pwc-archive/papers-with-abstracts huggingface dataset:
On Optimizing Deep Convolutional Neural Networks by Evolutionary Computing
Authors: M. U. B. Dias, D. D. N. De Silva, S. Fernando
Date: 2018-08-06 00:00:00
Tasks: Reinforcement Learning

Optimization for deep networks is currently a very active area of research.
As neural networks become deeper, the ability in manually optimizing the
network becomes harder. Mini-batch normalization, identification of effective
respective fields, momentum updates, introduction of residual blocks, learning
rate adoption, etc. have been proposed to speed up the rate of convergent in
manual training process while keeping the higher accuracy level. However, the
problem of finding optimal topological structure for a given problem is
becoming a challenging task need to be addressed immediately. Few researchers
have attempted to optimize the network structure using evolutionary computing
approaches. Among them, few have successfully evolved networks with
reinforcement learning and long-short-term memory. A very few has applied
evolutionary programming into deep convolution neural networks. These attempts
are mainly evolved the network structure and then subsequently optimized the
hyper-parameters of the network. However, a mechanism to evolve the deep
network structure under the techniques currently being practiced in manual
process is still absent. Incorporation of such techniques into chromosomes
level of evolutionary computing, certainly can take us to better topological
deep structures. The paper concludes by identifying the gap between
evolutionary based deep neural networks and deep neural networks. Further, it
proposes some insights for optimizing deep neural networks using evolutionary
computing techniques.
Gray-box Adversarial Training
Authors: Vivek B. S., Konda Reddy Mopuri, R. Venkatesh Babu
Date: 2018-08-06 00:00:00
Proceeding: ECCV 2018 9

Adversarial samples are perturbed inputs crafted to mislead the machine
learning systems. A training mechanism, called adversarial training, which
presents adversarial samples along with clean samples has been introduced to
learn robust models. In order to scale adversarial training for large datasets,
these perturbations can only be crafted using fast and simple methods (e.g.,
gradient ascent). However, it is shown that adversarial training converges to a
degenerate minimum, where the model appears to be robust by generating weaker
adversaries. As a result, the models are vulnerable to simple black-box
attacks. In this paper we, (i) demonstrate the shortcomings of existing
evaluation policy, (ii) introduce novel variants of white-box and black-box
attacks, dubbed gray-box adversarial attacks" based on which we propose novel
evaluation method to assess the robustness of the learned models, and (iii)
propose a novel variant of adversarial training, named Graybox Adversarial
Training" that uses intermediate versions of the models to seed the
adversaries. Experimental evaluation demonstrates that the models trained using
our method exhibit better robustness compared to both undefended and
adversarially trained model
Deep Transfer Learning for EEG-based Brain Computer Interface
Authors: Chuanqi Tan, Fuchun Sun, Wenchang Zhang
Date: 2018-08-06 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Optical Flow Estimation, Transfer Learning

The electroencephalography classifier is the most important component of
brain-computer interface based systems. There are two major problems hindering
the improvement of it. First, traditional methods do not fully exploit
multimodal information. Second, large-scale annotated EEG datasets are almost
impossible to acquire because biological data acquisition is challenging and
quality annotation is costly. Herein, we propose a novel deep transfer learning
approach to solve these two problems. First, we model cognitive events based on
EEG data by characterizing the data using EEG optical flow, which is designed
to preserve multimodal EEG information in a uniform representation. Second, we
design a deep transfer learning framework which is suitable for transferring
knowledge by joint training, which contains a adversarial network and a special
loss function. The experiments demonstrate that our approach, when applied to
EEG classification tasks, has many advantages, such as robustness and accuracy.
NIMFA: A Python Library for Nonnegative Matrix Factorization
Authors: Marinka Zitnik, Blaz Zupan
Date: 2018-08-06 00:00:00

NIMFA is an open-source Python library that provides a unified interface to
nonnegative matrix factorization algorithms. It includes implementations of
state-of-the-art factorization methods, initialization approaches, and quality
scoring. It supports both dense and sparse matrix representation. NIMFA's
component-based implementation and hierarchical design should help the users to
employ already implemented techniques or design and code new strategies for
matrix factorization tasks.
Using Linguistic Cues for Analyzing Social Movements
Authors: Rezvaneh Rezapour
Date: 2018-08-06 00:00:00
Tasks: Articles

With the growth of social media usage, social activists try to leverage this
platform to raise the awareness related to a social issue and engage the public
worldwide. The broad use of social media platforms in recent years, made it
easier for the people to stay up-to-date on the news related to regional and
worldwide events. While social media, namely Twitter, assists social movements
to connect with more people and mobilize the movement, traditional media such
as news articles help in spreading the news related to the events in a broader
aspect. In this study, we analyze linguistic features and cues, such as
individualism vs. pluralism, sentiment and emotion to examine the relationship
between the medium and discourse over time. We conduct this work in a specific
application context, the "Black Lives Matter" (BLM) movement, and compare
discussions related to this event in social media vs. news articles.
Logical Semantics and Commonsense Knowledge: Where Did we Go Wrong, and How to Go Forward, Again
Authors: Walid S. Saba
Date: 2018-08-06 00:00:00

We argue that logical semantics might have faltered due to its failure in
distinguishing between two fundamentally very different types of concepts:
ontological concepts, that should be types in a strongly-typed ontology, and
logical concepts, that are predicates corresponding to properties of and
relations between objects of various ontological types. We will then show that
accounting for these differences amounts to the integration of lexical and
compositional semantics in one coherent framework, and to an embedding in our
logical semantics of a strongly-typed ontology that reflects our commonsense
view of the world and the way we talk about it in ordinary language. We will
show that in such a framework a number of challenges in natural language
semantics can be adequately and systematically treated.
Concentration bounds for empirical conditional value-at-risk: The unbounded case
Authors: Ravi Kumar Kolla, Prashanth L. A., Sanjay P. Bhat, Krishna Jagannathan
Date: 2018-08-06 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

In several real-world applications involving decision making under
uncertainty, the traditional expected value objective may not be suitable, as
it may be necessary to control losses in the case of a rare but extreme event.
Conditional Value-at-Risk (CVaR) is a popular risk measure for modeling the
aforementioned objective. We consider the problem of estimating CVaR from
i.i.d. samples of an unbounded random variable, which is either sub-Gaussian or
sub-exponential. We derive a novel one-sided concentration bound for a natural
sample-based CVaR estimator in this setting. Our bound relies on a
concentration result for a quantile-based estimator for Value-at-Risk (VaR),
which may be of independent interest.
Incorporating Scalability in Unsupervised Spatio-Temporal Feature Learning
Authors: Sujoy Paul, Sourya Roy, Amit K. Roy-Chowdhury
Date: 2018-08-06 00:00:00

Deep neural networks are efficient learning machines which leverage upon a
large amount of manually labeled data for learning discriminative features.
However, acquiring substantial amount of supervised data, especially for videos
can be a tedious job across various computer vision tasks. This necessitates
learning of visual features from videos in an unsupervised setting. In this
paper, we propose a computationally simple, yet effective, framework to learn
spatio-temporal feature embedding from unlabeled videos. We train a
Convolutional 3D Siamese network using positive and negative pairs mined from
videos under certain probabilistic assumptions. Experimental results on three
datasets demonstrate that our proposed framework is able to learn weights which
can be used for same as well as cross dataset and tasks.
Liquid Pouring Monitoring via Rich Sensory Inputs
Authors: Tz-Ying Wu, Juan-Ting Lin, Tsun-Hsuang Wang, Chan-Wei Hu, Juan Carlos Niebles, Min Sun
Date: 2018-08-06 00:00:00
Proceeding: ECCV 2018 9

Humans have the amazing ability to perform very subtle manipulation task
using a closed-loop control system with imprecise mechanics (i.e., our body
parts) but rich sensory information (e.g., vision, tactile, etc.). In the
closed-loop system, the ability to monitor the state of the task via rich
sensory information is important but often less studied. In this work, we take
liquid pouring as a concrete example and aim at learning to continuously
monitor whether liquid pouring is successful (e.g., no spilling) or not via
rich sensory inputs. We mimic humans' rich sensories using synchronized
observation from a chest-mounted camera and a wrist-mounted IMU sensor. Given
many success and failure demonstrations of liquid pouring, we train a
hierarchical LSTM with late fusion for monitoring. To improve the robustness of
the system, we propose two auxiliary tasks during training: inferring (1) the
initial state of containers and (2) forecasting the one-step future 3D
trajectory of the hand with an adversarial training procedure. These tasks
encourage our method to learn representation sensitive to container states and
how objects are manipulated in 3D. With these novel components, our method
achieves ~8% and ~11% better monitoring accuracy than the baseline method
without auxiliary tasks on unseen containers and unseen users respectively.
A Study of Deep Feature Fusion based Methods for Classifying Multi-lead ECG
Authors: Bin Chen, Wei Guo, Bin Li, Rober K. F. Teng, Mingjun Dai, Jianping Luo, Hui Wang
Date: 2018-08-06 00:00:00
Tasks: Classification, General Classification, Sensitivity

An automatic classification method has been studied to effectively detect and
recognize Electrocardiogram (ECG). Based on the synchronizing and orthogonal
relationships of multiple leads, we propose a Multi-branch Convolution and
Residual Network (MBCRNet) with three kinds of feature fusion methods for
automatic detection of normal and abnormal ECG signals. Experiments are
conducted on the Chinese Cardiovascular Disease Database (CCDD). Through
10-fold cross-validation, we achieve an average accuracy of 87.04% and a
sensitivity of 89.93%, which outperforms previous methods under the same
database. It is also shown that the multi-lead feature fusion network can
improve the classification accuracy over the network only with the single lead
features.
Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss Weighting
Authors: Nils Gessert, Thilo Sentker, Frederic Madesta, Rüdiger Schmitz, Helge Kniep, Ivo Baltruschat, René Werner, Alexander Schlaefer
Date: 2018-08-05 00:00:00
Tasks: Meta-Learning

In this paper we present the methods of our submission to the ISIC 2018
challenge for skin lesion diagnosis (Task 3). The dataset consists of 10000
images with seven image-level classes to be distinguished by an automated
algorithm. We employ an ensemble of convolutional neural networks for this
task. In particular, we fine-tune pretrained state-of-the-art deep learning
models such as Densenet, SENet and ResNeXt. We identify heavy class imbalance
as a key problem for this challenge and consider multiple balancing approaches
such as loss weighting and balanced batch sampling. Another important feature
of our pipeline is the use of a vast amount of unscaled crops for evaluation.
Last, we consider meta learning approaches for the final predictions. Our team
placed second at the challenge while being the best approach using only
publicly available data.
Error Detection in a Large-Scale Lexical Taxonomy
Authors: Sifan Liu, Hongzhi Wang
Date: 2018-08-05 00:00:00
Tasks: Relation

Knowledge base (KB) is an important aspect in artificial intelligence. One
significant challenge faced by KB construction is that it contains many noises,
which prevents its effective usage. Even though some KB cleansing algorithms
have been proposed, they focus on the structure of the knowledge graph and
neglect the relation between the concepts, which could be helpful to discover
wrong relations in KB. Motived by this, we measure the relation of two concepts
by the distance between their corresponding instances and detect errors within
the intersection of the conflicting concept sets. For efficient and effective
knowledge base cleansing, we first apply a distance-based Model to determine
the conflicting concept sets using two different methods. Then, we propose and
analyze several algorithms on how to detect and repairing the errors based on
our model, where we use hash method for an efficient way to calculate distance.
Experimental results demonstrate that the proposed approaches could cleanse the
knowledge bases efficiently and effectively.
Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models
Authors: Dong Su, huan zhang, Hongge Chen, Jin-Feng Yi, Pin-Yu Chen, Yupeng Gao
Date: 2018-08-05 00:00:00
Proceeding: ECCV 2018 9
Tasks: General Classification, image-classification, Image Classification

The prediction accuracy has been the long-lasting and sole standard for
comparing the performance of different image classification models, including
the ImageNet competition. However, recent studies have highlighted the lack of
robustness in well-trained deep neural networks to adversarial examples.
Visually imperceptible perturbations to natural images can easily be crafted
and mislead the image classifiers towards misclassification. To demystify the
trade-offs between robustness and accuracy, in this paper we thoroughly
benchmark 18 ImageNet models using multiple robustness metrics, including the
distortion, success rate and transferability of adversarial examples between
306 pairs of models. Our extensive experimental results reveal several new
insights: (1) linear scaling law - the empirical $\ell_2$ and $\ell_\infty$
distortion metrics scale linearly with the logarithm of classification error;
(2) model architecture is a more critical factor to robustness than model size,
and the disclosed accuracy-robustness Pareto frontier can be used as an
evaluation criterion for ImageNet model designers; (3) for a similar network
architecture, increasing network depth slightly improves robustness in
$\ell_\infty$ distortion; (4) there exist models (in VGG family) that exhibit
high adversarial transferability, while most adversarial examples crafted from
one model can only be transferred within the same family. Experiment code is
publicly available at \url{https://github.com/huanzhang12/Adversarial_Survey}.
Hybrid Subspace Learning for High-Dimensional Data
Authors: Micol Marchetti-Bowick, Benjamin J. Lengerich, Ankur P. Parikh, Eric P. Xing
Date: 2018-08-05 00:00:00
Tasks: Dimensionality Reduction, Video Background Subtraction, Vocal Bursts Intensity Prediction

The high-dimensional data setting, in which p >> n, is a challenging
statistical paradigm that appears in many real-world problems. In this setting,
learning a compact, low-dimensional representation of the data can
substantially help distinguish signal from noise. One way to achieve this goal
is to perform subspace learning to estimate a small set of latent features that
capture the majority of the variance in the original data. Most existing
subspace learning models, such as PCA, assume that the data can be fully
represented by its embedding in one or more latent subspaces. However, in this
work, we argue that this assumption is not suitable for many high-dimensional
datasets; often only some variables can easily be projected to a
low-dimensional space. We propose a hybrid dimensionality reduction technique
in which some features are mapped to a low-dimensional subspace while others
remain in the original space. Our model leads to more accurate estimation of
the latent space and lower reconstruction error. We present a simple
optimization procedure for the resulting biconvex problem and show synthetic
data results that demonstrate the advantages of our approach over existing
methods. Finally, we demonstrate the effectiveness of this method for
extracting meaningful features from both gene expression and video background
subtraction datasets.
Too many secants: a hierarchical approach to secant-based dimensionality reduction on large data sets
Authors: Henry Kvinge, Elin Farnell, Michael Kirby, Chris Peterson
Date: 2018-08-05 00:00:00
Tasks: Dimensionality Reduction

A fundamental question in many data analysis settings is the problem of
discerning the "natural" dimension of a data set. That is, when a data set is
drawn from a manifold (possibly with noise), a meaningful aspect of the data is
the dimension of that manifold. Various approaches exist for estimating this
dimension, such as the method of Secant-Avoidance Projection (SAP).
Intuitively, the SAP algorithm seeks to determine a projection which best
preserves the lengths of all secants between points in a data set; by applying
the algorithm to find the best projections to vector spaces of various
dimensions, one may infer the dimension of the manifold of origination. That
is, one may learn the dimension at which it is possible to construct a
diffeomorphic copy of the data in a lower-dimensional Euclidean space. Using
Whitney's embedding theorem, we can relate this information to the natural
dimension of the data. A drawback of the SAP algorithm is that a data set with
$T$ points has $O(T^2)$ secants, making the computation and storage of all
secants infeasible for very large data sets. In this paper, we propose a novel
algorithm that generalizes the SAP algorithm with an emphasis on addressing
this issue. That is, we propose a hierarchical secant-based
dimensionality-reduction method, which can be employed for data sets where
explicitly calculating all secants is not feasible.
Missing Value Imputation Based on Deep Generative Models
Authors: Hongbao Zhang, Pengtao Xie, Eric Xing
Date: 2018-08-05 00:00:00
Tasks: Imputation, Missing Values

Missing values widely exist in many real-world datasets, which hinders the
performing of advanced data analytics. Properly filling these missing values is
crucial but challenging, especially when the missing rate is high. Many
approaches have been proposed for missing value imputation (MVI), but they are
mostly heuristics-based, lacking a principled foundation and do not perform
satisfactorily in practice. In this paper, we propose a probabilistic framework
based on deep generative models for MVI. Under this framework, imputing the
missing entries amounts to seeking a fixed-point solution between two
conditional distributions defined on the missing entries and latent variables
respectively. These distributions are parameterized by deep neural networks
(DNNs) which possess high approximation power and can capture the nonlinear
relationships between missing entries and the observed values. The learning of
weight parameters of DNNs is performed by maximizing an approximation of the
log-likelihood of observed values. We conducted extensive evaluation on 13
datasets and compared with 11 baselines methods, where our methods largely
outperforms the baselines.
Kid on The Phone! Toward Automatic Detection of Children on Mobile Devices
Authors: Toan Nguyen, Aditi Roy, Nasir Memon
Date: 2018-08-05 00:00:00

Studies have shown that children can be exposed to smart devices at a very
early age. This has important implications on research in children-computer
interaction, children online safety and early education. Many systems have been
built based on such research. In this work, we present multiple techniques to
automatically detect the presence of a child on a smart device, which could be
used as the first step on such systems. Our methods distinguish children from
adults based on behavioral differences while operating a touch-enabled modern
computing device. Behavioral differences are extracted from data recorded by
the touchscreen and built-in sensors. To evaluate the effectiveness of the
proposed methods, a new data set has been created from 50 children and adults
who interacted with off-the-shelf applications on smart phones. Results show
that it is possible to achieve 99% accuracy and less than 0.5% error rate after
8 consecutive touch gestures using only touch information or 5 seconds of
sensor reading. If information is used from multiple sensors, then only after 3
gestures, similar performance could be achieved.
A Multi-task Framework for Skin Lesion Detection and Segmentation
Authors: Sulaiman Vesal, Shreyas Malakarjun Patil, Nishant Ravikumar, Andreas Maier
Date: 2018-08-05 00:00:00
Tasks: Lesion Detection, Lesion Segmentation, Region Proposal, Segmentation, Skin Lesion Segmentation

Early detection and segmentation of skin lesions is crucial for timely
diagnosis and treatment, necessary to improve the survival rate of patients.
However, manual delineation is time consuming and subject to intra- and
inter-observer variations among dermatologists. This underlines the need for an
accurate and automatic approach to skin lesion segmentation. To tackle this
issue, we propose a multi-task convolutional neural network (CNN) based, joint
detection and segmentation framework, designed to initially localize the lesion
and subsequently, segment it. A `Faster region-based convolutional neural
network' (Faster-RCNN) which comprises a region proposal network (RPN), is used
to generate bounding boxes/region proposals, for lesion localization in each
image. The proposed regions are subsequently refined using a softmax classifier
and a bounding-box regressor. The refined bounding boxes are finally cropped
and segmented using `SkinNet', a modified version of U-Net. We trained and
evaluated the performance of our network, using the ISBI 2017 challenge and the
PH2 datasets, and compared it with the state-of-the-art, using the official
test data released as part of the challenge for the former. Our approach
outperformed others in terms of Dice coefficients ($>0.93$), Jaccard index
($>0.88$), accuracy ($>0.96$) and sensitivity ($>0.95$), across five-fold cross
validation experiments.