications: we present two live production deployments for content
recommendation that achieved click-through improvements of 25-30%, another with
18% revenue lift in the landing page, and ongoing applications in tech support
and machine failure handling. The service makes real-time decisions and learns
continuously and scalably, while significantly lowering technical debt.
An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits
Authors: Yevgeny Seldin, Gábor Lugosi
Date: 2017-02-20 00:00:00

We present a new strategy for gap estimation in randomized algorithms for
multiarmed bandits and combine it with the EXP3++ algorithm of Seldin and
Slivkins (2014). In the stochastic regime the strategy reduces dependence of
regret on a time horizon from $(\ln t)^3$ to $(\ln t)^2$ and eliminates an
additive factor of order $\Delta e^{1/\Delta^2}$, where $\Delta$ is the minimal
gap of a problem instance. In the adversarial regime regret guarantee remains
unchanged.
Adaptive Regularization of Some Inverse Problems in Image Analysis
Authors: Byung-Woo Hong, Ja-Keoung Koo, Martin Burger, Stefano Soatto
Date: 2017-05-09 00:00:00
Tasks: Denoising, Motion Estimation

We present an adaptive regularization scheme for optimizing composite energy
functionals arising in image analysis problems. The scheme automatically trades
off data fidelity and regularization depending on the current data fit during
the iterative optimization, so that regularization is strongest initially, and
wanes as data fidelity improves, with the weight of the regularizer being
minimized at convergence. We also introduce the use of a Huber loss function in
both data fidelity and regularization terms, and present an efficient convex
optimization algorithm based on the alternating direction method of multipliers
(ADMM) using the equivalent relation between the Huber function and the
proximal operator of the one-norm. We illustrate and validate our adaptive
Huber-Huber model on synthetic and real images in segmentation, motion
estimation, and denoising problems.
Going Deeper with Contextual CNN for Hyperspectral Image Classification
Authors: Hyungtae Lee, Heesung Kwon
Date: 2016-04-12 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

In this paper, we describe a novel deep convolutional neural network (CNN)
that is deeper and wider than other existing deep networks for hyperspectral
image classification. Unlike current state-of-the-art approaches in CNN-based
hyperspectral image classification, the proposed network, called contextual
deep CNN, can optimally explore local contextual interactions by jointly
exploiting local spatio-spectral relationships of neighboring individual pixel
vectors. The joint exploitation of the spatio-spectral information is achieved
by a multi-scale convolutional filter bank used as an initial component of the
proposed CNN pipeline. The initial spatial and spectral feature maps obtained
from the multi-scale filter bank are then combined together to form a joint
spatio-spectral feature map. The joint feature map representing rich spectral
and spatial properties of the hyperspectral image is then fed through a fully
convolutional network that eventually predicts the corresponding label of each
pixel vector. The proposed approach is tested on three benchmark datasets: the
Indian Pines dataset, the Salinas dataset and the University of Pavia dataset.
Performance comparison shows enhanced classification performance of the
proposed approach over the current state-of-the-art on the three datasets.
Soft Weight-Sharing for Neural Network Compression
Authors: Karen Ullrich, Edward Meeds, Max Welling
Date: 2017-02-13 00:00:00
Tasks: Neural Network Compression, Quantization

The success of deep learning in numerous application domains created the de-
sire to run and train them on mobile devices. This however, conflicts with
their computationally, memory and energy intense nature, leading to a growing
interest in compression. Recent work by Han et al. (2015a) propose a pipeline
that involves retraining, pruning and quantization of neural network weights,
obtaining state-of-the-art compression rates. In this paper, we show that
competitive compression rates can be achieved by using a version of soft
weight-sharing (Nowlan & Hinton, 1992). Our method achieves both quantization
and pruning in one simple (re-)training procedure. This point of view also
exposes the relation between compression and the minimum description length
(MDL) principle.
Model Complexity-Accuracy Trade-off for a Convolutional Neural Network
Authors: Atul Dhingra
Date: 2017-05-09 00:00:00

Convolutional Neural Networks(CNN) has had a great success in the recent
past, because of the advent of faster GPUs and memory access. CNNs are really
powerful as they learn the features from data in layers such that they exhibit
the structure of the V-1 features of the human brain. A huge bottleneck, in
this case, is that CNNs are very large and have a very high memory footprint,
and hence they cannot be employed on devices with limited storage such as
mobile phone, IoT etc. In this work, we study the model complexity versus
accuracy trade-off on MNSIT dataset, and give a concrete framework for handling
such a problem, given the worst case accuracy that a system can tolerate. In
our work, we reduce the model complexity by 236 times, and memory footprint by
19.5 times compared to the base model while achieving worst case accuracy
threshold.
Making up for the deficit in a marathon run
Authors: Iztok Fister Jr., Dušan Fister, Suash Deb, Uroš Mlakar, Janez Brest, Iztok Fister
Date: 2017-05-09 00:00:00

To predict the final result of an athlete in a marathon run thoroughly is the
eternal desire of each trainer. Usually, the achieved result is weaker than the
predicted one due to the objective (e.g., environmental conditions) as well as
subjective factors (e.g., athlete's malaise). Therefore, making up for the
deficit between predicted and achieved results is the main ingredient of the
analysis performed by trainers after the competition. In the analysis, they
search for parts of a marathon course where the athlete lost time. This paper
proposes an automatic making up for the deficit by using a Differential
Evolution algorithm. In this case study, the results that were obtained by a
wearable sports-watch by an athlete in a real marathon are analyzed. The first
experiments with Differential Evolution show the possibility of using this
method in the future.