pwc-archive/papers-with-abstracts huggingface dataset:
VLASE: Vehicle Localization by Aggregating Semantic Edges
Authors: Xin Yu, Sagar Chaturvedi, Chen Feng, Yuichi Taguchi, Teng-Yok Lee, Clinton Fernandes, Srikumar Ramalingam
Date: 2018-07-06 00:00:00
Tasks: Image Retrieval, Retrieval

In this paper, we propose VLASE, a framework to use semantic edge features
from images to achieve on-road localization. Semantic edge features denote edge
contours that separate pairs of distinct objects such as building-sky, road-
sidewalk, and building-ground. While prior work has shown promising results by
utilizing the boundary between prominent classes such as sky and building using
skylines, we generalize this approach to consider semantic edge features that
arise from 19 different classes. Our localization algorithm is simple, yet very
powerful. We extract semantic edge features using a recently introduced CASENet
architecture and utilize VLAD framework to perform image retrieval. Our
experiments show that we achieve improvement over some of the state-of-the-art
localization algorithms such as SIFT-VLAD and its deep variant NetVLAD. We use
ablation study to study the importance of different semantic classes and show
that our unified approach achieves better performance compared to individual
prominent features such as skylines.
YouTube for Patient Education: A Deep Learning Approach for Understanding Medical Knowledge from User-Generated Videos
Authors: Xiao Liu, Bin Zhang, Anjana Susarla, Rema Padman
Date: 2018-07-06 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, General Classification, image-classification, Image Classification, Natural Language Understanding

YouTube presents an unprecedented opportunity to explore how machine learning
methods can improve healthcare information dissemination. We propose an
interdisciplinary lens that synthesizes machine learning methods with
healthcare informatics themes to address the critical issue of developing a
scalable algorithmic solution to evaluate videos from a health literacy and
patient education perspective. We develop a deep learning method to understand
the level of medical knowledge encoded in YouTube videos. Preliminary results
suggest that we can extract medical knowledge from YouTube videos and classify
videos according to the embedded knowledge with satisfying performance. Deep
learning methods show great promise in knowledge extraction, natural language
understanding, and image classification, especially in an era of
patient-centric care and precision medicine.
Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning
Authors: Bryan Lim, Mihaela van der Schaar
Date: 2018-07-06 00:00:00
Tasks: Deep Learning

Joint models for longitudinal and time-to-event data are commonly used in
longitudinal studies to forecast disease trajectories over time. Despite the
many advantages of joint modeling, the standard forms suffer from limitations
that arise from a fixed model specification and computational difficulties when
applied to large datasets. We adopt a deep learning approach to address these
limitations, enhancing existing methods with the flexibility and scalability of
deep neural networks while retaining the benefits of joint modeling. Using data
from the Alzheimer's Disease Neuroimaging Institute, we show improvements in
performance and scalability compared to traditional methods.
The CodRep Machine Learning on Source Code Competition
Authors: Zimin Chen, Martin Monperrus
Date: 2018-07-06 00:00:00
Tasks: BIG-bench Machine Learning

CodRep is a machine learning competition on source code data. It is carefully
designed so that anybody can enter the competition, whether professional
researchers, students or independent scholars, without specific knowledge in
machine learning or program analysis. In particular, it aims at being a common
playground on which the machine learning and the software engineering research
communities can interact. The competition has started on April 14th 2018 and
has ended on October 14th 2018. The CodRep data is hosted at
https://github.com/KTH/CodRep-competition/.
Graph of brain structures grading for early detection of Alzheimer's disease
Authors: Kilian Hett, Vinh-Thong Ta, Jose Vicente Manjon, Pierrick Coupé
Date: 2018-07-06 00:00:00

Alzheimer's disease is the most common dementia leading to an irreversible
neurodegenerative process. To date, subject revealed advanced brain structural
alterations when the diagnosis is established. Therefore, an earlier diagnosis
of this dementia is crucial although it is a challenging task. Recently, many
studies have proposed biomarkers to perform early detection of Alzheimer's
disease. Some of them have proposed methods based on inter-subject similarity
while other approaches have investigated framework using intra-subject
variability. In this work, we propose a novel framework combining both
approaches within an efficient graph of brain structures grading. Subsequently,
we demonstrate the competitive performance of the proposed method compared to
state-of-the-art methods.
Deep Learning Based Sphere Decoding
Authors: Mostafa Mohammadkarimi, Mehrtash Mehrabi, Masoud Ardakani, Yindi Jing
Date: 2018-07-06 00:00:00
Tasks: Deep Learning

In this paper, a deep learning (DL)-based sphere decoding algorithm is proposed, where the radius of the decoding hypersphere is learned by a deep neural network (DNN). The performance achieved by the proposed algorithm is very close to the optimal maximum likelihood decoding (MLD) over a wide range of signal-to-noise ratios (SNRs), while the computational complexity, compared to existing sphere decoding variants, is significantly reduced. This improvement is attributed to DNN's ability of intelligently learning the radius of the hypersphere used in decoding. The expected complexity of the proposed DL-based algorithm is analytically derived and compared with existing ones. It is shown that the number of lattice points inside the decoding hypersphere drastically reduces in the DL-based algorithm in both the average and worst-case senses. The effectiveness of the proposed algorithm is shown through simulation for high-dimensional multiple-input multiple-output (MIMO) systems, using high-order modulations.
NAPS: Natural Program Synthesis Dataset
Authors: Maksym Zavershynskyi, Alex Skidanov, Illia Polosukhin
Date: 2018-07-06 00:00:00
Tasks: Program Synthesis

We present a program synthesis-oriented dataset consisting of human written
problem statements and solutions for these problems. The problem statements
were collected via crowdsourcing and the program solutions were extracted from
human-written solutions in programming competitions, accompanied by
input/output examples. We propose using this dataset for the program synthesis
tasks aimed for working with real user-generated data. As a baseline we present
few models, with the best model achieving 8.8% accuracy, showcasing both the
complexity of the dataset and large room for future research.
Data Augmentation for Detection of Architectural Distortion in Digital Mammography using Deep Learning Approach
Authors: Arthur C. Costa, Helder C. R. Oliveira, Juliana H. Catani, Nestor de Barros, Carlos F. E. Melo, Marcelo A. C. Vieira
Date: 2018-07-06 00:00:00
Tasks: Data Augmentation

Early detection of breast cancer can increase treatment efficiency.
Architectural Distortion (AD) is a very subtle contraction of the breast tissue
and may represent the earliest sign of cancer. Since it is very likely to be
unnoticed by radiologists, several approaches have been proposed over the years
but none using deep learning techniques. To train a Convolutional Neural
Network (CNN), which is a deep neural architecture, is necessary a huge amount
of data. To overcome this problem, this paper proposes a data augmentation
approach applied to clinical image dataset to properly train a CNN. Results
using receiver operating characteristic analysis showed that with a very
limited dataset we could train a CNN to detect AD in digital mammography with
area under the curve (AUC = 0.74).
Sparse Deep Neural Network Exact Solutions
Authors: Jeremy Kepner, Vijay Gadepally, Hayden Jananthan, Lauren Milechin, Sid Samsi
Date: 2018-07-06 00:00:00

Deep neural networks (DNNs) have emerged as key enablers of machine learning.
Applying larger DNNs to more diverse applications is an important challenge.
The computations performed during DNN training and inference are dominated by
operations on the weight matrices describing the DNN. As DNNs incorporate more
layers and more neurons per layers, these weight matrices may be required to be
sparse because of memory limitations. Sparse DNNs are one possible approach,
but the underlying theory is in the early stages of development and presents a
number of challenges, including determining the accuracy of inference and
selecting nonzero weights for training. Associative array algebra has been
developed by the big data community to combine and extend database, matrix, and
graph/network concepts for use in large, sparse data problems. Applying this
mathematics to DNNs simplifies the formulation of DNN mathematics and reveals
that DNNs are linear over oscillating semirings. This work uses associative
array DNNs to construct exact solutions and corresponding perturbation models
to the rectified linear unit (ReLU) DNN equations that can be used to construct
test vectors for sparse DNN implementations over various precisions. These
solutions can be used for DNN verification, theoretical explorations of DNN
properties, and a starting point for the challenge of sparse training.
Affective EEG-Based Person Identification Using the Deep Learning Approach
Authors: Theerawit Wilaiprasitporn, Apiwat Ditthapron, Karis Matchaparn, Tanaboon Tongbuasirilai, Nannapas Banluesombatkul, Ekapol Chuangsuwanich
Date: 2018-07-05 00:00:00
Tasks: Deep Learning, EEG, Electroencephalogram (EEG), Person Identification

Electroencephalography (EEG) is another mode for performing Person
Identification (PI). Due to the nature of the EEG signals, EEG-based PI is
typically done while the person is performing some kind of mental task, such as
motor control. However, few works have considered EEG-based PI while the person
is in different mental states (affective EEG). The aim of this paper is to
improve the performance of affective EEG-based PI using a deep learning
approach. \textcolor{red}{We proposed a cascade of deep learning using a
combination of Convolutional Neural Networks (CNNs) and Recurrent Neural
Networks (RNNs)}. CNNs are used to handle the spatial information from the EEG
while RNNs extract the temporal information. \textcolor{red}{We evaluated two
types of RNNs, namely, Long Short-Term Memory (CNN-LSTM) and Gated Recurrent
Unit (CNN-GRU). } The proposed method is evaluated on the state-of-the-art
affective dataset DEAP. The results indicate that CNN-GRU and CNN-LSTM can
perform PI from different affective states and reach up to 99.90--100\% mean
Correct Recognition Rate (CRR), significantly outperforming a support vector
machine (SVM) baseline system that uses power spectral density (PSD) features.
Notably, the 100\% mean \emph{CRR} comes from only 40 subjects in DEAP dataset.
To reduce the number of EEG electrodes from thirty-two to five for more
practical applications, the frontal region gives the best results reaching up
to 99.17\% CRR (from CNN-GRU). Amongst the two deep learning models, we find
CNN-GRU to slightly outperform CNN-LSTM, while having faster training time.
\textcolor{red}{Furthermore, CNN-GRU overcomes the influence of affective
states in EEG-Based PI reported in the previous works.
A new ultrasound despeckling method through adaptive threshold
Authors: Hamid Reza Shahdoosti
Date: 2018-07-05 00:00:00

An efficient despeckling method using a quantum-inspired adaptive threshold
function is presented for reducing noise of ultrasound images. In the first
step, the ultrasound image is decorrelated by an spectrum equalization
procedure due to the fact that speckle noise is neither Gaussian nor white. In
fact, a linear filter is exploited to flatten the power spectral density (PSD)
of the ultrasound image. Then, the proposed method shrinks complex wavelet
coefficients based on the quantum-inspired adaptive threshold function. The
proposed approach has been used to denoise both real and simulated data sets
and compare with other widely adopted techniques. Experimental results
demonstrate that the proposed method has a competitive performance to remove
speckle noise and can preserve details and textures of medical ultrasound
images.
Blockchain as a Service: A Decentralized and Secure Computing Paradigm
Authors: Gihan J. Mendis, Yi-Fu Wu, Jin Wei, Moein Sabounchi, Rigoberto Roche'
Date: 2018-07-05 00:00:00
Tasks: Cloud Computing, Privacy Preserving

Thanks to the advances in machine learning, data-driven analysis tools have become valuable solutions for various applications. However, there still remain essential challenges to develop effective data-driven methods because of the need to acquire a large amount of data and to have sufficient computing power to handle the data. In many instances these challenges are addressed by relying on a dominant cloud computing vendor, but, although commercial cloud vendors provide valuable platforms for data analytics, they can suffer from a lack of transparency, security, and privacy-perservation. Furthermore, reliance on cloud servers prevents applying big data analytics in environments where the computing power is scattered. To address these challenges, a decentralize, secure, and privacy-preserving computing paradigm is proposed to enable an asynchronized cooperative computing process amongst scattered and untrustworthy computing nodes that may have limited computing power and computing intelligence. This paradigm is designed by exploring blockchain, decentralized learning, homomorphic encryption, and software defined networking(SDN) techniques. The performance of the proposed paradigm is evaluated via different scenarios in the simulation section.
Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
Authors: Supasorn Suwajanakorn, Noah Snavely, Jonathan Tompson, Mohammad Norouzi
Date: 2018-07-05 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: 3D Pose Estimation, Pose Estimation

This paper presents KeypointNet, an end-to-end geometric reasoning framework
to learn an optimal set of category-specific 3D keypoints, along with their
detectors. Given a single image, KeypointNet extracts 3D keypoints that are
optimized for a downstream task. We demonstrate this framework on 3D pose
estimation by proposing a differentiable objective that seeks the optimal set
of keypoints for recovering the relative pose between two views of an object.
Our model discovers geometrically and semantically consistent keypoints across
viewing angles and instances of an object category. Importantly, we find that
our end-to-end framework using no ground-truth keypoint annotations outperforms
a fully supervised baseline using the same neural network architecture on the
task of pose estimation. The discovered 3D keypoints on the car, chair, and
plane categories of ShapeNet are visualized at http://keypointnet.github.io/.
Jigsaw Puzzle Solving Using Local Feature Co-Occurrences in Deep Neural Networks
Authors: Marie-Morgane Paumard, David Picard, Hedi Tabia
Date: 2018-07-05 00:00:00
Tasks: General Classification, Object Reconstruction, Position

Archaeologists are in dire need of automated object reconstruction methods.
Fragments reassembly is close to puzzle problems, which may be solved by
computer vision algorithms. As they are often beaten on most image related
tasks by deep learning algorithms, we study a classification method that can
solve jigsaw puzzles. In this paper, we focus on classifying the relative
position: given a couple of fragments, we compute their local relation (e.g. on
top). We propose several enhancements over the state of the art in this domain,
which is outperformed by our method by 25\%. We propose an original dataset
composed of pictures from the Metropolitan Museum of Art. We propose a greedy
reconstruction method based on the predicted relative positions.
Semisupervised Learning on Heterogeneous Graphs and its Applications to Facebook News Feed
Authors: Cheng Ju, James Li, Bram Wasti, Shengbo Guo
Date: 2018-05-18 00:00:00
Tasks: Classification, domain classification, General Classification, regression

Graph-based semi-supervised learning is a fundamental machine learning
problem, and has been well studied. Most studies focus on homogeneous networks
(e.g. citation network, friend network). In the present paper, we propose the
Heterogeneous Embedding Label Propagation (HELP) algorithm, a graph-based
semi-supervised deep learning algorithm, for graphs that are characterized by
heterogeneous node types. Empirically, we demonstrate the effectiveness of this
method in domain classification tasks with Facebook user-domain interaction
graph, and compare the performance of the proposed HELP algorithm with the
state of the art algorithms. We show that the HELP algorithm improves the
predictive performance across multiple tasks, together with semantically
meaningful embedding that are discriminative for downstream classification or
regression tasks.
Learning models for visual 3D localization with implicit mapping
Authors: Dan Rosenbaum, Frederic Besse, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami
Date: 2018-07-04 00:00:00
Tasks: Minecraft, Visual Localization

We consider learning based methods for visual localization that do not
require the construction of explicit maps in the form of point clouds or
voxels. The goal is to learn an implicit representation of the environment at a
higher, more abstract level. We propose to use a generative approach based on
Generative Query Networks (GQNs, Eslami et al. 2018), asking the following
questions: 1) Can GQN capture more complex scenes than those it was originally
demonstrated on? 2) Can GQN be used for localization in those scenes? To study
this approach we consider procedurally generated Minecraft worlds, for which we
can generate images of complex 3D scenes along with camera pose coordinates. We
first show that GQNs, enhanced with a novel attention mechanism can capture the
structure of 3D scenes in Minecraft, as evidenced by their samples. We then
apply the models to the localization problem, comparing the results to a
discriminative baseline, and comparing the ways each approach captures the task
uncertainty.
Fuzzy Logic Interpretation of Quadratic Networks
Authors: Fenglei Fan, Ge Wang
Date: 2018-07-04 00:00:00

Over past several years, deep learning has achieved huge successes in various applications. However, such a data-driven approach is often criticized for lack of interpretability. Recently, we proposed artificial quadratic neural networks consisting of second-order neurons in potentially many layers. In each second-order neuron, a quadratic function is used in the place of the inner product in a traditional neuron, and then undergoes a nonlinear activation. With a single second-order neuron, any fuzzy logic operation, such as XOR, can be implemented. In this sense, any deep network constructed with quadratic neurons can be interpreted as a deep fuzzy logic system. Since traditional neural networks and second-order counterparts can represent each other and fuzzy logic operations are naturally implemented in second-order neural networks, it is plausible to explain how a deep neural network works with a second-order network as the system model. In this paper, we generalize and categorize fuzzy logic operations implementable with individual second-order neurons, and then perform statistical/information theoretic analyses of exemplary quadratic neural networks.
A Comparative Study on using Principle Component Analysis with Different Text Classifiers
Authors: Ahmed I. Taloba, D. A. Eisa, Safaa S. I. Ismail
Date: 2018-07-04 00:00:00
Tasks: General Classification, Text Categorization

Text categorization (TC) is the task of automatically organizing a set of
documents into a set of pre-defined categories. Over the last few years,
increased attention has been paid to the use of documents in digital form and
this makes text categorization becomes a challenging issue. The most
significant problem of text categorization is its huge number of features. Most
of these features are redundant, noisy and irrelevant that cause over fitting
with most of the classifiers. Hence, feature extraction is an important step to
improve the overall accuracy and the performance of the text classifiers. In
this paper, we will provide an overview of using principle component analysis
(PCA) as a feature extraction with various classifiers. It was observed that
the performance rate of the classifiers after using PCA to reduce the dimension
of data improved. Experiments are conducted on three UCI data sets, Classic03,
CNAE-9 and DBWorld e-mails. We compare the classification performance results
of using PCA with popular and well-known text classifiers. Results show that
using PCA encouragingly enhances classification performance on most of the
classifiers.
Deep Learning for Launching and Mitigating Wireless Jamming Attacks
Authors: Tugba Erpek, Yalin E. Sagduyu, Yi Shi
Date: 2018-07-03 00:00:00
Tasks: Deep Learning, Generative Adversarial Network

An adversarial machine learning approach is introduced to launch jamming
attacks on wireless communications and a defense strategy is presented. A
cognitive transmitter uses a pre-trained classifier to predict the current
channel status based on recent sensing results and decides whether to transmit
or not, whereas a jammer collects channel status and ACKs to build a deep
learning classifier that reliably predicts the next successful transmissions
and effectively jams them. This jamming approach is shown to reduce the
transmitter's performance much more severely compared with random or
sensing-based jamming. The deep learning classification scores are used by the
jammer for power control subject to an average power constraint. Next, a
generative adversarial network (GAN) is developed for the jammer to reduce the
time to collect the training dataset by augmenting it with synthetic samples.
As a defense scheme, the transmitter deliberately takes a small number of wrong
actions in spectrum access (in form of a causative attack against the jammer)
and therefore prevents the jammer from building a reliable classifier. The
transmitter systematically selects when to take wrong actions and adapts the
level of defense to mislead the jammer into making prediction errors and
consequently increase its throughput.
Deep Spatio-Temporal Random Fields for Efficient Video Segmentation
Authors: Siddhartha Chandra, Camille Couprie, Iasonas Kokkinos
Date: 2018-07-03 00:00:00
Proceeding: CVPR 2018 6
Tasks: Instance Segmentation, Semantic Segmentation, Structured Prediction, Video Segmentation, Video Semantic Segmentation, Video Understanding

In this work we introduce a time- and memory-efficient method for structured
prediction that couples neuron decisions across both space at time. We show
that we are able to perform exact and efficient inference on a densely
connected spatio-temporal graph by capitalizing on recent advances on deep
Gaussian Conditional Random Fields (GCRFs). Our method, called VideoGCRF is (a)
efficient, (b) has a unique global minimum, and (c) can be trained end-to-end
alongside contemporary deep networks for video understanding. We experiment
with multiple connectivity patterns in the temporal domain, and present
empirical improvements over strong baselines on the tasks of both semantic and
instance segmentation of videos.