pwc-archive/papers-with-abstracts huggingface dataset:
Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs
Authors: Ursula Challita, Aidin Ferdowsi, Mingzhe Chen, Walid Saad
Date: 2018-04-15 00:00:00
Tasks: BIG-bench Machine Learning, Management

Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be
integrated into future cellular networks as new aerial mobile users. Providing
cellular connectivity to UAVs will enable a myriad of applications ranging from
online video streaming to medical delivery. However, to enable a reliable
wireless connectivity for the UAVs as well as a secure operation, various
challenges need to be addressed such as interference management, mobility
management and handover, cyber-physical attacks, and authentication. In this
paper, the goal is to expose the wireless and security challenges that arise in
the context of UAV-based delivery systems, UAV-based real-time multimedia
streaming, and UAV-enabled intelligent transportation systems. To address such
challenges, artificial neural network (ANN) based solution schemes are
introduced. The introduced approaches enable the UAVs to adaptively exploit the
wireless system resources while guaranteeing a secure operation, in real-time.
Preliminary simulation results show the benefits of the introduced solutions
for each of the aforementioned cellular-connected UAV application use case.
SeGAN: Segmenting and Generating the Invisible
Authors: Kiana Ehsani, Roozbeh Mottaghi, Ali Farhadi
Date: 2017-03-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: Depth Estimation, Scene Understanding, Segmentation

Objects often occlude each other in scenes; Inferring their appearance beyond
their visible parts plays an important role in scene understanding, depth
estimation, object interaction and manipulation. In this paper, we study the
challenging problem of completing the appearance of occluded objects. Doing so
requires knowing which pixels to paint (segmenting the invisible parts of
objects) and what color to paint them (generating the invisible parts). Our
proposed novel solution, SeGAN, jointly optimizes for both segmentation and
generation of the invisible parts of objects. Our experimental results show
that: (a) SeGAN can learn to generate the appearance of the occluded parts of
objects; (b) SeGAN outperforms state-of-the-art segmentation baselines for the
invisible parts of objects; (c) trained on synthetic photo realistic images,
SeGAN can reliably segment natural images; (d) by reasoning about occluder
occludee relations, our method can infer depth layering.
Detecting Compressed Cleartext Traffic from Consumer Internet of Things Devices
Authors: Daniel Hahn, Noah Apthorpe, Nick Feamster
Date: 2018-05-07 00:00:00
Tasks: BIG-bench Machine Learning

Data encryption is the primary method of protecting the privacy of consumer
device Internet communications from network observers. The ability to
automatically detect unencrypted data in network traffic is therefore an
essential tool for auditing Internet-connected devices. Existing methods
identify network packets containing cleartext but cannot differentiate packets
containing encrypted data from packets containing compressed unencrypted data,
which can be easily recovered by reversing the compression algorithm. This
makes it difficult for consumer protection advocates to identify devices that
risk user privacy by sending sensitive data in a compressed unencrypted format.
Here, we present the first technique to automatically distinguish encrypted
from compressed unencrypted network transmissions on a per-packet basis. We
apply three machine learning models and achieve a maximum 66.9% accuracy with a
convolutional neural network trained on raw packet data. This result is a
baseline for this previously unstudied machine learning problem, which we hope
will motivate further attention and accuracy improvements. To facilitate
continuing research on this topic, we have made our training and test datasets
available to the public.
Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain
Authors: Larissa Heinrich, Jan Funke, Constantin Pape, Juan Nunez-Iglesias, Stephan Saalfeld
Date: 2018-05-07 00:00:00

Neural circuit reconstruction at single synapse resolution is increasingly
recognized as crucially important to decipher the function of biological
nervous systems. Volume electron microscopy in serial transmission or scanning
mode has been demonstrated to provide the necessary resolution to segment or
trace all neurites and to annotate all synaptic connections.
  Automatic annotation of synaptic connections has been done successfully in
near isotropic electron microscopy of vertebrate model organisms. Results on
non-isotropic data in insect models, however, are not yet on par with human
annotation.
  We designed a new 3D-U-Net architecture to optimally represent isotropic
fields of view in non-isotropic data. We used regression on a signed distance
transform of manually annotated synaptic clefts of the CREMI challenge dataset
to train this model and observed significant improvement over the state of the
art.
  We developed open source software for optimized parallel prediction on very
large volumetric datasets and applied our model to predict synaptic clefts in a
50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes
well to areas far away from where training data was available.
Real-time regression analysis with deep convolutional neural networks
Authors: E. A. Huerta, Daniel George, Zhizhen Zhao, Gabrielle Allen
Date: 2018-05-07 00:00:00
Tasks: Deep Learning, regression, Time Series, Time Series Analysis

We discuss the development of novel deep learning algorithms to enable
real-time regression analysis for time series data. We showcase the application
of this new method with a timely case study, and then discuss the applicability
of this approach to tackle similar challenges across science domains.
Image Super-Resolution via Dual-State Recurrent Networks
Authors: Wei Han, Shiyu Chang, Ding Liu, Mo Yu, Michael Witbrock, Thomas S. Huang
Date: 2018-05-07 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Super-Resolution, Super-Resolution

Advances in image super-resolution (SR) have recently benefited significantly
from rapid developments in deep neural networks. Inspired by these recent
discoveries, we note that many state-of-the-art deep SR architectures can be
reformulated as a single-state recurrent neural network (RNN) with finite
unfoldings. In this paper, we explore new structures for SR based on this
compact RNN view, leading us to a dual-state design, the Dual-State Recurrent
Network (DSRN). Compared to its single state counterparts that operate at a
fixed spatial resolution, DSRN exploits both low-resolution (LR) and
high-resolution (HR) signals jointly. Recurrent signals are exchanged between
these states in both directions (both LR to HR and HR to LR) via delayed
feedback. Extensive quantitative and qualitative evaluations on benchmark
datasets and on a recent challenge demonstrate that the proposed DSRN performs
favorably against state-of-the-art algorithms in terms of both memory
consumption and predictive accuracy.
Region-Based Classification of PolSAR Data Using Radial Basis Kernel Functions With Stochastic Distances
Authors: R. G. Negri, A. C. Frery, W. B. Silva, T. S. G. Mendes, L. V. Dutra
Date: 2018-05-07 00:00:00
Tasks: General Classification

Region-based classification of PolSAR data can be effectively performed by
seeking for the assignment that minimizes a distance between prototypes and
segments. Silva et al (2013) used stochastic distances between complex
multivariate Wishart models which, differently from other measures, are
computationally tractable. In this work we assess the robustness of such
approach with respect to errors in the training stage, and propose an extension
that alleviates such problems. We introduce robustness in the process by
incorporating a combination of radial basis kernel functions and stochastic
distances with Support Vector Machines (SVM). We consider several stochastic
distances between Wishart: Bhatacharyya, Kullback-Leibler, Chi-Square,
R\'{e}nyi, and Hellinger. We perform two case studies with PolSAR images, both
simulated and from actual sensors, and different classification scenarios to
compare the performance of Minimum Distance and SVM classification frameworks.
With this, we model the situation of imperfect training samples. We show that
SVM with the proposed kernel functions achieves better performance with respect
to Minimum Distance, at the expense of more computational resources and the
need of parameter tuning. Code and data are provided for reproducibility.
Holarchic Structures for Decentralized Deep Learning - A Performance Analysis
Authors: Evangelos Pournaras, Srivatsan Yadhunathan, Ada Diaconescu
Date: 2018-05-07 00:00:00
Tasks: Deep Learning, Hyperparameter Optimization, Management

Structure plays a key role in learning performance. In centralized
computational systems, hyperparameter optimization and regularization
techniques such as dropout are computational means to enhance learning
performance by adjusting the deep hierarchical structure. However, in
decentralized deep learning by the Internet of Things, the structure is an
actual network of autonomous interconnected devices such as smart phones that
interact via complex network protocols. Self-adaptation of the learning
structure is a challenge. Uncertainties such as network latency, node and link
failures or even bottlenecks by limited processing capacity and energy
availability can signif- icantly downgrade learning performance. Network
self-organization and self-management is complex, while it requires additional
computational and network resources that hinder the feasibility of
decentralized deep learning. In contrast, this paper introduces a self-adaptive
learning approach based on holarchic learning structures for exploring,
mitigating and boosting learning performance in distributed environments with
uncertainties. A large-scale performance analysis with 864000 experiments fed
with synthetic and real-world data from smart grid and smart city pilot
projects confirm the cost-effectiveness of holarchic structures for
decentralized deep learning.
Predicting Graph Categories from Structural Properties
Authors: James P. Canning, Emma E. Ingram, Sammantha Nowak-Wolff, Adriana M. Ortiz, Nesreen K. Ahmed, Ryan A. Rossi, Karl R. B. Schmitt, Sucheta Soundarajan
Date: 2018-05-07 00:00:00
Tasks: General Classification

This paper has been withdrawn from arXiv.org due to a disagreement among the authors related to several peer-review comments received prior to submission on arXiv.org. Even though the current version of this paper is withdrawn, there was no disagreement between authors on the novel work in this paper. One specific issue was the discussion of related work by Ikehara \& Clauset (found on page 8 of the previously posted version). Peer-review comments on a similar version made ALL authors aware that the discussion misrepresented their work prior to submission to arXiv.org. However, some authors choose to post to arXiv a minimally updated version without the consent of all authors or properly addressing this attribution issue. ================ Original Paper Abstract: Complex networks are often categorized according to the underlying phenomena that they represent such as molecular interactions, re-tweets, and brain activity. In this work, we investigate the problem of predicting the category (domain) of arbitrary networks. This includes complex networks from different domains as well as synthetically generated graphs from five different network models. A classification accuracy of $96.6\%$ is achieved using a random forest classifier with both real and synthetic networks. This work makes two important findings. First, our results indicate that complex networks from various domains have distinct structural properties that allow us to predict with high accuracy the category of a new previously unseen network. Second, synthetic graphs are trivial to classify as the classification model can predict with near-certainty the network model used to generate it. Overall, the results demonstrate that networks drawn from different domains (and network models) are trivial to distinguish using only a handful of simple structural properties.
Multichannel Distributed Local Pattern for Content Based Indexing and Retrieval
Authors: Sonakshi Mathur, Mallika Chaudhary, Hemant Verma, Murari Mandal, S. K. Vipparthi, Subrahmanyam Murala
Date: 2018-05-07 00:00:00
Tasks: Retrieval

A novel color feature descriptor, Multichannel Distributed Local Pattern
(MDLP) is proposed in this manuscript. The MDLP combines the salient features
of both local binary and local mesh patterns in the neighborhood. The
multi-distance information computed by the MDLP aids in robust extraction of
the texture arrangement. Further, MDLP features are extracted for each color
channel of an image. The retrieval performance of the MDLP is evaluated on the
three benchmark datasets for CBIR, namely Corel-5000, Corel-10000 and MIT-Color
Vistex respectively. The proposed technique attains substantial improvement as
compared to other state-of- the-art feature descriptors in terms of various
evaluation parameters such as ARP and ARR on the respective databases.
Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds
Authors: Santosh Vempala, John Wilmes
Date: 2018-05-07 00:00:00

We study the complexity of training neural network models with one hidden nonlinear activation layer and an output weighted sum layer. We analyze Gradient Descent applied to learning a bounded target function on $n$ real-valued inputs. We give an agnostic learning guarantee for GD: starting from a randomly initialized network, it converges in mean squared loss to the minimum error (in $2$-norm) of the best approximation of the target function using a polynomial of degree at most $k$. Moreover, for any $k$, the size of the network and number of iterations needed are both bounded by $n^{O(k)}\log(1/\epsilon)$. In particular, this applies to training networks of unbiased sigmoids and ReLUs. We also rigorously explain the empirical finding that gradient descent discovers lower frequency Fourier components before higher frequency components. We complement this result with nearly matching lower bounds in the Statistical Query model. GD fits well in the SQ framework since each training step is determined by an expectation over the input distribution. We show that any SQ algorithm that achieves significant improvement over a constant function with queries of tolerance some inverse polynomial in the input dimensionality $n$ must use $n^{\Omega(k)}$ queries even when the target functions are restricted to a set of $n^{O(k)}$ degree-$k$ polynomials, and the input distribution is uniform over the unit sphere; for this class the information-theoretic lower bound is only $\Theta(k \log n)$. Our approach for both parts is based on spherical harmonics. We view gradient descent as an operator on the space of functions, and study its dynamics. An essential tool is the Funk-Hecke theorem, which explains the eigenfunctions of this operator in the case of the mean squared loss.
Wavelet Decomposition of Gradient Boosting
Authors: Shai Dekel, Oren Elisha, Ohad Morgan
Date: 2018-05-07 00:00:00

In this paper we introduce a significant improvement to the popular tree-based Stochastic Gradient Boosting algorithm using a wavelet decomposition of the trees. This approach is based on harmonic analysis and approximation theoretical elements, and as we show through extensive experimentation, our wavelet based method generally outperforms existing methods, particularly in difficult scenarios of class unbalance and mislabeling in the training data.
Label Refinery: Improving ImageNet Classification through Label Progression
Authors: Hessam Bagherinezhad, Maxwell Horton, Mohammad Rastegari, Ali Farhadi
Date: 2018-05-07 00:00:00
Tasks: Classification, General Classification

Among the three main components (data, labels, and models) of any supervised
learning system, data and models have been the main subjects of active
research. However, studying labels and their properties has received very
little attention. Current principles and paradigms of labeling impose several
challenges to machine learning algorithms. Labels are often incomplete,
ambiguous, and redundant. In this paper we study the effects of various
properties of labels and introduce the Label Refinery: an iterative procedure
that updates the ground truth labels after examining the entire dataset. We
show significant gain using refined labels across a wide range of models. Using
a Label Refinery improves the state-of-the-art top-1 accuracy of (1) AlexNet
from 59.3 to 67.2, (2) MobileNet from 70.6 to 73.39, (3) MobileNet-0.25 from
50.6 to 55.59, (4) VGG19 from 72.7 to 75.46, and (5) Darknet19 from 72.9 to
74.47.
Procedural Content Generation via Machine Learning (PCGML)
Authors: Adam Summerville, Sam Snodgrass, Matthew Guzdial, Christoffer Holmgård, Amy K. Hoover, Aaron Isaksen, Andy Nealen, Julian Togelius
Date: 2017-02-02 00:00:00
Tasks: BIG-bench Machine Learning, Card Games, Clustering, Style Transfer

This survey explores Procedural Content Generation via Machine Learning
(PCGML), defined as the generation of game content using machine learning
models trained on existing content. As the importance of PCG for game
development increases, researchers explore new avenues for generating
high-quality content with or without human involvement; this paper addresses
the relatively new paradigm of using machine learning (in contrast with
search-based, solver-based, and constructive methods). We focus on what is most
often considered functional game content such as platformer levels, game maps,
interactive fiction stories, and cards in collectible card games, as opposed to
cosmetic content such as sprites and sound effects. In addition to using PCG
for autonomous generation, co-creativity, mixed-initiative design, and
compression, PCGML is suited for repair, critique, and content analysis because
of its focus on modeling existing content. We discuss various data sources and
representations that affect the resulting generated content. Multiple PCGML
methods are covered, including neural networks, long short-term memory (LSTM)
networks, autoencoders, and deep convolutional networks; Markov models,
$n$-grams, and multi-dimensional Markov chains; clustering; and matrix
factorization. Finally, we discuss open problems in the application of PCGML,
including learning from small datasets, lack of training data, multi-layered
learning, style-transfer, parameter tuning, and PCG as a game mechanic.
Constructive Preference Elicitation over Hybrid Combinatorial Spaces
Authors: Paolo Dragone, Stefano Teso, Andrea Passerini
Date: 2017-11-21 00:00:00

Preference elicitation is the task of suggesting a highly preferred
configuration to a decision maker. The preferences are typically learned by
querying the user for choice feedback over pairs or sets of objects. In its
constructive variant, new objects are synthesized "from scratch" by maximizing
an estimate of the user utility over a combinatorial (possibly infinite) space
of candidates. In the constructive setting, most existing elicitation
techniques fail because they rely on exhaustive enumeration of the candidates.
A previous solution explicitly designed for constructive tasks comes with no
formal performance guarantees, and can be very expensive in (or unapplicable
to) problems with non-Boolean attributes. We propose the Choice Perceptron, a
Perceptron-like algorithm for learning user preferences from set-wise choice
feedback over constructive domains and hybrid Boolean-numeric feature spaces.
We provide a theoretical analysis on the attained regret that holds for a large
class of query selection strategies, and devise a heuristic strategy that aims
at optimizing the regret in practice. Finally, we demonstrate its effectiveness
by empirical evaluation against existing competitors on constructive scenarios
of increasing complexity.
Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading
Authors: Matthew F. Dixon, Nicholas G. Polson, Vadim O. Sokolov
Date: 2017-05-27 00:00:00
Tasks: Deep Learning, General Classification

Deep learning applies hierarchical layers of hidden variables to construct
nonlinear high dimensional predictors. Our goal is to develop and train deep
learning architectures for spatio-temporal modeling. Training a deep
architecture is achieved by stochastic gradient descent (SGD) and drop-out (DO)
for parameter regularization with a goal of minimizing out-of-sample predictive
mean squared error. To illustrate our methodology, we predict the sharp
discontinuities in traffic flow data, and secondly, we develop a classification
rule to predict short-term futures market prices as a function of the order
book depth. Finally, we conclude with directions for future research.
Correlated Components Analysis - Extracting Reliable Dimensions in Multivariate Data
Authors: Lucas C. Parra, Stefan Haufe, Jacek P. Dmochowski
Date: 2018-01-26 00:00:00

How does one find dimensions in multivariate data that are reliably expressed
across repetitions? For example, in a brain imaging study one may want to
identify combinations of neural signals that are reliably expressed across
multiple trials or subjects. For a behavioral assessment with multiple ratings,
one may want to identify an aggregate score that is reliably reproduced across
raters. Correlated Components Analysis (CorrCA) addresses this problem by
identifying components that are maximally correlated between repetitions (e.g.
trials, subjects, raters). Here we formalize this as the maximization of the
ratio of between-repetition to within-repetition covariance. We show that this
criterion maximizes repeat-reliability, defined as mean over variance across
repeats, and that it leads to CorrCA or to multi-set Canonical Correlation
Analysis, depending on the constraints. Surprisingly, we also find that CorrCA
is equivalent to Linear Discriminant Analysis for zero-mean signals, which
provides an unexpected link between classic concepts of multivariate analysis.
We present an exact parametric test of statistical significance based on the
F-statistic for normally distributed independent samples, and present and
validate shuffle statistics for the case of dependent samples. Regularization
and extension to non-linear mappings using kernels are also presented. The
algorithms are demonstrated on a series of data analysis applications, and we
provide all code and data required to reproduce the results.