pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Task Allocation for Mobile Edge Learning
Authors: Umair Mohammad, Sameh Sorour
Date: 2018-11-09 00:00:00

This paper aims to establish a new optimization paradigm for implementing
realistic distributed learning algorithms, with performance guarantees, on
wireless edge nodes with heterogeneous computing and communication capacities.
We will refer to this new paradigm as `Mobile Edge Learning (MEL)'. The problem
of dynamic task allocation for MEL is considered in this paper with the aim to
maximize the learning accuracy, while guaranteeing that the total times of data
distribution/aggregation over heterogeneous channels, and local computing
iterations at the heterogeneous nodes, are bounded by a preset duration. The
problem is first formulated as a quadratically-constrained integer linear
problem. Being an NP-hard problem, the paper relaxes it into a non-convex
problem over real variables. We thus proposed two solutions based on deriving
analytical upper bounds of the optimal solution of this relaxed problem using
Lagrangian analysis and KKT conditions, and the use of suggest-and-improve
starting from equal batch allocation, respectively. The merits of these
proposed solutions are exhibited by comparing their performances to both
numerical approaches and the equal task allocation approach.
Density estimation for shift-invariant multidimensional distributions
Authors: Anindya De, Philip M. Long, Rocco A. Servedio
Date: 2018-11-09 00:00:00
Tasks: Density Estimation

We study density estimation for classes of shift-invariant distributions over
$\mathbb{R}^d$. A multidimensional distribution is "shift-invariant" if,
roughly speaking, it is close in total variation distance to a small shift of
it in any direction. Shift-invariance relaxes smoothness assumptions commonly
used in non-parametric density estimation to allow jump discontinuities. The
different classes of distributions that we consider correspond to different
rates of tail decay.
  For each such class we give an efficient algorithm that learns any
distribution in the class from independent samples with respect to total
variation distance. As a special case of our general result, we show that
$d$-dimensional shift-invariant distributions which satisfy an exponential tail
bound can be learned to total variation distance error $\epsilon$ using
$\tilde{O}_d(1/ \epsilon^{d+2})$ examples and $\tilde{O}_d(1/ \epsilon^{2d+2})$
time. This implies that, for constant $d$, multivariate log-concave
distributions can be learned in $\tilde{O}_d(1/\epsilon^{2d+2})$ time using
$\tilde{O}_d(1/\epsilon^{d+2})$ samples, answering a question of [Diakonikolas,
Kane and Stewart, 2016] All of our results extend to a model of noise-tolerant
density estimation using Huber's contamination model, in which the target
distribution to be learned is a $(1-\epsilon,\epsilon)$ mixture of some unknown
distribution in the class with some other arbitrary and unknown distribution,
and the learning algorithm must output a hypothesis distribution with total
variation distance error $O(\epsilon)$ from the target distribution. We show
that our general results are close to best possible by proving a simple
$\Omega\left(1/\epsilon^d\right)$ information-theoretic lower bound on sample
complexity even for learning bounded distributions that are shift-invariant.
Analysis of Fleet Modularity in an Artificial Intelligence-Based Attacker-Defender Game
Authors: Xingyu Li, Bogdan I. Epureanu
Date: 2018-11-09 00:00:00
Tasks: Decision Making

Because combat environments change over time and technology upgrades are
widespread for ground vehicles, a large number of vehicles and equipment become
quickly obsolete. A possible solution for the U.S. Army is to develop fleets of
modular military vehicles, which are built by interchangeable substantial
components also known as modules. One of the typical characteristics of module
is their ease of assembly and disassembly through simple means such as
plug-in/pull-out actions, which allows for real-time fleet reconfiguration to
meet dynamic demands. Moreover, military demands are time-varying and highly
stochastic because commanders keep reacting to enemy's actions. To capture
these characteristics, we formulated an intelligent agent-based model to
imitate decision making process during fleet operation, which combines
real-time optimization with artificial intelligence. The agents are capable of
inferring enemy's future move based on historical data and optimize
dispatch/operation decisions accordingly. We implement our model to simulate an
attacker-defender game between two adversarial and intelligent players,
representing the commanders from modularized fleet and conventional fleet
respectively. Given the same level of combat resources and intelligence, we
highlight the tactical advantages of fleet modularity in terms of win rate,
unpredictability and suffered damage.
Securing Behavior-based Opinion Spam Detection
Authors: Shuaijun Ge, Guixiang Ma, Sihong Xie, Philip S. Yu
Date: 2018-11-09 00:00:00
Tasks: Spam detection

Reviews spams are prevalent in e-commerce to manipulate product ranking and
customers decisions maliciously. While spams generated based on simple spamming
strategy can be detected effectively, hardened spammers can evade regular
detectors via more advanced spamming strategies. Previous work gave more
attention to evasion against text and graph-based detectors, but evasions
against behavior-based detectors are largely ignored, leading to
vulnerabilities in spam detection systems. Since real evasion data are scarce,
we first propose EMERAL (Evasion via Maximum Entropy and Rating sAmpLing) to
generate evasive spams to certain existing detectors. EMERAL can simulate
spammers with different goals and levels of knowledge about the detectors,
targeting at different stages of the life cycle of target products. We show
that in the evasion-defense dynamic, only a few evasion types are meaningful to
the spammers, and any spammer will not be able to evade too many detection
signals at the same time. We reveal that some evasions are quite insidious and
can fail all detection signals. We then propose DETER (Defense via Evasion
generaTion using EmeRal), based on model re-training on diverse evasive samples
generated by EMERAL. Experiments confirm that DETER is more accurate in
detecting both suspicious time window and individual spamming reviews. In terms
of security, DETER is versatile enough to be vaccinated against diverse and
unexpected evasions, is agnostic about evasion strategy and can be released
without privacy concern.
Semantic and Contrast-Aware Saliency
Authors: Xiaoshuai Sun
Date: 2018-11-09 00:00:00
Tasks: Saliency Prediction

In this paper, we proposed an integrated model of semantic-aware and
contrast-aware saliency combining both bottom-up and top-down cues for
effective saliency estimation and eye fixation prediction. The proposed model
processes visual information using two pathways. The first pathway aims to
capture the attractive semantic information in images, especially for the
presence of meaningful objects and object parts such as human faces. The second
pathway is based on multi-scale on-line feature learning and information
maximization, which learns an adaptive sparse representation for the input and
discovers the high contrast salient patterns within the image context. The two
pathways characterize both long-term and short-term attention cues and are
integrated dynamically using maxima normalization. We investigate two different
implementations of the semantic pathway including an End-to-End deep neural
network solution and a dynamic feature integration solution, resulting in the
SCA and SCAFI model respectively. Experimental results on artificial images and
5 popular benchmark datasets demonstrate the superior performance and better
plausibility of the proposed model over both classic approaches and recent deep
models.
Universal Decision-Based Black-Box Perturbations: Breaking Security-Through-Obscurity Defenses
Authors: Thomas A. Hogan, Bhavya Kailkhura
Date: 2018-11-09 00:00:00

We study the problem of finding a universal (image-agnostic) perturbation to
fool machine learning (ML) classifiers (e.g., neural nets, decision tress) in
the hard-label black-box setting. Recent work in adversarial ML in the
white-box setting (model parameters are known) has shown that many
state-of-the-art image classifiers are vulnerable to universal adversarial
perturbations: a fixed human-imperceptible perturbation that, when added to any
image, causes it to be misclassified with high probability Kurakin et al.
[2016], Szegedy et al. [2013], Chen et al. [2017a], Carlini and Wagner [2017].
This paper considers a more practical and challenging problem of finding such
universal perturbations in an obscure (or black-box) setting. More
specifically, we use zeroth order optimization algorithms to find such a
universal adversarial perturbation when no model information is revealed-except
that the attacker can make queries to probe the classifier. We further relax
the assumption that the output of a query is continuous valued confidence
scores for all the classes and consider the case where the output is a
hard-label decision. Surprisingly, we found that even in these extremely
obscure regimes, state-of-the-art ML classifiers can be fooled with a very high
probability just by adding a single human-imperceptible image perturbation to
any natural image. The surprising existence of universal perturbations in a
hard-label black-box setting raises serious security concerns with the
existence of a universal noise vector that adversaries can possibly exploit to
break a classifier on most natural images.
Incorporating Relevant Knowledge in Context Modeling and Response Generation
Authors: Yan-ran Li, Wenjie Li, Ziqiang Cao, Chengyao Chen
Date: 2018-11-09 00:00:00
Tasks: Attribute, Chatbot, Decoder, Response Generation

To sustain engaging conversation, it is critical for chatbots to make good
use of relevant knowledge. Equipped with a knowledge base, chatbots are able to
extract conversation-related attributes and entities to facilitate context
modeling and response generation. In this work, we distinguish the uses of
attribute and entity and incorporate them into the encoder-decoder architecture
in different manners. Based on the augmented architecture, our chatbot, namely
Mike, is able to generate responses by referring to proper entities from the
collected knowledge. To validate the proposed approach, we build a movie
conversation corpus on which the proposed approach significantly outperforms
other four knowledge-grounded models.
Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering
Authors: Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, Biplav Srivastava
Date: 2018-11-09 00:00:00
Tasks: Clustering

While machine learning (ML) models are being increasingly trusted to make
decisions in different and varying areas, the safety of systems using such
models has become an increasing concern. In particular, ML models are often
trained on data from potentially untrustworthy sources, providing adversaries
with the opportunity to manipulate them by inserting carefully crafted samples
into the training set. Recent work has shown that this type of attack, called a
poisoning attack, allows adversaries to insert backdoors or trojans into the
model, enabling malicious behavior with simple external backdoor triggers at
inference time and only a blackbox perspective of the model itself. Detecting
this type of attack is challenging because the unexpected behavior occurs only
when a backdoor trigger, which is known only to the adversary, is present.
Model users, either direct users of training data or users of pre-trained model
from a catalog, may not guarantee the safe operation of their ML-based system.
In this paper, we propose a novel approach to backdoor detection and removal
for neural networks. Through extensive experimental results, we demonstrate its
effectiveness for neural networks classifying text and images. To the best of
our knowledge, this is the first methodology capable of detecting poisonous
data crafted to insert backdoors and repairing the model that does not require
a verified and trusted dataset.
Learning Energy Based Inpainting for Optical Flow
Authors: Christoph Vogel, Patrick Knöbelreiter, Thomas Pock
Date: 2018-11-09 00:00:00
Tasks: feature selection, Optical Flow Estimation

Modern optical flow methods are often composed of a cascade of many
independent steps or formulated as a black box neural network that is hard to
interpret and analyze. In this work we seek for a plain, interpretable, but
learnable solution. We propose a novel inpainting based algorithm that
approaches the problem in three steps: feature selection and matching,
selection of supporting points and energy based inpainting. To facilitate the
inference we propose an optimization layer that allows to backpropagate through
10K iterations of a first-order method without any numerical or memory
problems. Compared to recent state-of-the-art networks, our modular CNN is very
lightweight and competitive with other, more involved, inpainting based
methods.
Fast determinantal point processes via distortion-free intermediate sampling
Authors: Michał Dereziński
Date: 2018-11-08 00:00:00
Tasks: Data Summarization, Point Processes, Stochastic Optimization

Given a fixed $n\times d$ matrix $\mathbf{X}$, where $n\gg d$, we study the
complexity of sampling from a distribution over all subsets of rows where the
probability of a subset is proportional to the squared volume of the
parallelepiped spanned by the rows (a.k.a. a determinantal point process). In
this task, it is important to minimize the preprocessing cost of the procedure
(performed once) as well as the sampling cost (performed repeatedly). To that
end, we propose a new determinantal point process algorithm which has the
following two properties, both of which are novel: (1) a preprocessing step
which runs in time $O(\text{number-of-non-zeros}(\mathbf{X})\cdot\log
n)+\text{poly}(d)$, and (2) a sampling step which runs in $\text{poly}(d)$
time, independent of the number of rows $n$. We achieve this by introducing a
new regularized determinantal point process (R-DPP), which serves as an
intermediate distribution in the sampling procedure by reducing the number of
rows from $n$ to $\text{poly}(d)$. Crucially, this intermediate distribution
does not distort the probabilities of the target sample. Our key novelty in
defining the R-DPP is the use of a Poisson random variable for controlling the
probabilities of different subset sizes, leading to new determinantal formulas
such as the normalization constant for this distribution. Our algorithm has
applications in many diverse areas where determinantal point processes have
been used, such as machine learning, stochastic optimization, data
summarization and low-rank matrix reconstruction.
Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series
Authors: Qiang Zhang, Rui Luo, Yaodong Yang, Yuanyuan Liu
Date: 2018-11-08 00:00:00
Tasks: Benchmarking, Decision Making, Time Series, Time Series Analysis

Volatility is a quantity of measurement for the price movements of stocks or
options which indicates the uncertainty within financial markets. As an
indicator of the level of risk or the degree of variation, volatility is
important to analyse the financial market, and it is taken into consideration
in various decision-making processes in financial activities. On the other
hand, recent advancement in deep learning techniques has shown strong
capabilities in modelling sequential data, such as speech and natural language.
In this paper, we empirically study the applicability of the latest deep
structures with respect to the volatility modelling problem, through which we
aim to provide an empirical guidance for the theoretical analysis of the
marriage between deep learning techniques and financial applications in the
future. We examine both the traditional approaches and the deep sequential
models on the task of volatility prediction, including the most recent variants
of convolutional and recurrent networks, such as the dilated architecture.
Accordingly, experiments with real-world stock price datasets are performed on
a set of 1314 daily stock series for 2018 days of transaction. The evaluation
and comparison are based on the negative log likelihood (NLL) of real-world
stock price time series. The result shows that the dilated neural models,
including dilated CNN and Dilated RNN, produce most accurate estimation and
prediction, outperforming various widely-used deterministic models in the GARCH
family and several recently proposed stochastic models. In addition, the high
flexibility and rich expressive power are validated in this study.
Validating Hyperspectral Image Segmentation
Authors: Jakub Nalepa, Michal Myller, Michal Kawulok
Date: 2018-11-08 00:00:00
Tasks: Hyperspectral Image Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

Hyperspectral satellite imaging attracts enormous research attention in the
remote sensing community, hence automated approaches for precise segmentation
of such imagery are being rapidly developed. In this letter, we share our
observations on the strategy for validating hyperspectral image segmentation
algorithms currently followed in the literature, and show that it can lead to
over-optimistic experimental insights. We introduce a new routine for
generating segmentation benchmarks, and use it to elaborate ready-to-use
hyperspectral training-test data partitions. They can be utilized for fair
validation of new and existing algorithms without any training-test data
leakage.
A Comparison of Lattice-free Discriminative Training Criteria for Purely Sequence-Trained Neural Network Acoustic Models
Authors: Chao Weng, Dong Yu
Date: 2018-11-08 00:00:00

In this work, three lattice-free (LF) discriminative training criteria for
purely sequence-trained neural network acoustic models are compared on LVCSR
tasks, namely maximum mutual information (MMI), boosted maximum mutual
information (bMMI) and state-level minimum Bayes risk (sMBR). We demonstrate
that, analogous to LF-MMI, a neural network acoustic model can also be trained
from scratch using LF-bMMI or LF-sMBR criteria respectively without the need of
cross-entropy pre-training. Furthermore, experimental results on
Switchboard-300hrs and Switchboard+Fisher-2100hrs datasets show that models
trained with LF-bMMI consistently outperform those trained with plain LF-MMI
and achieve a relative word error rate (WER) reduction of 5% over competitive
temporal convolution projected LSTM (TDNN-LSTMP) LF-MMI baselines.
Deep Learning Predicts Hip Fracture using Confounding Patient and Healthcare Variables
Authors: Marcus A. Badgeley, John R. Zech, Luke Oakden-Rayner, Benjamin S. Glicksberg, Manway Liu, William Gale, Michael V. McConnell, Beth Percha, Thomas M. Snyder, Joel T. Dudley
Date: 2018-11-08 00:00:00
Tasks: Deep Learning

Hip fractures are a leading cause of death and disability among older adults.
Hip fractures are also the most commonly missed diagnosis on pelvic
radiographs. Computer-Aided Diagnosis (CAD) algorithms have shown promise for
helping radiologists detect fractures, but the image features underpinning
their predictions are notoriously difficult to understand. In this study, we
trained deep learning models on 17,587 radiographs to classify fracture, five
patient traits, and 14 hospital process variables. All 20 variables could be
predicted from a radiograph (p < 0.05), with the best performances on scanner
model (AUC=1.00), scanner brand (AUC=0.98), and whether the order was marked
"priority" (AUC=0.79). Fracture was predicted moderately well from the image
(AUC=0.78) and better when combining image features with patient data
(AUC=0.86, p=2e-9) or patient data plus hospital process features (AUC=0.91,
p=1e-21). The model performance on a test set with matched patient variables
was significantly lower than a random test set (AUC=0.67, p=0.003); and when
the test set was matched on patient and image acquisition variables, the model
performed randomly (AUC=0.52, 95% CI 0.46-0.58), indicating that these
variables were the main source of the model's predictive ability overall. We
also used Naive Bayes to combine evidence from image models with patient and
hospital data and found their inclusion improved performance, but that this
approach was nevertheless inferior to directly modeling all variables. If CAD
algorithms are inexplicably leveraging patient and process variables in their
predictions, it is unclear how radiologists should interpret their predictions
in the context of other known patient data. Further research is needed to
illuminate deep learning decision processes so that computers and clinicians
can effectively cooperate.
Mode matching in GANs through latent space learning and inversion
Authors: Deepak Mishra, Prathosh A. P., Aravind Jayendran, Varun Srivastava, Santanu Chaudhury
Date: 2018-11-08 00:00:00
Tasks: Attribute

Generative adversarial networks (GANs) have shown remarkable success in
generation of unstructured data, such as, natural images. However, discovery
and separation of modes in the generated space, essential for several tasks
beyond naive data generation, is still a challenge. In this paper, we address
the problem of imposing desired modal properties on the generated space using a
latent distribution, engineered in accordance with the modal properties of the
true data distribution. This is achieved by training a latent space inversion
network in tandem with the generative network using a divergence loss. The
latent space is made to follow a continuous multimodal distribution generated
by reparameterization of a pair of continuous and discrete random variables. In
addition, the modal priors of the latent distribution are learned to match with
the true data distribution using minimal-supervision with negligible increment
in number of learnable parameters. We validate our method on multiple tasks
such as mode separation, conditional generation, and attribute discovery on
multiple real world image datasets and demonstrate its efficacy over other
state-of-the-art methods.
Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?
Authors: Hongming Shan, Atul Padole, Fatemeh Homayounieh, Uwe Kruger, Ruhani Doda Khera, Chayanin Nitiwarangkul, Mannudeep K. Kalra, Ge Wang
Date: 2018-11-08 00:00:00
Tasks: CT Reconstruction, Deep Learning, Denoising, Image Reconstruction

Commercial iterative reconstruction techniques on modern CT scanners target
radiation dose reduction but there are lingering concerns over their impact on
image appearance and low contrast detectability. Recently, machine learning,
especially deep learning, has been actively investigated for CT. Here we design
a novel neural network architecture for low-dose CT (LDCT) and compare it with
commercial iterative reconstruction methods used for standard of care CT. While
popular neural networks are trained for end-to-end mapping, driven by big data,
our novel neural network is intended for end-to-process mapping so that
intermediate image targets are obtained with the associated search gradients
along which the final image targets are gradually reached. This learned dynamic
process allows to include radiologists in the training loop to optimize the
LDCT denoising workflow in a task-specific fashion with the denoising depth as
a key parameter. Our progressive denoising network was trained with the Mayo
LDCT Challenge Dataset, and tested on images of the chest and abdominal regions
scanned on the CT scanners made by three leading CT vendors. The best deep
learning based reconstructions are systematically compared to the best
iterative reconstructions in a double-blinded reader study. It is found that
our deep learning approach performs either comparably or favorably in terms of
noise suppression and structural fidelity, and runs orders of magnitude faster
than the commercial iterative CT reconstruction algorithms.