pwc-archive/papers-with-abstracts huggingface dataset:
Non-exchangeable random partition models for microclustering
Authors: Giuseppe Di Benedetto, François Caron, Yee Whye Teh
Date: 2017-11-20 00:00:00
Tasks: Clustering

Many popular random partition models, such as the Chinese restaurant process
and its two-parameter extension, fall in the class of exchangeable random
partitions, and have found wide applicability in model-based clustering,
population genetics, ecology or network analysis. While the exchangeability
assumption is sensible in many cases, it has some strong implications. In
particular, Kingman's representation theorem implies that the size of the
clusters necessarily grows linearly with the sample size; this feature may be
undesirable for some applications, as recently pointed out by Miller et al.
(2015). We present here a flexible class of non-exchangeable random partition
models which are able to generate partitions whose cluster sizes grow
sublinearly with the sample size, and where the growth rate is controlled by
one parameter. Along with this result, we provide the asymptotic behaviour of
the number of clusters of a given size, and show that the model can exhibit a
power-law behavior, controlled by another parameter. The construction is based
on completely random measures and a Poisson embedding of the random partition,
and inference is performed using a Sequential Monte Carlo algorithm.
Additionally, we show how the model can also be directly used to generate
sparse multigraphs with power-law degree distributions and degree sequences
with sublinear growth. Finally, experiments on real datasets emphasize the
usefulness of the approach compared to a two-parameter Chinese restaurant
process.
Speech recognition for medical conversations
Authors: Chung-Cheng Chiu, Anshuman Tripathi, Katherine Chou, Chris Co, Navdeep Jaitly, Diana Jaunzeikare, Anjuli Kannan, Patrick Nguyen, Hasim Sak, Ananth Sankar, Justin Tansuwan, Nathan Wan, Yonghui Wu, Xuedong Zhang
Date: 2017-11-20 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

In this work we explored building automatic speech recognition models for
transcribing doctor patient conversation. We collected a large scale dataset of
clinical conversations ($14,000$ hr), designed the task to represent the real
word scenario, and explored several alignment approaches to iteratively improve
data quality. We explored both CTC and LAS systems for building speech
recognition models. The LAS was more resilient to noisy data and CTC required
more data clean up. A detailed analysis is provided for understanding the
performance for clinical tasks. Our analysis showed the speech recognition
models performed well on important medical utterances, while errors occurred in
causal conversations. Overall we believe the resulting models can provide
reasonable quality in practice.
Facets, Tiers and Gems: Ontology Patterns for Hypernormalisation
Authors: Phillip Lord, Robert Stevens
Date: 2017-11-20 00:00:00

There are many methodologies and techniques for easing the task of ontology
building. Here we describe the intersection of two of these: ontology
normalisation and fully programmatic ontology development. The first of these
describes a standardized organisation for an ontology, with singly inherited
self-standing entities, and a number of small taxonomies of refining entities.
The former are described and defined in terms of the latter and used to manage
the polyhierarchy of the self-standing entities. Fully programmatic development
is a technique where an ontology is developed using a domain-specific language
within a programming language, meaning that as well defining ontological
entities, it is possible to add arbitrary patterns or new syntax within the
same environment. We describe how new patterns can be used to enable a new
style of ontology development that we call hypernormalisation.
Fast BTG-Forest-Based Hierarchical Sub-sentential Alignment
Authors: Hao Wang, Yves Lepage
Date: 2017-11-20 00:00:00
Tasks: Translation

In this paper, we propose a novel BTG-forest-based alignment method. Based on
a fast unsupervised initialization of parameters using variational IBM models,
we synchronously parse parallel sentences top-down and align hierarchically
under the constraint of BTG. Our two-step method can achieve the same run-time
and comparable translation performance as fast_align while it yields smaller
phrase tables. Final SMT results show that our method even outperforms in the
experiment of distantly related languages, e.g., English-Japanese.
Learning a Robust Representation via a Deep Network on Symmetric Positive Definite Manifolds
Authors: Zhi Gao, Yuwei Wu, Xingyuan Bu, Yunde Jia
Date: 2017-11-17 00:00:00

Recent studies have shown that aggregating convolutional features of a
pre-trained Convolutional Neural Network (CNN) can obtain impressive
performance for a variety of visual tasks. The symmetric Positive Definite
(SPD) matrix becomes a powerful tool due to its remarkable ability to learn an
appropriate statistic representation to characterize the underlying structure
of visual features. In this paper, we propose to aggregate deep convolutional
features into an SPD matrix representation through the SPD generation and the
SPD transformation under an end-to-end deep network. To this end, several new
layers are introduced in our network, including a nonlinear kernel aggregation
layer, an SPD matrix transformation layer, and a vectorization layer. The
nonlinear kernel aggregation layer is employed to aggregate the convolutional
features into a real SPD matrix directly. The SPD matrix transformation layer
is designed to construct a more compact and discriminative SPD representation.
The vectorization and normalization operations are performed in the
vectorization layer for reducing the redundancy and accelerating the
convergence. The SPD matrix in our network can be considered as a mid-level
representation bridging convolutional features and high-level semantic
features. To demonstrate the effectiveness of our method, we conduct extensive
experiments on visual classification. Experiment results show that our method
notably outperforms state-of-the-art methods.
MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (Extended Version)
Authors: Lucky Onwuzurike, Enrico Mariconti, Panagiotis Andriotis, Emiliano De Cristofaro, Gordon Ross, Gianluca Stringhini
Date: 2017-11-20 00:00:00

As Android has become increasingly popular, so has malware targeting it, thus
pushing the research community to propose different detection techniques.
However, the constant evolution of the Android ecosystem, and of malware
itself, makes it hard to design robust tools that can operate for long periods
of time without the need for modifications or costly re-training. Aiming to
address this issue, we set to detect malware from a behavioral point of view,
modeled as the sequence of abstracted API calls. We introduce MaMaDroid, a
static-analysis based system that abstracts the API calls performed by an app
to their class, package, or family, and builds a model from their sequences
obtained from the call graph of an app as Markov chains. This ensures that the
model is more resilient to API changes and the features set is of manageable
size. We evaluate MaMaDroid using a dataset of 8.5K benign and 35.5K malicious
apps collected over a period of six years, showing that it effectively detects
malware (with up to 0.99 F-measure) and keeps its detection capabilities for
long periods of time (up to 0.87 F-measure two years after training). We also
show that MaMaDroid remarkably outperforms DroidAPIMiner, a state-of-the-art
detection system that relies on the frequency of (raw) API calls. Aiming to
assess whether MaMaDroid's effectiveness mainly stems from the API abstraction
or from the sequencing modeling, we also evaluate a variant of it that uses
frequency (instead of sequences), of abstracted API calls. We find that it is
not as accurate, failing to capture maliciousness when trained on malware
samples that include API calls that are equally or more frequently used by
benign apps.
Optical Character Recognition (OCR) for Telugu: Database, Algorithm and Application
Authors: Chandra Prakash Konkimalla, Manikanta Srikar Yellapragada, Trishal Gayam, Souraj Mandal, Sumohana S. Channappayya
Date: 2017-11-20 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Transfer Learning

Telugu is a Dravidian language spoken by more than 80 million people
worldwide. The optical character recognition (OCR) of the Telugu script has
wide ranging applications including education, health-care, administration etc.
The beautiful Telugu script however is very different from Germanic scripts
like English and German. This makes the use of transfer learning of Germanic
OCR solutions to Telugu a non-trivial task. To address the challenge of OCR for
Telugu, we make three contributions in this work: (i) a database of Telugu
characters, (ii) a deep learning based OCR algorithm, and (iii) a client server
solution for the online deployment of the algorithm. For the benefit of the
Telugu people and the research community, we will make our code freely
available at https://gayamtrishal.github.io/OCR_Telugu.github.io/
Stochastic metamorphosis with template uncertainties
Authors: Alexis Arnaudon, Darryl Holm, Stefan Sommer
Date: 2017-11-20 00:00:00

In this paper, we investigate two stochastic perturbations of the
metamorphosis equations of image analysis, in the geometrical context of the
Euler-Poincar\'e theory. In the metamorphosis of images, the Lie group of
diffeomorphisms deforms a template image that is undergoing its own internal
dynamics as it deforms. This type of deformation allows more freedom for image
matching and has analogies with complex fluids when the template properties are
regarded as order parameters (coset spaces of broken symmetries). The first
stochastic perturbation we consider corresponds to uncertainty due to random
errors in the reconstruction of the deformation map from its vector field. We
also consider a second stochastic perturbation, which compounds the uncertainty
in of the deformation map with the uncertainty in the reconstruction of the
template position from its velocity field. We apply this general geometric
theory to several classical examples, including landmarks, images, and closed
curves, and we discuss its use for functional data analysis.
Optimism-Based Adaptive Regulation of Linear-Quadratic Systems
Authors: Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
Date: 2017-11-20 00:00:00

The main challenge for adaptive regulation of linear-quadratic systems is the
trade-off between identification and control. An adaptive policy needs to
address both the estimation of unknown dynamics parameters (exploration), as
well as the regulation of the underlying system (exploitation). To this end,
optimism-based methods which bias the identification in favor of optimistic
approximations of the true parameter are employed in the literature. A number
of asymptotic results have been established, but their finite time counterparts
are few, with important restrictions.
  This study establishes results for the worst-case regret of optimism-based
adaptive policies. The presented high probability upper bounds are optimal up
to logarithmic factors. The non-asymptotic analysis of this work requires very
mild assumptions; (i) stabilizability of the system's dynamics, and (ii)
limiting the degree of heaviness of the noise distribution. To establish such
bounds, certain novel techniques are developed to comprehensively address the
probabilistic behavior of dependent random matrices with heavy-tailed
distributions.
Model Extraction Warning in MLaaS Paradigm
Authors: Manish Kesarwani, Bhaskar Mukhoty, Vijay Arya, Sameep Mehta
Date: 2017-11-20 00:00:00
Tasks: Adversarial Attack, BIG-bench Machine Learning, model, Model extraction

Cloud vendors are increasingly offering machine learning services as part of
their platform and services portfolios. These services enable the deployment of
machine learning models on the cloud that are offered on a pay-per-query basis
to application developers and end users. However recent work has shown that the
hosted models are susceptible to extraction attacks. Adversaries may launch
queries to steal the model and compromise future query payments or privacy of
the training data. In this work, we present a cloud-based extraction monitor
that can quantify the extraction status of models by observing the query and
response streams of both individual and colluding adversarial users. We present
a novel technique that uses information gain to measure the model learning rate
by users with increasing number of queries. Additionally, we present an
alternate technique that maintains intelligent query summaries to measure the
learning rate relative to the coverage of the input feature space in the
presence of collusion. Both these approaches have low computational overhead
and can easily be offered as services to model owners to warn them of possible
extraction attacks from adversaries. We present performance results for these
approaches for decision tree models deployed on BigML MLaaS platform, using
open source datasets and different adversarial attack strategies.
Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms
Authors: Chao Qian, Yang Yu, Ke Tang, Xin Yao, Zhi-Hua Zhou
Date: 2017-11-20 00:00:00
Tasks: Combinatorial Optimization, Evolutionary Algorithms

Evolutionary algorithms (EAs) are a kind of nature-inspired general-purpose optimization algorithm, and have shown empirically good performance in solving various real-word optimization problems. During the past two decades, promising results on the running time analysis (one essential theoretical aspect) of EAs have been obtained, while most of them focused on isolated combinatorial optimization problems, which do not reflect the general-purpose nature of EAs. To provide a general theoretical explanation of the behavior of EAs, it is desirable to study their performance on general classes of combinatorial optimization problems. To the best of our knowledge, the only result towards this direction is the provably good approximation guarantees of EAs for the problem class of maximizing monotone submodular functions with matroid constraints. The aim of this work is to contribute to this line of research. Considering that many combinatorial optimization problems involve non-monotone or non-submodular objective functions, we study the general problem classes, maximizing submodular functions with/without a size constraint and maximizing monotone approximately submodular functions with a size constraint. We prove that a simple multi-objective EA called GSEMO-C can generally achieve good approximation guarantees in polynomial expected running time.
List-Decodable Robust Mean Estimation and Learning Mixtures of Spherical Gaussians
Authors: Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
Date: 2017-11-20 00:00:00

We study the problem of list-decodable Gaussian mean estimation and the
related problem of learning mixtures of separated spherical Gaussians. We
develop a set of techniques that yield new efficient algorithms with
significantly improved guarantees for these problems.
  {\bf List-Decodable Mean Estimation.} Fix any $d \in \mathbb{Z}_+$ and $0<
\alpha <1/2$. We design an algorithm with runtime $O
(\mathrm{poly}(n/\alpha)^{d})$ that outputs a list of $O(1/\alpha)$ many
candidate vectors such that with high probability one of the candidates is
within $\ell_2$-distance $O(\alpha^{-1/(2d)})$ from the true mean. The only
previous algorithm for this problem achieved error $\tilde O(\alpha^{-1/2})$
under second moment conditions. For $d = O(1/\epsilon)$, our algorithm runs in
polynomial time and achieves error $O(\alpha^{\epsilon})$. We also give a
Statistical Query lower bound suggesting that the complexity of our algorithm
is qualitatively close to best possible.
  {\bf Learning Mixtures of Spherical Gaussians.} We give a learning algorithm
for mixtures of spherical Gaussians that succeeds under significantly weaker
separation assumptions compared to prior work. For the prototypical case of a
uniform mixture of $k$ identity covariance Gaussians we obtain: For any
$\epsilon>0$, if the pairwise separation between the means is at least
$\Omega(k^{\epsilon}+\sqrt{\log(1/\delta)})$, our algorithm learns the unknown
parameters within accuracy $\delta$ with sample complexity and running time
$\mathrm{poly} (n, 1/\delta, (k/\epsilon)^{1/\epsilon})$. The previously best
known polynomial time algorithm required separation at least $k^{1/4}
\mathrm{polylog}(k/\delta)$.
  Our main technical contribution is a new technique, using degree-$d$
multivariate polynomials, to remove outliers from high-dimensional datasets
where the majority of the points are corrupted.
End-to-end Trained CNN Encode-Decoder Networks for Image Steganography
Authors: Atique ur Rehman, Rafia Rahim, M Shahroz Nadeem, Sibt Ul Hussain
Date: 2017-11-20 00:00:00
Tasks: Decoder, Image Steganography, SSIM

All the existing image steganography methods use manually crafted features to
hide binary payloads into cover images. This leads to small payload capacity
and image distortion. Here we propose a convolutional neural network based
encoder-decoder architecture for embedding of images as payload. To this end,
we make following three major contributions: (i) we propose a deep learning
based generic encoder-decoder architecture for image steganography; (ii) we
introduce a new loss function that ensures joint end-to-end training of
encoder-decoder networks; (iii) we perform extensive empirical evaluation of
proposed architecture on a range of challenging publicly available datasets
(MNIST, CIFAR10, PASCAL-VOC12, ImageNet, LFW) and report state-of-the-art
payload capacity at high PSNR and SSIM values.
Acceleration of Histogram-Based Contrast Enhancement via Selective Downsampling
Authors: Gang Cao, Huawei Tian, Lifang Yu, Xianglin Huang, Yongbin Wang
Date: 2017-09-14 00:00:00

In this paper, we propose a general framework to accelerate the universal histogram-based image contrast enhancement (CE) algorithms. Both spatial and gray-level selective down-sampling of digital images are adopted to decrease computational cost, while the visual quality of enhanced images is still preserved and without apparent degradation. Mapping function calibration is novelly proposed to reconstruct the pixel mapping on the gray levels missed by downsampling. As two case studies, accelerations of histogram equalization (HE) and the state-of-the-art global CE algorithm, i.e., spatial mutual information and PageRank (SMIRANK), are presented detailedly. Both quantitative and qualitative assessment results have verified the effectiveness of our proposed CE acceleration framework. In typical tests, computational efficiencies of HE and SMIRANK have been speeded up by about 3.9 and 13.5 times, respectively.
Block-Cyclic Stochastic Coordinate Descent for Deep Neural Networks
Authors: Kensuke Nakamura, Stefano Soatto, Byung-Woo Hong
Date: 2017-11-20 00:00:00

We present a stochastic first-order optimization algorithm, named BCSC, that
adds a cyclic constraint to stochastic block-coordinate descent. It uses
different subsets of the data to update different subsets of the parameters,
thus limiting the detrimental effect of outliers in the training set. Empirical
tests in benchmark datasets show that our algorithm outperforms
state-of-the-art optimization methods in both accuracy as well as convergence
speed. The improvements are consistent across different architectures, and can
be combined with other training techniques and regularization methods.
Tactics to Directly Map CNN graphs on Embedded FPGAs
Authors: Kamel Abdelouahab, Maxime Pelcat, Jocelyn Sérot, Cédric Bourrasset, François Berry, Jocelyn Serot
Date: 2017-11-20 00:00:00
Tasks: image-classification, Image Classification

Deep Convolutional Neural Networks (CNNs) are the state-of-the-art in image
classification. Since CNN feed forward propagation involves highly regular
parallel computation, it benefits from a significant speed-up when running on
fine grain parallel programmable logic devices. As a consequence, several
studies have proposed FPGA-based accelerators for CNNs. However, because of the
large computationalpower required by CNNs, none of the previous studies has
proposed a direct mapping of the CNN onto the physical resources of an FPGA,
allocating each processing actor to its own hardware instance.In this paper, we
demonstrate the feasibility of the so called direct hardware mapping (DHM) and
discuss several tactics we explore to make DHM usable in practice. As a proof
of concept, we introduce the HADDOC2 open source tool, that automatically
transforms a CNN description into a synthesizable hardware description with
platform-independent direct hardware mapping.
Situationally Aware Options
Authors: Daniel J. Mankowitz, Aviv Tamar, Shie Mannor
Date: 2017-11-20 00:00:00
Tasks: Reinforcement Learning

Hierarchical abstractions, also known as options -- a type of temporally
extended action (Sutton et. al. 1999) that enables a reinforcement learning
agent to plan at a higher level, abstracting away from the lower-level details.
In this work, we learn reusable options whose parameters can vary, encouraging
different behaviors, based on the current situation. In principle, these
behaviors can include vigor, defence or even risk-averseness. These are some
examples of what we refer to in the broader context as Situational Awareness
(SA). We incorporate SA, in the form of vigor, into hierarchical RL by defining
and learning situationally aware options in a Probabilistic Goal Semi-Markov
Decision Process (PG-SMDP). This is achieved using our Situationally Aware
oPtions (SAP) policy gradient algorithm which comes with a theoretical
convergence guarantee. We learn reusable options in different scenarios in a
RoboCup soccer domain (i.e., winning/losing). These options learn to execute
with different levels of vigor resulting in human-like behaviours such as
`time-wasting' in the winning scenario. We show the potential of the agent to
exit bad local optima using reusable options in RoboCup. Finally, using SAP,
the agent mitigates feature-based model misspecification in a Bottomless Pit of
Death domain.
Is prioritized sweeping the better episodic control?
Authors: Johanni Brea
Date: 2017-11-20 00:00:00
Tasks: Reinforcement Learning

Episodic control has been proposed as a third approach to reinforcement
learning, besides model-free and model-based control, by analogy with the three
types of human memory. i.e. episodic, procedural and semantic memory. But the
theoretical properties of episodic control are not well investigated. Here I
show that in deterministic tree Markov decision processes, episodic control is
equivalent to a form of prioritized sweeping in terms of sample efficiency as
well as memory and computation demands. For general deterministic and
stochastic environments, prioritized sweeping performs better even when memory
and computation demands are restricted to be equal to those of episodic
control. These results suggest generalizations of prioritized sweeping to
partially observable environments, its combined use with function approximation
and the search for possible implementations of prioritized sweeping in brains.