pwc-archive/papers-with-abstracts huggingface dataset:
Rich Character-Level Information for Korean Morphological Analysis and Part-of-Speech Tagging
Authors: Andrew Matteson, Chanhee Lee, Young-Bum Kim, Heuiseok Lim
Date: 2018-06-28 00:00:00
Proceeding: COLING 2018 8
Tasks: Morphological Analysis, Part-Of-Speech Tagging, Sentence

Due to the fact that Korean is a highly agglutinative, character-rich
language, previous work on Korean morphological analysis typically employs the
use of sub-character features known as graphemes or otherwise utilizes
comprehensive prior linguistic knowledge (i.e., a dictionary of known
morphological transformation forms, or actions). These models have been created
with the assumption that character-level, dictionary-less morphological
analysis was intractable due to the number of actions required. We present, in
this study, a multi-stage action-based model that can perform morphological
transformation and part-of-speech tagging using arbitrary units of input and
apply it to the case of character-level Korean morphological analysis. Among
models that do not employ prior linguistic knowledge, we achieve
state-of-the-art word and sentence-level tagging accuracy with the Sejong
Korean corpus using our proposed data-driven Bi-LSTM model.
State-aware Anti-drift Robust Correlation Tracking
Authors: Yuqi Han, Chenwei Deng, Zengshuo Zhang, Jinghong Nan, Baojun Zhao
Date: 2018-06-28 00:00:00
Tasks: Visual Tracking

Correlation filter (CF) based trackers have aroused increasing attentions in
visual tracking field due to the superior performance on several datasets while
maintaining high running speed. For each frame, an ideal filter is trained in
order to discriminate the target from its surrounding background. Considering
that the target always undergoes external and internal interference during
tracking procedure, the trained filter should take consideration of not only
the external distractions but also the target appearance variation
synchronously. To this end, we present a State-aware Anti-drift Tracker (SAT)
in this paper, which jointly model the discrimination and reliability
information in filter learning. Specifically, global context patches are
incorporated into filter training stage to better distinguish the target from
backgrounds. Meanwhile, a color-based reliable mask is learned to encourage the
filter to focus on more reliable regions suitable for tracking. We show that
the proposed optimization problem could be efficiently solved using Alternative
Direction Method of Multipliers and fully carried out in Fourier domain.
Extensive experiments are conducted on OTB-100 datasets to compare the SAT
tracker (both hand-crafted feature and CNN feature) with other relevant
state-of-the-art methods. Both quantitative and qualitative evaluations further
demonstrate the effectiveness and robustness of the proposed work.
A Benchmark for Interpretability Methods in Deep Neural Networks
Authors: Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim
Date: 2018-06-28 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Feature Importance, image-classification, Image Classification

We propose an empirical measure of the approximate accuracy of feature importance estimates in deep neural networks. Our results across several large-scale image classification datasets show that many popular interpretability methods produce estimates of feature importance that are not better than a random designation of feature importance. Only certain ensemble based approaches---VarGrad and SmoothGrad-Squared---outperform such a random assignment of importance. The manner of ensembling remains critical, we show that some approaches do no better then the underlying method but carry a far higher computational burden.
Robust Fuzzy-Learning For Partially Overlapping Channels Allocation In UAV Communication Networks
Authors: Chaoqiong Fan, Bin Li, Jia Hou, Yi Wu, Weisi Guo, Chenglin Zhao
Date: 2018-06-28 00:00:00

In this paper, we consider a mesh-structured unmanned aerial vehicle (UAV)
networks exploiting partially overlapping channels (POCs). For general
data-collection tasks in UAV networks, we aim to optimize the network
throughput with constraints on transmission power and quality of service (QoS).
As far as the highly mobile and constantly changing UAV networks are concerned,
unfortunately, most existing methods rely on definite information which is
vulnerable to the dynamic environment, rendering system performance to be less
effective. In order to combat dynamic topology and varying interference of UAV
networks, a robust and distributed learning scheme is proposed. Rather than the
perfect channel state information (CSI), we introduce uncertainties to
characterize the dynamic channel gains among UAV nodes, which are then
interpreted with fuzzy numbers. Instead of the traditional observation space
where the channel capacity is a crisp reward, we implement the learning and
decision process in a mapped fuzzy space. This allows the system to achieve a
smoother and more robust performance by optimizing in an alternate space. To
this end, we design a fuzzy payoffs function (FPF) to describe the fluctuated
utility, and the problem of POCs assignment is formulated as a fuzzy payoffs
game (FPG). Assisted by an attractive property of fuzzy bi-matrix games, the
existence of fuzzy Nash equilibrium (FNE) for our formulated FPG is proved. Our
robust fuzzy-learning algorithm could reach the equilibrium solution via a
least-deviation method. Finally, numerical simulations are provided to
demonstrate the advantages of our new scheme over the existing scheme.
A Computational Theory for Life-Long Learning of Semantics
Authors: Peter Sutor Jr., Douglas Summers-Stay, Yiannis Aloimonos
Date: 2018-06-28 00:00:00

Semantic vectors are learned from data to express semantic relationships
between elements of information, for the purpose of solving and informing
downstream tasks. Other models exist that learn to map and classify supervised
data. However, the two worlds of learning rarely interact to inform one another
dynamically, whether across types of data or levels of semantics, in order to
form a unified model. We explore the research problem of learning these vectors
and propose a framework for learning the semantics of knowledge incrementally
and online, across multiple mediums of data, via binary vectors. We discuss the
aspects of this framework to spur future research on this approach and problem.
Towards automatic initialization of registration algorithms using simulated endoscopy images
Authors: Ayushi Sinha, Masaru Ishii, Russell H. Taylor, Gregory D. Hager, Austin Reiter
Date: 2018-06-28 00:00:00
Tasks: Anatomy, Scene Classification

Registering images from different modalities is an active area of research in
computer aided medical interventions. Several registration algorithms have been
developed, many of which achieve high accuracy. However, these results are
dependent on many factors, including the quality of the extracted features or
segmentations being registered as well as the initial alignment. Although
several methods have been developed towards improving segmentation algorithms
and automating the segmentation process, few automatic initialization
algorithms have been explored. In many cases, the initial alignment from which
a registration is initiated is performed manually, which interferes with the
clinical workflow. Our aim is to use scene classification in endoscopic
procedures to achieve coarse alignment of the endoscope and a preoperative
image of the anatomy. In this paper, we show using simulated scenes that a
neural network can predict the region of anatomy (with respect to a
preoperative image) that the endoscope is located in by observing a single
endoscopic video frame. With limited training and without any hyperparameter
tuning, our method achieves an accuracy of 76.53 (+/-1.19)%. There are several
avenues for improvement, making this a promising direction of research. Code is
available at https://github.com/AyushiSinha/AutoInitialization.
Two-layer Lossless HDR Coding considering Histogram Sparseness with Backward Compatibility to JPEG
Authors: Osamu Watanabe, Hiroyuki Kobayashi, Hitoshi Kiya
Date: 2018-06-28 00:00:00

An efficient two-layer coding method using the histogram packing technique
with the backward compatibility to the legacy JPEG is proposed in this paper.
The JPEG XT, which is the international standard to compress HDR images, adopts
two-layer coding scheme for backward compatibility to the legacy JPEG. However,
this two-layer coding structure does not give better lossless performance than
the other existing single-layer coding methods for HDR images. Moreover, the
JPEG XT has problems on determination of the lossless coding parameters;
Finding appropriate combination of the parameter values is necessary to achieve
good lossless performance. The histogram sparseness of HDR images is discussed
and it is pointed out that the histogram packing technique considering the
sparseness is able to improve the performance of lossless compression for HDR
images and a novel two-layer coding with the histogram packing technique is
proposed. The experimental results demonstrate that not only the proposed
method has a better lossless compression performance than that of the JPEG XT,
but also there is no need to determine image-dependent parameter values for
good compression performance in spite of having the backward compatibility to
the well known legacy JPEG standard.
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
Authors: Dylan J. Foster, Akshay Krishnamurthy
Date: 2018-06-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Multi-Armed Bandits, regression

We use surrogate losses to obtain several new regret bounds and new
algorithms for contextual bandit learning. Using the ramp loss, we derive new
margin-based regret bounds in terms of standard sequential complexity measures
of a benchmark class of real-valued regression functions. Using the hinge loss,
we derive an efficient algorithm with a $\sqrt{dT}$-type mistake bound against
benchmark policies induced by $d$-dimensional regressors. Under realizability
assumptions, our results also yield classical regret bounds.
Robust Neural Malware Detection Models for Emulation Sequence Learning
Authors: Rakshit Agrawal, Jack W. Stokes, Mady Marinescu, Karthik Selvaraj
Date: 2018-06-28 00:00:00
Tasks: Computer Security, Malware Classification, Malware Detection

Malicious software, or malware, presents a continuously evolving challenge in
computer security. These embedded snippets of code in the form of malicious
files or hidden within legitimate files cause a major risk to systems with
their ability to run malicious command sequences. Malware authors even use
polymorphism to reorder these commands and create several malicious variations.
However, if executed in a secure environment, one can perform early malware
detection on emulated command sequences.
  The models presented in this paper leverage this sequential data derived via
emulation in order to perform Neural Malware Detection. These models target the
core of the malicious operation by learning the presence and pattern of
co-occurrence of malicious event actions from within these sequences. Our
models can capture entire event sequences and be trained directly using the
known target labels. These end-to-end learning models are powered by two
commonly used structures - Long Short-Term Memory (LSTM) Networks and
Convolutional Neural Networks (CNNs). Previously proposed sequential malware
classification models process no more than 200 events. Attackers can evade
detection by delaying any malicious activity beyond the beginning of the file.
We present specialized models that can handle extremely long sequences while
successfully performing malware detection in an efficient way. We present an
implementation of the Convoluted Partitioning of Long Sequences approach in
order to tackle this vulnerability and operate on long sequences. We present
our results on a large dataset consisting of 634,249 file sequences, with
extremely long file sequences.
Risk-averse estimation, an axiomatic approach to inference, and Wallace-Freeman without MML
Authors: Michael Brand
Date: 2018-06-28 00:00:00

We define a new class of Bayesian point estimators, which we refer to as risk
averse. Using this definition, we formulate axioms that provide natural
requirements for inference, e.g. in a scientific setting, and show that for
well-behaved estimation problems the axioms uniquely characterise an estimator.
Namely, for estimation problems in which some parameter values have a positive
posterior probability (such as, e.g., problems with a discrete hypothesis
space), the axioms characterise Maximum A Posteriori (MAP) estimation, whereas
elsewhere (such as in continuous estimation) they characterise the
Wallace-Freeman estimator.
  Our results provide a novel justification for the Wallace-Freeman estimator,
which previously was derived only as an approximation to the
information-theoretic Strict Minimum Message Length estimator. By contrast, our
derivation requires neither approximations nor coding.
Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation
Authors: Niels Justesen, Ruben Rodriguez Torrado, Philip Bontrager, Ahmed Khalifa, Julian Togelius, Sebastian Risi
Date: 2018-06-28 00:00:00
Tasks: Clustering, Deep Reinforcement Learning, Dimensionality Reduction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning (RL) has shown impressive results in a variety of
domains, learning directly from high-dimensional sensory streams. However, when
neural networks are trained in a fixed environment, such as a single level in a
video game, they will usually overfit and fail to generalize to new levels.
When RL models overfit, even slight modifications to the environment can result
in poor agent performance. This paper explores how procedurally generated
levels during training can increase generality. We show that for some games
procedural level generation enables generalization to new levels within the
same distribution. Additionally, it is possible to achieve better performance
with less data by manipulating the difficulty of the levels in response to the
performance of the agent. The generality of the learned behaviors is also
evaluated on a set of human-designed levels. The results suggest that the
ability to generalize to human-designed levels highly depends on the design of
the level generators. We apply dimensionality reduction and clustering
techniques to visualize the generators' distributions of levels and analyze to
what degree they can produce levels similar to those designed by a human.
Deep Echo State Networks with Uncertainty Quantification for Spatio-Temporal Forecasting
Authors: Patrick L. McDermott, Christopher K. Wikle
Date: 2018-06-28 00:00:00
Tasks: Spatio-Temporal Forecasting, Uncertainty Quantification

Long-lead forecasting for spatio-temporal systems can often entail complex
nonlinear dynamics that are difficult to specify it a priori. Current
statistical methodologies for modeling these processes are often highly
parameterized and thus, challenging to implement from a computational
perspective. One potential parsimonious solution to this problem is a method
from the dynamical systems and engineering literature referred to as an echo
state network (ESN). ESN models use so-called {\it reservoir computing} to
efficiently compute recurrent neural network (RNN) forecasts. Moreover,
so-called "deep" models have recently been shown to be successful at predicting
high-dimensional complex nonlinear processes, particularly those with multiple
spatial and temporal scales of variability (such as we often find in
spatio-temporal environmental data). Here we introduce a deep ensemble ESN
(D-EESN) model. We present two versions of this model for spatio-temporal
processes that both produce forecasts and associated measures of uncertainty.
The first approach utilizes a bootstrap ensemble framework and the second is
developed within a hierarchical Bayesian framework (BD-EESN). This more general
hierarchical Bayesian framework naturally accommodates non-Gaussian data types
and multiple levels of uncertainties. The methodology is first applied to a
data set simulated from a novel non-Gaussian multiscale Lorenz-96 dynamical
system simulation model and then to a long-lead United States (U.S.) soil
moisture forecasting application.
Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face Hallucination
Authors: Junjun Jiang, Yi Yu, Jinhui Hu, Suhua Tang, Jiayi Ma
Date: 2018-06-28 00:00:00
Tasks: Face Hallucination, Hallucination, Super-Resolution

Most of the current face hallucination methods, whether they are shallow
learning-based or deep learning-based, all try to learn a relationship model
between Low-Resolution (LR) and High-Resolution (HR) spaces with the help of a
training set. They mainly focus on modeling image prior through either
model-based optimization or discriminative inference learning. However, when
the input LR face is tiny, the learned prior knowledge is no longer effective
and their performance will drop sharply. To solve this problem, in this paper
we propose a general face hallucination method that can integrate model-based
optimization and discriminative inference. In particular, to exploit the model
based prior, the Deep Convolutional Neural Networks (CNN) denoiser prior is
plugged into the super-resolution optimization model with the aid of
image-adaptive Laplacian regularization. Additionally, we further develop a
high-frequency details compensation method by dividing the face image to facial
components and performing face hallucination in a multi-layer neighbor
embedding manner. Experiments demonstrate that the proposed method can achieve
promising super-resolution results for tiny input LR faces.
DeepTag: inferring all-cause diagnoses from clinical notes in under-resourced medical domain
Authors: Allen Nie, Ashley Zehnder, Rodney L. Page, Arturo L. Pineda, Manuel A. Rivas, Carlos D. Bustamante, James Zou
Date: 2018-06-28 00:00:00
Tasks: All, Diagnostic

Large scale veterinary clinical records can become a powerful resource for
patient care and research. However, clinicians lack the time and resource to
annotate patient records with standard medical diagnostic codes and most
veterinary visits are captured in free text notes. The lack of standard coding
makes it challenging to use the clinical data to improve patient care. It is
also a major impediment to cross-species translational research, which relies
on the ability to accurately identify patient cohorts with specific diagnostic
criteria in humans and animals. In order to reduce the coding burden for
veterinary clinical practice and aid translational research, we have developed
a deep learning algorithm, DeepTag, which automatically infers diagnostic codes
from veterinary free text notes. DeepTag is trained on a newly curated dataset
of 112,558 veterinary notes manually annotated by experts. DeepTag extends
multi-task LSTM with an improved hierarchical objective that captures the
semantic structures between diseases. To foster human-machine collaboration,
DeepTag also learns to abstain in examples when it is uncertain and defers them
to human experts, resulting in improved performance. DeepTag accurately infers
disease codes from free text even in challenging cross-hospital settings where
the text comes from different clinical settings than the ones used for
training. It enables automated disease annotation across a broad range of
clinical diagnoses with minimal pre-processing. The technical framework in this
work can be applied in other medical domains that currently lack medical coding
resources.
A Topological Regularizer for Classifiers via Persistent Homology
Authors: Chao Chen, Xiuyan Ni, Qinxun Bai, Yusu Wang
Date: 2018-06-27 00:00:00

Regularization plays a crucial role in supervised learning. Most existing
methods enforce a global regularization in a structure agnostic manner. In this
paper, we initiate a new direction and propose to enforce the structural
simplicity of the classification boundary by regularizing over its topological
complexity. In particular, our measurement of topological complexity
incorporates the importance of topological features (e.g., connected
components, handles, and so on) in a meaningful manner, and provides a direct
control over spurious topological structures. We incorporate the new
measurement as a topological penalty in training classifiers. We also pro- pose
an efficient algorithm to compute the gradient of such penalty. Our method pro-
vides a novel way to topologically simplify the global structure of the model,
without having to sacrifice too much of the flexibility of the model. We
demonstrate the effectiveness of our new topological regularizer on a range of
synthetic and real-world datasets.
Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning
Authors: Jasjeet Dhaliwal, Saurabh Shintre
Date: 2018-06-27 00:00:00

Deep neural networks are susceptible to small-but-specific adversarial
perturbations capable of deceiving the network. This vulnerability can lead to
potentially harmful consequences in security-critical applications. To address
this vulnerability, we propose a novel metric called \emph{Gradient Similarity}
that allows us to capture the influence of training data on test inputs. We
show that \emph{Gradient Similarity} behaves differently for normal and
adversarial inputs, and enables us to detect a variety of adversarial attacks
with a near perfect ROC-AUC of 95-100\%. Even white-box adversaries equipped
with perfect knowledge of the system cannot bypass our detector easily. On the
MNIST dataset, white-box attacks are either detected with a high ROC-AUC of
87-96\%, or require very high distortion to bypass our detector.
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data
Authors: Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz
Date: 2018-06-27 00:00:00
Tasks: Graph Sampling, Node Classification

Empirical risk minimization is the main tool for prediction problems, but its
extension to relational data remains unsolved. We solve this problem using
recent ideas from graph sampling theory to (i) define an empirical risk for
relational data and (ii) obtain stochastic gradients for this empirical risk
that are automatically unbiased. This is achieved by considering the method by
which data is sampled from a graph as an explicit component of model design. By
integrating fast implementations of graph sampling schemes with standard
automatic differentiation tools, we provide an efficient turnkey solver for the
risk minimization problem. We establish basic theoretical properties of the
procedure. Finally, we demonstrate relational ERM with application to two
non-standard problems: one-stage training for semi-supervised node
classification, and learning embedding vectors for vertex attributes.
Experiments confirm that the turnkey inference procedure is effective in
practice, and that the sampling scheme used for model specification has a
strong effect on model performance. Code is available at
https://github.com/wooden-spoon/relational-ERM.