pwc-archive/papers-with-abstracts huggingface dataset:
Hypertree Decompositions Revisited for PGMs
Authors: Aarthy Shivram Arun, Sai Vikneshwar Mani Jayaraman, Christopher Ré, Atri Rudra
Date: 2018-07-02 00:00:00

We revisit the classical problem of exact inference on probabilistic
graphical models (PGMs). Our algorithm is based on recent \emph{worst-case
optimal database join} algorithms, which can be asymptotically faster than
traditional data processing methods. We present the first empirical evaluation
of these algorithms via JoinInfer -- a new exact inference engine. We
empirically explore the properties of the data for which our engine can be
expected to outperform traditional inference engines, refining current
theoretical notions. Further, JoinInfer outperforms existing state-of-the-art
inference engines (ACE, IJGP and libDAI) on some standard benchmark datasets by
up to a factor of 630x. Finally, we propose a promising data-driven heuristic
that extends JoinInfer to automatically tailor its parameters and/or switch to
the traditional inference algorithms.
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
Authors: Shaoxing Mo, Yinhao Zhu, Nicholas Zabaras, Xiaoqing Shi, Jichun Wu
Date: 2018-07-02 00:00:00
Tasks: Computational Efficiency, Decoder, Image-to-Image Regression, regression, Uncertainty Quantification

Surrogate strategies are used widely for uncertainty quantification of
groundwater models in order to improve computational efficiency. However, their
application to dynamic multiphase flow problems is hindered by the curse of
dimensionality, the saturation discontinuity due to capillarity effects, and
the time-dependence of the multi-output responses. In this paper, we propose a
deep convolutional encoder-decoder neural network methodology to tackle these
issues. The surrogate modeling task is transformed to an image-to-image
regression strategy. This approach extracts high-level coarse features from the
high-dimensional input permeability images using an encoder, and then refines
the coarse features to provide the output pressure/saturation images through a
decoder. A training strategy combining a regression loss and a segmentation
loss is proposed in order to better approximate the discontinuous saturation
field. To characterize the high-dimensional time-dependent outputs of the
dynamic system, time is treated as an additional input to the network that is
trained using pairs of input realizations and of the corresponding system
outputs at a limited number of time instances. The proposed method is evaluated
using a geological carbon storage process-based multiphase flow model with a
2500-dimensional stochastic permeability field. With a relatively small number
of training data, the surrogate model is capable of accurately characterizing
the spatio-temporal evolution of the pressure and discontinuous CO2 saturation
fields and can be used efficiently to compute the statistics of the system
responses.
Exploring End-to-End Techniques for Low-Resource Speech Recognition
Authors: Vladimir Bataev, Maxim Korenevsky, Ivan Medennikov, Alexander Zatvornitskiy
Date: 2018-07-02 00:00:00
Tasks: speech-recognition, Speech Recognition

In this work we present simple grapheme-based system for low-resource speech
recognition using Babel data for Turkish spontaneous speech (80 hours). We have
investigated different neural network architectures performance, including
fully-convolutional, recurrent and ResNet with GRU. Different features and
normalization techniques are compared as well. We also proposed CTC-loss
modification using segmentation during training, which leads to improvement
while decoding with small beam size. Our best model achieved word error rate of
45.8%, which is the best reported result for end-to-end systems using in-domain
data for this task, according to our knowledge.
Multi-User Multi-Armed Bandits for Uncoordinated Spectrum Access
Authors: Meghana Bande, Venugopal V. Veeravalli
Date: 2018-07-02 00:00:00
Tasks: Multi-Armed Bandits

A multi-user multi-armed bandit (MAB) framework is used to develop algorithms
for uncoordinated spectrum access. The number of users is assumed to be unknown
to each user. A stochastic setting is first considered, where the rewards on a
channel are the same for each user. In contrast to prior work, it is assumed
that the number of users can possibly exceed the number of channels, and that
rewards can be non-zero even under collisions. The proposed algorithm consists
of an estimation phase and an allocation phase. It is shown that if every user
adopts the algorithm, the system wide regret is constant with time with high
probability. The regret guarantees hold for any number of users and channels,
in particular, even when the number of users is less than the number of
channels. Next, an adversarial multi-user MAB framework is considered, where
the rewards on the channels are user-dependent. It is assumed that the number
of users is less than the number of channels, and that the users receive zero
reward on collision. The proposed algorithm combines the Exp3.P algorithm
developed in prior work for single user adversarial bandits with a collision
resolution mechanism to achieve sub-linear regret. It is shown that if every
user employs the proposed algorithm, the system wide regret is of the order
$O(T^\frac{3}{4})$ over a horizon of time $T$. The algorithms in both
stochastic and adversarial scenarios are extended to the dynamic case where the
number of users in the system evolves over time and are shown to lead to
sub-linear regret.
Semi-supervised Learning: Fusion of Self-supervised, Supervised Learning, and Multimodal Cues for Tactical Driver Behavior Detection
Authors: Athma Narayanan, Yi-Ting Chen, Srikanth Malla
Date: 2018-07-02 00:00:00

In this paper, we presented a preliminary study for tactical driver behavior
detection from untrimmed naturalistic driving recordings. While supervised
learning based detection is a common approach, it suffers when labeled data is
scarce. Manual annotation is both time-consuming and expensive. To emphasize
this problem, we experimented on a 104-hour real-world naturalistic driving
dataset with a set of predefined driving behaviors annotated. There are three
challenges in the dataset. First, predefined driving behaviors are sparse in a
naturalistic driving setting. Second, the distribution of driving behaviors is
long-tail. Third, a huge intra-class variation is observed. To address these
issues, recent self-supervised and supervised learning and fusion of multimodal
cues are leveraged into our architecture design. Preliminary experiments and
discussions are reported.
Client-Specific Anomaly Detection for Face Presentation Attack Detection
Authors: Shervin Rahimzadeh Arashloo, Josef Kittler
Date: 2018-07-02 00:00:00
Tasks: Anomaly Detection, Decision Making, Face Presentation Attack Detection

The one-class anomaly detection approach has previously been found to be
effective in face presentation attack detection, especially in an
\textit{unseen} attack scenario, where the system is exposed to novel types of
attacks. This work follows the same anomaly-based formulation of the problem
and analyses the merits of deploying \textit{client-specific} information for
face spoofing detection. We propose training one-class client-specific
classifiers (both generative and discriminative) using representations obtained
from pre-trained deep convolutional neural networks. Next, based on
subject-specific score distributions, a distinct threshold is set for each
client, which is then used for decision making regarding a test query. Through
extensive experiments using different one-class systems, it is shown that the
use of client-specific information in a one-class anomaly detection formulation
(both in model construction as well as decision threshold tuning) improves the
performance significantly. In addition, it is demonstrated that the same set of
deep convolutional features used for the recognition purposes is effective for
face presentation attack detection in the class-specific one-class anomaly
detection paradigm.
Make (Nearly) Every Neural Network Better: Generating Neural Network Ensembles by Weight Parameter Resampling
Authors: Jiayi Liu, Samarth Tripathi, Unmesh Kurup, Mohak Shah
Date: 2018-07-02 00:00:00

Deep Neural Networks (DNNs) have become increasingly popular in computer
vision, natural language processing, and other areas. However, training and
fine-tuning a deep learning model is computationally intensive and
time-consuming. We propose a new method to improve the performance of nearly
every model including pre-trained models. The proposed method uses an ensemble
approach where the networks in the ensemble are constructed by reassigning
model parameter values based on the probabilistic distribution of these
parameters, calculated towards the end of the training process. For pre-trained
models, this approach results in an additional training step (usually less than
one epoch). We perform a variety of analysis using the MNIST dataset and
validate the approach with a number of DNN models using pre-trained models on
the ImageNet dataset.
Credit Default Mining Using Combined Machine Learning and Heuristic Approach
Authors: Sheikh Rabiul Islam, William Eberle, Sheikh Khaled Ghafoor
Date: 2018-07-02 00:00:00
Tasks: BIG-bench Machine Learning

Predicting potential credit default accounts in advance is challenging.
Traditional statistical techniques typically cannot handle large amounts of
data and the dynamic nature of fraud and humans. To tackle this problem, recent
research has focused on artificial and computational intelligence based
approaches. In this work, we present and validate a heuristic approach to mine
potential default accounts in advance where a risk probability is precomputed
from all previous data and the risk probability for recent transactions are
computed as soon they happen. Beside our heuristic approach, we also apply a
recently proposed machine learning approach that has not been applied
previously on our targeted dataset [15]. As a result, we find that these
applied approaches outperform existing state-of-the-art approaches.
Improving part-of-speech tagging via multi-task learning and character-level word representations
Authors: Daniil Anastasyev, Ilya Gusev, Eugene Indenbom
Date: 2018-07-02 00:00:00
Tasks: Multi-Task Learning, Part-Of-Speech Tagging, POS, POS Tagging

In this paper, we explore the ways to improve POS-tagging using various types
of auxiliary losses and different word representations. As a baseline, we
utilized a BiLSTM tagger, which is able to achieve state-of-the-art results on
the sequence labelling tasks. We developed a new method for character-level
word representation using feedforward neural network. Such representation gave
us better results in terms of speed and performance of the model. We also
applied a novel technique of pretraining such word representations with
existing word vectors. Finally, we designed a new variant of auxiliary loss for
sequence labelling tasks: an additional prediction of the neighbour labels.
Such loss forces a model to learn the dependencies in-side a sequence of labels
and accelerates the process of training. We test these methods on English and
Russian languages.
Mining Illegal Insider Trading of Stocks: A Proactive Approach
Authors: Sheikh Rabiul Islam, Sheikh Khaled Ghafoor, William Eberle
Date: 2018-07-02 00:00:00
Tasks: Time Series, Time Series Analysis

Illegal insider trading of stocks is based on releasing non-public
information (e.g., new product launch, quarterly financial report, acquisition
or merger plan) before the information is made public. Detecting illegal
insider trading is difficult due to the complex, nonlinear, and non-stationary
nature of the stock market. In this work, we present an approach that detects
and predicts illegal insider trading proactively from large heterogeneous
sources of structured and unstructured data using a deep-learning based
approach combined with discrete signal processing on the time series data. In
addition, we use a tree-based approach that visualizes events and actions to
aid analysts in their understanding of large amounts of unstructured data.
Using existing data, we have discovered that our approach has a good success
rate in detecting illegal insider trading patterns.
A non-convex approach to low-rank and sparse matrix decomposition
Authors: Angang Cui, Meng Wen, Haiyang Li, Jigen Peng
Date: 2018-07-02 00:00:00

In this paper, we develop a nonconvex approach to the problem of low-rank and sparse matrix decomposition. In our nonconvex method, we replace the rank function and the $l_{0}$-norm of a given matrix with a non-convex fraction function on the singular values and the elements of the matrix respectively. An alternative direction method of multipliers algorithm is utilized to solve our proposed nonconvex problem with the nonconvex fraction function penalty. Numerical experiments on some low-rank and sparse matrix decomposition problems show that our method performs very well in recovering low-rank matrices which are heavily corrupted by large sparse errors.
Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST
Authors: Jinzheng Cai, You-Bao Tang, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers
Date: 2018-07-02 00:00:00
Tasks: Computed Tomography (CT), Lesion Segmentation, Segmentation

Volumetric lesion segmentation from computed tomography (CT) images is a
powerful means to precisely assess multiple time-point lesion/tumor changes.
However, because manual 3D segmentation is prohibitively time consuming,
current practices rely on an imprecise surrogate called response evaluation
criteria in solid tumors (RECIST). Despite their coarseness, RECIST markers are
commonly found in current hospital picture and archiving systems (PACS),
meaning they can provide a potentially powerful, yet extraordinarily
challenging, source of weak supervision for full 3D segmentation. Toward this
end, we introduce a convolutional neural network (CNN) based weakly supervised
slice-propagated segmentation (WSSS) method to 1) generate the initial lesion
segmentation on the axial RECIST-slice; 2) learn the data distribution on
RECIST-slices; 3) extrapolate to segment the whole lesion slice by slice to
finally obtain a volumetric segmentation. To validate the proposed method, we
first test its performance on a fully annotated lymph node dataset, where WSSS
performs comparably to its fully supervised counterparts. We then test on a
comprehensive lesion dataset with 32,735 RECIST marks, where we report a mean
Dice score of 92% on RECIST-marked slices and 76% on the entire 3D volumes.
Fast Fourier-Based Generation of the Compression Matrix for Deterministic Compressed Sensing
Authors: Sai Charan Jajimi
Date: 2018-07-01 00:00:00
Tasks: compressed sensing, Data Compression

The primary goal of this work is to review the importance of data compression
and present a fast Fourier-based method for generating the deterministic
compression matrix in the area of deterministic compressed sensing. The
principle concepts of data compression such as general process of data
compression, sparse signals, coherence matrix and Restricted Isometry Property
(RIP) have been defined. We have introduced two methods of sparse data
compression. The first method is formed by utilizing a stochastic matrix which
is a common approach, and the second method is created by utilizing a
deterministic matrix which is proposed more recently. The main goal of this
work is to improve the execution time of the deterministic matrix generation.
The execution time is related to the generation method of the deterministic
matrix. Furthermore, we have implemented a software which makes it possible to
compare different methods of reconstructing data compression. To make this
comparison, it is necessary to draw and compare certain graphs, e.g. phase
transition, the ratio of output signal to noise and input signal to noise,
signal to noise output and also the ratio of percentage of accurate
reconstructing and order of sparse signals for various reconstructing methods.
To facilitate this process, the user would be able to draw his/her favorite
graphs in GUI environment.
Neuro-memristive Circuits for Edge Computing: A review
Authors: Olga Krestinskaya, Alex Pappachen James, Leon O. Chua
Date: 2018-07-01 00:00:00
Tasks: Cloud Computing, Edge-computing, Management

The volume, veracity, variability, and velocity of data produced from the
ever-increasing network of sensors connected to Internet pose challenges for
power management, scalability, and sustainability of cloud computing
infrastructure. Increasing the data processing capability of edge computing
devices at lower power requirements can reduce several overheads for cloud
computing solutions. This paper provides the review of neuromorphic
CMOS-memristive architectures that can be integrated into edge computing
devices. We discuss why the neuromorphic architectures are useful for edge
devices and show the advantages, drawbacks and open problems in the field of
neuro-memristive circuits for edge computing.
Trust-Region Algorithms for Training Responses: Machine Learning Methods Using Indefinite Hessian Approximations
Authors: Jennifer B. Erway, Joshua Griffin, Roummel F. Marcia, Riadh Omheni
Date: 2018-07-01 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning (ML) problems are often posed as highly nonlinear and nonconvex unconstrained optimization problems. Methods for solving ML problems based on stochastic gradient descent are easily scaled for very large problems but may involve fine-tuning many hyper-parameters. Quasi-Newton approaches based on the limited-memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) update typically do not require manually tuning hyper-parameters but suffer from approximating a potentially indefinite Hessian with a positive-definite matrix. Hessian-free methods leverage the ability to perform Hessian-vector multiplication without needing the entire Hessian matrix, but each iteration's complexity is significantly greater than quasi-Newton methods. In this paper we propose an alternative approach for solving ML problems based on a quasi-Newton trust-region framework for solving large-scale optimization problems that allow for indefinite Hessian approximations. Numerical experiments on a standard testing data set show that with a fixed computational time budget, the proposed methods achieve better results than the traditional limited-memory BFGS and the Hessian-free methods.
Algorithms for solving optimization problems arising from deep neural net models: nonsmooth problems
Authors: Vyacheslav Kungurtsev, Tomas Pevny
Date: 2018-06-30 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Machine Learning models incorporating multiple layered learning networks have
been seen to provide effective models for various classification problems. The
resulting optimization problem to solve for the optimal vector minimizing the
empirical risk is, however, highly nonconvex. This alone presents a challenge
to application and development of appropriate optimization algorithms for
solving the problem. However, in addition, there are a number of interesting
problems for which the objective function is non- smooth and nonseparable. In
this paper, we summarize the primary challenges involved, the state of the art,
and present some numerical results on an interesting and representative class
of problems.
Algorithms for solving optimization problems arising from deep neural net models: smooth problems
Authors: Vyacheslav Kungurtsev, Tomas Pevny
Date: 2018-06-30 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Machine Learning models incorporating multiple layered learning networks have
been seen to provide effective models for various classification problems. The
resulting optimization problem to solve for the optimal vector minimizing the
empirical risk is, however, highly nonlinear. This presents a challenge to
application and development of appropriate optimization algorithms for solving
the problem. In this paper, we summarize the primary challenges involved and
present the case for a Newton-based method incorporating directions of negative
curvature, including promising numerical results on data arising from security
anomally deetection.
Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations
Authors: Dan Hendrycks, Thomas G. Dietterich
Date: 2018-07-04 00:00:00
Proceeding: ICLR 2019 5
Tasks: Adversarial Defense, Benchmarking

In this paper we establish rigorous benchmarks for image classifier
robustness. Our first benchmark, ImageNet-C, standardizes and expands the
corruption robustness topic, while showing which classifiers are preferable in
safety-critical applications. Unlike recent robustness research, this benchmark
evaluates performance on commonplace corruptions not worst-case adversarial
corruptions. We find that there are negligible changes in relative corruption
robustness from AlexNet to ResNet classifiers, and we discover ways to enhance
corruption robustness. Then we propose a new dataset called Icons-50 which
opens research on a new kind of robustness, surface variation robustness. With
this dataset we evaluate the frailty of classifiers on new styles of known
objects and unexpected instances of known classes. We also demonstrate two
methods that improve surface variation robustness. Together our benchmarks may
aid future work toward networks that learn fundamental class structure and also
robustly generalize.
Localization Recall Precision (LRP): A New Performance Metric for Object Detection
Authors: Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan
Date: 2018-07-04 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object, object-detection, Object Detection

Average precision (AP), the area under the recall-precision (RP) curve, is
the standard performance measure for object detection. Despite its wide
acceptance, it has a number of shortcomings, the most important of which are
(i) the inability to distinguish very different RP curves, and (ii) the lack of
directly measuring bounding box localization accuracy. In this paper, we
propose 'Localization Recall Precision (LRP) Error', a new metric which we
specifically designed for object detection. LRP Error is composed of three
components related to localization, false negative (FN) rate and false positive
(FP) rate. Based on LRP, we introduce the 'Optimal LRP', the minimum achievable
LRP error representing the best achievable configuration of the detector in
terms of recall-precision and the tightness of the boxes. In contrast to AP,
which considers precisions over the entire recall domain, Optimal LRP
determines the 'best' confidence score threshold for a class, which balances
the trade-off between localization and recall-precision. In our experiments, we
show that, for state-of-the-art object (SOTA) detectors, Optimal LRP provides
richer and more discriminative information than AP. We also demonstrate that
the best confidence score thresholds vary significantly among classes and
detectors. Moreover, we present LRP results of a simple online video object
detector which uses a SOTA still image object detector and show that the
class-specific optimized thresholds increase the accuracy against the common
approach of using a general threshold for all classes. At
https://github.com/cancam/LRP we provide the source code that can compute LRP
for the PASCAL VOC and MSCOCO datasets. Our source code can easily be adapted
to other datasets as well.