pwc-archive/papers-with-abstracts huggingface dataset:
Failure Prediction for Autonomous Driving
Authors: Simon Hecker, Dengxin Dai, Luc van Gool
Date: 2018-05-04 00:00:00
Tasks: Autonomous Driving, Prediction

The primary focus of autonomous driving research is to improve driving
accuracy. While great progress has been made, state-of-the-art algorithms still
fail at times. Such failures may have catastrophic consequences. It therefore
is important that automated cars foresee problems ahead as early as possible.
This is also of paramount importance if the driver will be asked to take over.
We conjecture that failures do not occur randomly. For instance, driving models
may fail more likely at places with heavy traffic, at complex intersections,
and/or under adverse weather/illumination conditions. This work presents a
method to learn to predict the occurrence of these failures, i.e. to assess how
difficult a scene is to a given driving model and to possibly give the human
driver an early headsup. A camera-based driving model is developed and trained
over real driving datasets. The discrepancies between the model's predictions
and the human `ground-truth' maneuvers were then recorded, to yield the
`failure' scores. Experimental results show that the failure score can indeed
be learned and predicted. Thus, our prediction method is able to improve the
overall safety of an automated driving model by alerting the human driver
timely, leading to better human-vehicle collaborative driving.
Unsupervised learning for concept detection in medical images: a comparative analysis
Authors: Eduardo Pinho, Carlos Costa
Date: 2018-05-04 00:00:00
Tasks: Information Retrieval, Representation Learning, Retrieval

As digital medical imaging becomes more prevalent and archives increase in
size, representation learning exposes an interesting opportunity for enhanced
medical decision support systems. On the other hand, medical imaging data is
often scarce and short on annotations. In this paper, we present an assessment
of unsupervised feature learning approaches for images in the biomedical
literature, which can be applied to automatic biomedical concept detection. Six
unsupervised representation learning methods were built, including traditional
bags of visual words, autoencoders, and generative adversarial networks. Each
model was trained, and their respective feature space evaluated using images
from the ImageCLEF 2017 concept detection task. We conclude that it is possible
to obtain more powerful representations with modern deep learning approaches,
in contrast with previously popular computer vision methods. Although
generative adversarial networks can provide good results, they are harder to
succeed in highly varied data sets. The possibility of semi-supervised
learning, as well as their use in medical information retrieval problems, are
the next steps to be strongly considered.
Dynamic Control Flow in Large-Scale Machine Learning
Authors: Yuan Yu, Martín Abadi, Paul Barham, Eugene Brevdo, Mike Burrows, Andy Davis, Jeff Dean, Sanjay Ghemawat, Tim Harley, Peter Hawkins, Michael Isard, Manjunath Kudlur, Rajat Monga, Derek Murray, Xiaoqiang Zheng
Date: 2018-05-04 00:00:00
Tasks: BIG-bench Machine Learning, Reinforcement Learning

Many recent machine learning models rely on fine-grained dynamic control flow
for training and inference. In particular, models based on recurrent neural
networks and on reinforcement learning depend on recurrence relations,
data-dependent conditional execution, and other features that call for dynamic
control flow. These applications benefit from the ability to make rapid
control-flow decisions across a set of computing devices in a distributed
system. For performance, scalability, and expressiveness, a machine learning
system must support dynamic control flow in distributed and heterogeneous
environments.
  This paper presents a programming model for distributed machine learning that
supports dynamic control flow. We describe the design of the programming model,
and its implementation in TensorFlow, a distributed machine learning system.
Our approach extends the use of dataflow graphs to represent machine learning
models, offering several distinctive features. First, the branches of
conditionals and bodies of loops can be partitioned across many machines to run
on a set of heterogeneous devices, including CPUs, GPUs, and custom ASICs.
Second, programs written in our model support automatic differentiation and
distributed gradient computations, which are necessary for training machine
learning models that use control flow. Third, our choice of non-strict
semantics enables multiple loop iterations to execute in parallel across
machines, and to overlap compute and I/O operations.
  We have done our work in the context of TensorFlow, and it has been used
extensively in research and production. We evaluate it using several real-world
applications, and demonstrate its performance and scalability.
A brief introduction to the Grey Machine Learning
Authors: Xin Ma
Date: 2018-05-04 00:00:00
Tasks: BIG-bench Machine Learning

This paper presents a brief introduction to the key points of the Grey
Machine Learning (GML) based on the kernels. The general formulation of the
grey system models have been firstly summarized, and then the nonlinear
extension of the grey models have been developed also with general
formulations. The kernel implicit mapping is used to estimate the nonlinear
function of the GML model, by extending the nonparametric formulation of the
LSSVM, the estimation of the nonlinear function of the GML model can also be
expressed by the kernels. A short discussion on the priority of this new
framework to the existing grey models and LSSVM have also been discussed in
this paper. And the perspectives and future orientations of this framework have
also been presented.
Classification of Epileptic EEG Signals by Wavelet based CFC
Authors: Amirmasoud Ahmadi, Mahsa Behroozi, Vahid Shalchyan, Mohammad Reza Daliri
Date: 2018-05-04 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification

Electroencephalogram, an influential equipment for analyzing humans
activities and recognition of seizure attacks can play a crucial role in
designing accurate systems which can distinguish ictal seizures from regular
brain alertness, since it is the first step towards accomplishing a high
accuracy computer aided diagnosis system (CAD). In this article a novel
approach for classification of ictal signals with wavelet based cross frequency
coupling (CFC) is suggested. After extracting features by wavelet based CFC,
optimal features have been selected by t-test and quadratic discriminant
analysis (QDA) have completed the Classification.
Motion Artifact Detection in Confocal Laser Endomicroscopy Images
Authors: Maike P. Stoeve, Marc Aubreville, Nicolai Oetter, Christian Knipfer, Helmut Neumann, Florian Stelzle, Andreas Maier
Date: 2017-11-03 00:00:00
Tasks: Artifact Detection, Deep Learning, Diagnostic

Confocal Laser Endomicroscopy (CLE), an optical imaging technique allowing
non-invasive examination of the mucosa on a (sub)cellular level, has proven to
be a valuable diagnostic tool in gastroenterology and shows promising results
in various anatomical regions including the oral cavity. Recently, the
feasibility of automatic carcinoma detection for CLE images of sufficient
quality was shown. However, in real world data sets a high amount of CLE images
is corrupted by artifacts. Amongst the most prevalent artifact types are
motion-induced image deteriorations. In the scope of this work, algorithmic
approaches for the automatic detection of motion artifact-tainted image regions
were developed. Hence, this work provides an important step towards clinical
applicability of automatic carcinoma detection. Both, conventional machine
learning and novel, deep learning-based approaches were assessed. The deep
learning-based approach outperforms the conventional approaches, attaining an
AUC of 0.90.
Multi-Task Learning for Argumentation Mining in Low-Resource Settings
Authors: Claudia Schulz, Steffen Eger, Johannes Daxenberger, Tobias Kahse, Iryna Gurevych
Date: 2018-04-11 00:00:00
Proceeding: NAACL 2018 6
Tasks: Multi-Task Learning

We investigate whether and where multi-task learning (MTL) can improve
performance on NLP problems related to argumentation mining (AM), in particular
argument component identification. Our results show that MTL performs
particularly well (and better than single-task learning) when little training
data is available for the main task, a common scenario in AM. Our findings
challenge previous assumptions that conceptualizations across AM datasets are
divergent and that MTL is difficult for semantic or higher-level tasks.
Feature extraction with regularized siamese networks for outlier detection: application to lesion screening in medical imaging
Authors: Z. Alaverdyan, C. Lartizien
Date: 2018-05-04 00:00:00
Tasks: Lesion Detection, Outlier Detection

Computer aided diagnosis (CAD) systems are designed to assist clinicians in
various tasks, including highlighting abnormal regions in a medical image. A
common approach consists in training a voxel-level binary classifier on a set
of feature vectors extracted from normal and pathological areas in patients'
scans. However, many pathologies (such as epilepsy) are characterized by
lesions that may be located anywhere in the brain, have various shapes, sizes
and texture. An adequate representation of such a heterogeneity requires a
significant amount of annotated data which is a major issue in the medical
domain. Therefore, we built on a previously proposed approach that considers
epilepsy lesion detection task as a voxel-level outlier detection problem. It
consists in building a oc-SVM classifier for each voxel in the brain volume
using a small number of clinically-guided features El Azami et al., 2016. Our
goal in this study is to make a step forward by replacing the handcrafted
features with automatically learnt representations using neural networks. We
propose a novel version of siamese networks trained on patches extracted from
healthy patients' scans only. This network, composed of stacked autoencoders as
subnetworks, is regularized by the reconstruction error of the patches. It is
designed to learn representations that bring patches centered at the same voxel
localization 'closer' with respect to the chosen metric (i.e. cosine). Finally,
the middle layer representations of the subnetworks are fed to oc-SVM
classifiers at voxel-level. The method is validated on 3 patients' MRI scans
with confirmed epilepsy lesions and shows a promising performance.
Multimodal Utterance-level Affect Analysis using Visual, Audio and Text Features
Authors: Didan Deng, Yuqian Zhou, Jimin Pi, Bertram E. Shi
Date: 2018-05-02 00:00:00
Tasks: Emotion Recognition

The integration of information across multiple modalities and across time is
a promising way to enhance the emotion recognition performance of affective
systems. Much previous work has focused on instantaneous emotion recognition.
The 2018 One-Minute Gradual-Emotion Recognition (OMG-Emotion) challenge, which
was held in conjunction with the IEEE World Congress on Computational
Intelligence, encouraged participants to address long-term emotion recognition
by integrating cues from multiple modalities, including facial expression,
audio and language. Intuitively, a multi-modal inference network should be able
to leverage information from each modality and their correlations to improve
recognition over that achievable by a single modality network. We describe here
a multi-modal neural architecture that integrates visual information over time
using an LSTM, and combines it with utterance level audio and text cues to
recognize human sentiment from multimodal clips. Our model outperforms the
unimodal baseline, achieving the concordance correlation coefficients (CCC) of
0.400 on the arousal task, and 0.353 on the valence task.
Using Quantum Mechanics to Cluster Time Series
Authors: Clark Alexander, Luke Shi, Sofya Akhmametyeva
Date: 2018-05-04 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis, Time Series Clustering

In this article we present a method by which we can reduce a time series into
a single point in $\mathbb{R}^{13}$. We have chosen 13 dimensions so as to
prevent too many points from being labeled as "noise." When using a Euclidean
(or Mahalanobis) metric, a simple clustering algorithm will with near certainty
label the majority of points as "noise." On pure physical considerations, this
is not possible. Included in our 13 dimensions are four parameters which
describe the coefficients of a cubic polynomial attached to a Gaussian picking
up a general trend, four parameters picking up periodicity in a time series,
two each for amplitude of a wave and period of a wave, and the final five
report the "leftover" noise of the detrended and aperiodic time series. Of the
final five parameters, four are the centralized probabilistic moments, and the
final for the relative size of the series. The first main contribution of this
work is to apply a theorem of quantum mechanics about the completeness of the
solutions to the quantum harmonic oscillator on $L^2(\mathbb{R})$ to estimating
trends in time series. The second main contribution is the method of fitting
parameters. After many numerical trials, we realized that methods such a
Newton-Rhaphson and Levenberg-Marquardt converge extremely fast if the initial
guess is good. Thus we guessed many initial points in our parameter space and
computed only a few iterations, a technique common in Keogh's work on time
series clustering. Finally, we have produced a model which gives incredibly
accurate results quickly. We ackowledge that there are faster methods as well
of more accurate methods, but this work shows that we can still increase
computation speed with little, if any, cost to accuracy in the sense of data
clustering.
Adversarial Feature Augmentation for Unsupervised Domain Adaptation
Authors: Riccardo Volpi, Pietro Morerio, Silvio Savarese, Vittorio Murino
Date: 2017-11-23 00:00:00
Proceeding: CVPR 2018 6
Tasks: Data Augmentation, Domain Adaptation, Unsupervised Domain Adaptation

Recent works showed that Generative Adversarial Networks (GANs) can be
successfully applied in unsupervised domain adaptation, where, given a labeled
source dataset and an unlabeled target dataset, the goal is to train powerful
classifiers for the target samples. In particular, it was shown that a GAN
objective function can be used to learn target features indistinguishable from
the source ones. In this work, we extend this framework by (i) forcing the
learned feature extractor to be domain-invariant, and (ii) training it through
data augmentation in the feature space, namely performing feature augmentation.
While data augmentation in the image space is a well established technique in
deep learning, feature augmentation has not yet received the same level of
attention. We accomplish it by means of a feature generator trained by playing
the GAN minimax game against source features. Results show that both enforcing
domain-invariance and performing feature augmentation lead to superior or
comparable performance to state-of-the-art results in several unsupervised
domain adaptation benchmarks.
Beyond the Click-Through Rate: Web Link Selection with Multi-level Feedback
Authors: Kun Chen, Kechao Cai, Longbo Huang, John C. S. Lui
Date: 2018-05-04 00:00:00

The web link selection problem is to select a small subset of web links from
a large web link pool, and to place the selected links on a web page that can
only accommodate a limited number of links, e.g., advertisements,
recommendations, or news feeds. Despite the long concerned click-through rate
which reflects the attractiveness of the link itself, the revenue can only be
obtained from user actions after clicks, e.g., purchasing after being directed
to the product pages by recommendation links. Thus, the web links have an
intrinsic \emph{multi-level feedback structure}. With this observation, we
consider the context-free web link selection problem, where the objective is to
maximize revenue while ensuring that the attractiveness is no less than a
preset threshold. The key challenge of the problem is that each link's
multi-level feedbacks are stochastic, and unobservable unless the link is
selected. We model this problem with a constrained stochastic multi-armed
bandit formulation, and design an efficient link selection algorithm, called
Constrained Upper Confidence Bound algorithm (\textbf{Con-UCB}), and prove
$O(\sqrt{T\ln T})$ bounds on both the regret and the violation of the
attractiveness constraint. We conduct extensive experiments on three real-world
datasets, and show that \textbf{Con-UCB} outperforms state-of-the-art
context-free bandit algorithms concerning the multi-level feedback structure.
Local Learning with Deep and Handcrafted Features for Facial Expression Recognition
Authors: Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Marius Popescu
Date: 2018-04-29 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), General Classification

We present an approach that combines automatic features learned by convolutional neural networks (CNN) and handcrafted features computed by the bag-of-visual-words (BOVW) model in order to achieve state-of-the-art results in facial expression recognition. To obtain automatic features, we experiment with multiple CNN architectures, pre-trained models and training procedures, e.g. Dense-Sparse-Dense. After fusing the two types of features, we employ a local learning framework to predict the class label for each test image. The local learning framework is based on three steps. First, a k-nearest neighbors model is applied in order to select the nearest training samples for an input test image. Second, a one-versus-all Support Vector Machines (SVM) classifier is trained on the selected training samples. Finally, the SVM classifier is used to predict the class label only for the test image it was trained for. Although we have used local learning in combination with handcrafted features in our previous work, to the best of our knowledge, local learning has never been employed in combination with deep features. The experiments on the 2013 Facial Expression Recognition (FER) Challenge data set, the FER+ data set and the AffectNet data set demonstrate that our approach achieves state-of-the-art results. With a top accuracy of 75.42% on FER 2013, 87.76% on the FER+, 59.58% on AffectNet 8-way classification and 63.31% on AffectNet 7-way classification, we surpass the state-of-the-art methods by more than 1% on all data sets.
On the Complexity of CCG Parsing
Authors: Marco Kuhlmann, Giorgio Satta, Peter Jonsson
Date: 2017-02-21 00:00:00
Proceeding: CL 2018 9
Tasks: Sentence, TAG

We study the parsing complexity of Combinatory Categorial Grammar (CCG) in
the formalism of Vijay-Shanker and Weir (1994). As our main result, we prove
that any parsing algorithm for this formalism will take in the worst case
exponential time when the size of the grammar, and not only the length of the
input sentence, is included in the analysis. This sets the formalism of
Vijay-Shanker and Weir (1994) apart from weakly equivalent formalisms such as
Tree-Adjoining Grammar (TAG), for which parsing can be performed in time
polynomial in the combined size of grammar and input sentence. Our results
contribute to a refined understanding of the class of mildly context-sensitive
grammars, and inform the search for new, mildly context-sensitive versions of
CCG.
Combinatorial Pure Exploration with Continuous and Separable Reward Functions and Its Applications (Extended Version)
Authors: Weiran Huang, Jungseul Ok, Liang Li, Wei Chen
Date: 2018-05-04 00:00:00

We study the Combinatorial Pure Exploration problem with Continuous and
Separable reward functions (CPE-CS) in the stochastic multi-armed bandit
setting. In a CPE-CS instance, we are given several stochastic arms with
unknown distributions, as well as a collection of possible decisions. Each
decision has a reward according to the distributions of arms. The goal is to
identify the decision with the maximum reward, using as few arm samples as
possible. The problem generalizes the combinatorial pure exploration problem
with linear rewards, which has attracted significant attention in recent years.
In this paper, we propose an adaptive learning algorithm for the CPE-CS
problem, and analyze its sample complexity. In particular, we introduce a new
hardness measure called the consistent optimality hardness, and give both the
upper and lower bounds of sample complexity. Moreover, we give examples to
demonstrate that our solution has the capacity to deal with non-linear reward
functions.
Comparison of various image fusion methods for impervious surface classification from VNREDSat-1
Authors: Hung V. Luu, Manh V. Pham, Chuc D. Man, Hung Q. Bui, Thanh T. N. Nguyen
Date: 2018-03-06 00:00:00
Tasks: Classification, Diversity, General Classification

Impervious surface is an important indicator for urban development
monitoring. Accurate urban impervious surfaces mapping with VNREDSat-1 remains
challenging due to their spectral diversity not captured by individual PAN
image. In this artical, five multi-resolution image fusion techniques were
compared for classification task of urban impervious surface. The result shows
that for VNREDSat-1 dataset, UNB and Wavelet tranform methods are the best
techniques reserving spatial and spectral information of original MS image,
respectively. However, the UNB technique gives best results when it comes to
impervious surface classification especially in the case of shadow area
included in non-impervious surface group.
Transferring GANs: generating images from limited data
Authors: Yaxing Wang, Chenshen Wu, Luis Herranz, Joost Van de Weijer, Abel Gonzalez-Garcia, Bogdan Raducanu
Date: 2018-05-04 00:00:00
Proceeding: ECCV 2018 9
Tasks: 10-shot image generation, Diversity, Domain Adaptation, Image Generation

Transferring the knowledge of pretrained networks to new domains by means of
finetuning is a widely used practice for applications based on discriminative
models. To the best of our knowledge this practice has not been studied within
the context of generative deep networks. Therefore, we study domain adaptation
applied to image generation with generative adversarial networks. We evaluate
several aspects of domain adaptation, including the impact of target domain
size, the relative distance between source and target domain, and the
initialization of conditional GANs. Our results show that using knowledge from
pretrained networks can shorten the convergence time and can significantly
improve the quality of the generated images, especially when the target data is
limited. We show that these conclusions can also be drawn for conditional GANs
even when the pretrained model was trained without conditioning. Our results
also suggest that density may be more important than diversity and a dataset
with one or few densely sampled classes may be a better source model than more
diverse datasets such as ImageNet or Places.