pwc-archive/papers-with-abstracts huggingface dataset:
Rao-Blackwellized Stochastic Gradients for Discrete Distributions
Authors: Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael. I. Jordan, Jon McAuliffe
Date: 2018-10-10 00:00:00
Tasks: General Classification

We wish to compute the gradient of an expectation over a finite or countably infinite sample space having $K \leq \infty$ categories. When $K$ is indeed infinite, or finite but very large, the relevant summation is intractable. Accordingly, various stochastic gradient estimators have been proposed. In this paper, we describe a technique that can be applied to reduce the variance of any such estimator, without changing its bias---in particular, unbiasedness is retained. We show that our technique is an instance of Rao-Blackwellization, and we demonstrate the improvement it yields on a semi-supervised classification problem and a pixel attention task.
The IFF Foundation for Ontological Knowledge Organization
Authors: Robert E. Kent
Date: 2018-10-10 00:00:00

This paper discusses an axiomatic approach for the integration of ontologies,
an approach that extends to first order logic a previous approach (Kent 2000)
based on information flow. This axiomatic approach is represented in the
Information Flow Framework (IFF), a metalevel framework for organizing the
information that appears in digital libraries, distributed databases and
ontologies (Kent 2001). The paper argues that the integration of ontologies is
the two-step process of alignment and unification. Ontological alignment
consists of the sharing of common terminology and semantics through a mediating
ontology. Ontological unification, concentrated in a virtual ontology of
community connections, is fusion of the alignment diagram of participant
community ontologies - the quotient of the sum of the participant portals
modulo the ontological alignment structure.
Towards Differentially Private Truth Discovery for Crowd Sensing Systems
Authors: Yaliang Li, Houping Xiao, Zhan Qin, Chenglin Miao, Lu Su, Jing Gao, Kui Ren, Bolin Ding
Date: 2018-10-10 00:00:00
Tasks: Privacy Preserving

Nowadays, crowd sensing becomes increasingly more popular due to the
ubiquitous usage of mobile devices. However, the quality of such
human-generated sensory data varies significantly among different users. To
better utilize sensory data, the problem of truth discovery, whose goal is to
estimate user quality and infer reliable aggregated results through
quality-aware data aggregation, has emerged as a hot topic. Although the
existing truth discovery approaches can provide reliable aggregated results,
they fail to protect the private information of individual users. Moreover,
crowd sensing systems typically involve a large number of participants, making
encryption or secure multi-party computation based solutions difficult to
deploy. To address these challenges, in this paper, we propose an efficient
privacy-preserving truth discovery mechanism with theoretical guarantees of
both utility and privacy. The key idea of the proposed mechanism is to perturb
data from each user independently and then conduct weighted aggregation among
users' perturbed data. The proposed approach is able to assign user weights
based on information quality, and thus the aggregated results will not deviate
much from the true results even when large noise is added. We adapt local
differential privacy definition to this privacy-preserving task and demonstrate
the proposed mechanism can satisfy local differential privacy while preserving
high aggregation accuracy. We formally quantify utility and privacy trade-off
and further verify the claim by experiments on both synthetic data and a
real-world crowd sensing system.
Leveraging Textual Specifications for Grammar-based Fuzzing of Network Protocols
Authors: Samuel Jero, Maria Leonor Pacheco, Dan Goldwasser, Cristina Nita-Rotaru
Date: 2018-10-10 00:00:00

Grammar-based fuzzing is a technique used to find software vulnerabilities by
injecting well-formed inputs generated following rules that encode application
semantics. Most grammar-based fuzzers for network protocols rely on human
experts to manually specify these rules. In this work we study automated
learning of protocol rules from textual specifications (i.e. RFCs). We evaluate
the automatically extracted protocol rules by applying them to a
state-of-the-art fuzzer for transport protocols and show that it leads to a
smaller number of test cases while finding the same attacks as the system that
uses manually specified rules.
Efficient Tensor Decomposition with Boolean Factors
Authors: Sung-En Chang, Xun Zheng, Ian E. H. Yen, Pradeep Ravikumar, Rose Yu
Date: 2018-10-10 00:00:00
Tasks: Tensor Decomposition

Tensor decomposition has been extensively used as a tool for exploratory analysis. Motivated by neuroscience applications, we study tensor decomposition with Boolean factors. The resulting optimization problem is challenging due to the non-convex objective and the combinatorial constraints. We propose Binary Matching Pursuit (BMP), a novel generalization of the matching pursuit strategy to decompose the tensor efficiently. BMP iteratively searches for atoms in a greedy fashion. The greedy atom search step is solved efficiently via a MAXCUT-like boolean quadratic program. We prove that BMP is guaranteed to converge sublinearly to the optimal solution and recover the factors under mild identifiability conditions. Experiments demonstrate the superior performance of our method over baselines on synthetic and real datasets. We also showcase the application of BMP in quantifying neural interactions underlying high-resolution spatiotemporal ECoG recordings.
Deep Recurrent Level Set for Segmenting Brain Tumors
Authors: T. Hoang Ngan Le, Raajitha Gummadi, Marios Savvides
Date: 2018-10-10 00:00:00
Tasks: Brain Tumor Segmentation, Segmentation, Tumor Segmentation

Variational Level Set (VLS) has been a widely used method in medical
segmentation. However, segmentation accuracy in the VLS method dramatically
decreases when dealing with intervening factors such as lighting, shadows,
colors, etc. Additionally, results are quite sensitive to initial settings and
are highly dependent on the number of iterations. In order to address these
limitations, the proposed method incorporates VLS into deep learning by
defining a novel end-to-end trainable model called as Deep Recurrent Level Set
(DRLS). The proposed DRLS consists of three layers, i.e, Convolutional layers,
Deconvolutional layers with skip connections and LevelSet layers. Brain tumor
segmentation is taken as an instant to illustrate the performance of the
proposed DRLS. Convolutional layer learns visual representation of brain tumor
at different scales. Since brain tumors occupy a small portion of the image,
deconvolutional layers are designed with skip connections to obtain a high
quality feature map. Level-Set Layer drives the contour towards the brain
tumor. In each step, the Convolutional Layer is fed with the LevelSet map to
obtain a brain tumor feature map. This in turn serves as input for the LevelSet
layer in the next step. The experimental results have been obtained on
BRATS2013, BRATS2015 and BRATS2017 datasets. The proposed DRLS model improves
both computational time and segmentation accuracy when compared to the the
classic VLS-based method. Additionally, a fully end-to-end system DRLS achieves
state-of-the-art segmentation on brain tumors.
Probabilistic Clustering Using Maximal Matrix Norm Couplings
Authors: David Qiu, Anuran Makur, Lizhong Zheng
Date: 2018-10-10 00:00:00
Tasks: Clustering, Sentence, Sentence Completion

In this paper, we present a local information theoretic approach to
explicitly learn probabilistic clustering of a discrete random variable. Our
formulation yields a convex maximization problem for which it is NP-hard to
find the global optimum. In order to algorithmically solve this optimization
problem, we propose two relaxations that are solved via gradient ascent and
alternating maximization. Experiments on the MSR Sentence Completion Challenge,
MovieLens 100K, and Reuters21578 datasets demonstrate that our approach is
competitive with existing techniques and worthy of further investigation.
Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD
Authors: Phuong Ha Nguyen, Lam M. Nguyen, Marten van Dijk
Date: 2018-10-10 00:00:00
Proceeding: NeurIPS 2019 12

We study the convergence of Stochastic Gradient Descent (SGD) for strongly convex objective functions. We prove for all $t$ a lower bound on the expected convergence rate after the $t$-th SGD iteration; the lower bound is over all possible sequences of diminishing step sizes. It implies that recently proposed sequences of step sizes at ICML 2018 and ICML 2019 are {\em universally} close to optimal in that the expected convergence rate after {\em each} iteration is within a factor $32$ of our lower bound. This factor is independent of dimension $d$. We offer a framework for comparing with lower bounds in state-of-the-art literature and when applied to SGD for strongly convex objective functions our lower bound is a significant factor $775\cdot d$ larger compared to existing work.
Fully Supervised Speaker Diarization
Authors: Aonan Zhang, Quan Wang, Zhenyao Zhu, John Paisley, Chong Wang
Date: 2018-10-10 00:00:00
Tasks: Clustering, speaker-diarization, Speaker Diarization

In this paper, we propose a fully supervised speaker diarization approach,
named unbounded interleaved-state recurrent neural networks (UIS-RNN). Given
extracted speaker-discriminative embeddings (a.k.a. d-vectors) from input
utterances, each individual speaker is modeled by a parameter-sharing RNN,
while the RNN states for different speakers interleave in the time domain. This
RNN is naturally integrated with a distance-dependent Chinese restaurant
process (ddCRP) to accommodate an unknown number of speakers. Our system is
fully supervised and is able to learn from examples where time-stamped speaker
labels are annotated. We achieved a 7.6% diarization error rate on NIST SRE
2000 CALLHOME, which is better than the state-of-the-art method using spectral
clustering. Moreover, our method decodes in an online fashion while most
state-of-the-art systems rely on offline clustering.
Training Generative Adversarial Networks with Binary Neurons by End-to-end Backpropagation
Authors: Hao-Wen Dong, Yi-Hsuan Yang
Date: 2018-10-10 00:00:00
Tasks: Generative Adversarial Network

We propose the BinaryGAN, a novel generative adversarial network (GAN) that
uses binary neurons at the output layer of the generator. We employ the
sigmoid-adjusted straight-through estimators to estimate the gradients for the
binary neurons and train the whole network by end-to-end backpropogation. The
proposed model is able to directly generate binary-valued predictions at test
time. We implement such a model to generate binarized MNIST digits and
experimentally compare the performance for different types of binary neurons,
GAN objectives and network architectures. Although the results are still
preliminary, we show that it is possible to train a GAN that has binary neurons
and that the use of gradient estimators can be a promising direction for
modeling discrete distributions with GANs. For reproducibility, the source code
is available at https://github.com/salu133445/binarygan .
Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time
Authors: Yinghao Huang, Manuel Kaufmann, Emre Aksan, Michael J. Black, Otmar Hilliges, Gerard Pons-Moll
Date: 2018-10-10 00:00:00

We demonstrate a novel deep neural network capable of reconstructing human
full body pose in real-time from 6 Inertial Measurement Units (IMUs) worn on
the user's body. In doing so, we address several difficult challenges. First,
the problem is severely under-constrained as multiple pose parameters produce
the same IMU orientations. Second, capturing IMU data in conjunction with
ground-truth poses is expensive and difficult to do in many target application
scenarios (e.g., outdoors). Third, modeling temporal dependencies through
non-linear optimization has proven effective in prior work but makes real-time
prediction infeasible. To address this important limitation, we learn the
temporal pose priors using deep learning. To learn from sufficient data, we
synthesize IMU data from motion capture datasets. A bi-directional RNN
architecture leverages past and future information that is available at
training time. At test time, we deploy the network in a sliding window fashion,
retaining real time capabilities. To evaluate our method, we recorded DIP-IMU,
a dataset consisting of $10$ subjects wearing 17 IMUs for validation in $64$
sequences with $330\,000$ time instants; this constitutes the largest IMU
dataset publicly available. We quantitatively evaluate our approach on multiple
datasets and show results from a real-time implementation. DIP-IMU and the code
are available for research purposes.
End-to-End Content and Plan Selection for Data-to-Text Generation
Authors: Sebastian Gehrmann, Falcon Z. Dai, Henry Elder, Alexander M. Rush
Date: 2018-10-10 00:00:00
Proceeding: WS 2018 11
Tasks: Data-to-Text Generation, Sentence, Text Generation

Learning to generate fluent natural language from structured data with neural
networks has become an common approach for NLG. This problem can be challenging
when the form of the structured data varies between examples. This paper
presents a survey of several extensions to sequence-to-sequence models to
account for the latent content selection process, particularly variants of copy
attention and coverage decoding. We further propose a training method based on
diverse ensembling to encourage models to learn distinct sentence templates
during training. An empirical evaluation of these techniques shows an increase
in the quality of generated text across five automated metrics, as well as
human evaluation.
Intrusion Detection Using Mouse Dynamics
Authors: Margit Antal, Elod Egyed-Zsigmond
Date: 2018-10-10 00:00:00
Tasks: Intrusion Detection

Compared to other behavioural biometrics, mouse dynamics is a less explored
area. General purpose data sets containing unrestricted mouse usage data are
usually not available. The Balabit data set was released in 2016 for a data
science competition, which against the few subjects, can be considered the
first adequate publicly available one. This paper presents a performance
evaluation study on this data set for impostor detection. The existence of very
short test sessions makes this data set challenging. Raw data were segmented
into mouse move, point and click and drag and drop types of mouse actions, then
several features were extracted. In contrast to keystroke dynamics, mouse data
is not sensitive, therefore it is possible to collect negative mouse dynamics
data and to use two-class classifiers for impostor detection. Both action- and
set of actions-based evaluations were performed. Set of actions-based
evaluation achieves 0.92 AUC on the test part of the data set. However, the
same type of evaluation conducted on the training part of the data set resulted
in maximal AUC (1) using only 13 actions. Drag and drop mouse actions proved to
be the best actions for impostor detection.
Quantum Neural Network and Soft Quantum Computing
Authors: Zeng-Bing Chen
Date: 2018-10-10 00:00:00

A new paradigm of quantum computing, namely, soft quantum computing, is
proposed for nonclassical computation using real world quantum systems with
naturally occurring environment-induced decoherence and dissipation. As a
specific example of soft quantum computing, we suggest a quantum neural
network, where the neurons connect pairwise via the "controlled Kraus
operations", hoping to pave an easier and more realistic way to quantum
artificial intelligence and even to better understanding certain functioning of
the human brain. Our quantum neuron model mimics as much as possible the
realistic neurons and meanwhile, uses quantum laws for processing information.
The quantum features of the noisy neural network are uncovered by the presence
of quantum discord and by non-commutability of quantum operations. We believe
that our model puts quantum computing into a wider context and inspires the
hope to build a soft quantum computer much earlier than the standard one.
Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record
Authors: Jinghe Zhang, Kamran Kowsari, James H. Harrison, Jennifer M. Lobo, Laura E. Barnes
Date: 2018-10-10 00:00:00
Tasks: Feature Importance

The wide implementation of electronic health record (EHR) systems facilitates
the collection of large-scale health data from real clinical settings. Despite
the significant increase in adoption of EHR systems, this data remains largely
unexplored, but presents a rich data source for knowledge discovery from
patient health histories in tasks such as understanding disease correlations
and predicting health outcomes. However, the heterogeneity, sparsity, noise,
and bias in this data present many complex challenges. This complexity makes it
difficult to translate potentially relevant information into machine learning
algorithms. In this paper, we propose a computational framework, Patient2Vec,
to learn an interpretable deep representation of longitudinal EHR data which is
personalized for each patient. To evaluate this approach, we apply it to the
prediction of future hospitalizations using real EHR data and compare its
predictive performance with baseline methods. Patient2Vec produces a vector
space with meaningful structure and it achieves an AUC around 0.799
outperforming baseline methods. In the end, the learned feature importance can
be visualized and interpreted at both the individual and population levels to
bring clinical insights.
A General Framework for Fair Regression
Authors: Jack Fitzsimons, AbdulRahman Al Ali, Michael Osborne, Stephen Roberts
Date: 2018-10-10 00:00:00
Tasks: Fairness, Gaussian Processes, regression

Fairness, through its many forms and definitions, has become an important
issue facing the machine learning community. In this work, we consider how to
incorporate group fairness constraints in kernel regression methods, applicable
to Gaussian processes, support vector machines, neural network regression and
decision tree regression. Further, we focus on examining the effect of
incorporating these constraints in decision tree regression, with direct
applications to random forests and boosted trees amongst other widespread
popular inference techniques. We show that the order of complexity of memory
and computation is preserved for such models and tightly bound the expected
perturbations to the model in terms of the number of leaves of the trees.
Importantly, the approach works on trained models and hence can be easily
applied to models in current use and group labels are only required on training
data.
On the Brain Networks of Complex Problem Solving
Authors: Abdullah Alchihabi, Omer Ekmekci, Baran B. Kivilcim, Sharlene D. Newman, Fatos T. Yarman Vural
Date: 2018-10-10 00:00:00

Complex problem solving is a high level cognitive process which has been
thoroughly studied over the last decade. The Tower of London (TOL) is a task
that has been widely used to study problem-solving. In this study, we aim to
explore the underlying cognitive network dynamics among anatomical regions of
complex problem solving and its sub-phases, namely planning and execution. A
new brain network construction model establishing dynamic functional brain
networks using fMRI is proposed. The first step of the model is a preprocessing
pipeline that manages to decrease the spatial redundancy while increasing the
temporal resolution of the fMRI recordings. Then, dynamic brain networks are
estimated using artificial neural networks. The network properties of the
estimated brain networks are studied in order to identify regions of interest,
such as hubs and subgroups of densely connected brain regions. The major
similarities and dissimilarities of the network structure of planning and
execution phases are highlighted. Our findings show the hubs and clusters of
densely interconnected regions during both subtasks. It is observed that there
are more hubs during the planning phase compared to the execution phase, and
the clusters are more strongly connected during planning compared to execution.
Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling
Authors: Chris D. Cantwell, Yumnah Mohamied, Konstantinos N. Tzortzis, Stef Garasto, Charles Houston, Rasheda A. Chowdhury, Fu Siong Ng, Anil A. Bharath, Nicholas S. Peters
Date: 2018-10-09 00:00:00
Tasks: BIG-bench Machine Learning

We review some of the latest approaches to analysing cardiac
electrophysiology data using machine learning and predictive modelling. Cardiac
arrhythmias, particularly atrial fibrillation, are a major global healthcare
challenge. Treatment is often through catheter ablation, which involves the
targeted localized destruction of regions of the myocardium responsible for
initiating or perpetuating the arrhythmia. Ablation targets are either
anatomically defined, or identified based on their functional properties as
determined through the analysis of contact intracardiac electrograms acquired
with increasing spatial density by modern electroanatomic mapping systems.
While numerous quantitative approaches have been investigated over the past
decades for identifying these critical curative sites, few have provided a
reliable and reproducible advance in success rates. Machine learning
techniques, including recent deep-learning approaches, offer a potential route
to gaining new insight from this wealth of highly complex spatio-temporal
information that existing methods struggle to analyse. Coupled with predictive
modelling, these techniques offer exciting opportunities to advance the field
and produce more accurate diagnoses and robust personalised treatment. We
outline some of these methods and illustrate their use in making predictions
from the contact electrogram and augmenting predictive modelling tools, both by
more rapidly predicting future states of the system and by inferring the
parameters of these models from experimental observations.
Event Coreference Resolution Using Neural Network Classifiers
Authors: Arun Pandian, Lamana Mulaffer, Kemal Oflazer, Amna AlZeyara
Date: 2018-10-09 00:00:00
Tasks: coreference-resolution, Coreference Resolution, Cross Document Coreference Resolution, Event Coreference Resolution, Event Cross-Document Coreference Resolution

This paper presents a neural network classifier approach to detecting both
within- and cross- document event coreference effectively using only event
mention based features. Our approach does not (yet) rely on any event argument
features such as semantic roles or spatiotemporal arguments. Experimental
results on the ECB+ dataset show that our approach produces F1 scores that
significantly outperform the state-of-the-art methods for both within-document
and cross-document event coreference resolution when we use B3 and CEAFe
evaluation measures, but gets worse F1 score with the MUC measure. However,
when we use the CoNLL measure, which is the average of these three scores, our
approach has slightly better F1 for within- document event coreference
resolution but is significantly better for cross-document event coreference
resolution.