pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning for Distant Speech Recognition
Authors: Mirco Ravanelli
Date: 2017-12-17 00:00:00
Tasks: Deep Learning, Distant Speech Recognition, speech-recognition, Speech Recognition

Deep learning is an emerging technology that is considered one of the most
promising directions for reaching higher levels of artificial intelligence.
Among the other achievements, building computers that understand speech
represents a crucial leap towards intelligent machines. Despite the great
efforts of the past decades, however, a natural and robust human-machine speech
interaction still appears to be out of reach, especially when users interact
with a distant microphone in noisy and reverberant environments. The latter
disturbances severely hamper the intelligibility of a speech signal, making
Distant Speech Recognition (DSR) one of the major open challenges in the field.
  This thesis addresses the latter scenario and proposes some novel techniques,
architectures, and algorithms to improve the robustness of distant-talking
acoustic models. We first elaborate on methodologies for realistic data
contamination, with a particular emphasis on DNN training with simulated data.
We then investigate on approaches for better exploiting speech contexts,
proposing some original methodologies for both feed-forward and recurrent
neural networks. Lastly, inspired by the idea that cooperation across different
DNNs could be the key for counteracting the harmful effects of noise and
reverberation, we propose a novel deep learning paradigm called network of deep
neural networks. The analysis of the original concepts were based on extensive
experimental validations conducted on both real and simulated data, considering
different corpora, microphone configurations, environments, noisy conditions,
and ASR tasks.
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
Authors: Xingang Pan, Jianping Shi, Ping Luo, Xiaogang Wang, Xiaoou Tang
Date: 2017-12-17 00:00:00
Tasks: Lane Detection, Scene Understanding

Convolutional neural networks (CNNs) are usually built by stacking
convolutional operations layer-by-layer. Although CNN has shown strong
capability to extract semantics from raw pixels, its capacity to capture
spatial relationships of pixels across rows and columns of an image is not
fully explored. These relationships are important to learn semantic objects
with strong shape priors but weak appearance coherences, such as traffic lanes,
which are often occluded or not even painted on the road surface as shown in
Fig. 1 (a). In this paper, we propose Spatial CNN (SCNN), which generalizes
traditional deep layer-by-layer convolutions to slice-byslice convolutions
within feature maps, thus enabling message passings between pixels across rows
and columns in a layer. Such SCNN is particular suitable for long continuous
shape structure or large objects, with strong spatial relationship but less
appearance clues, such as traffic lanes, poles, and wall. We apply SCNN on a
newly released very challenging traffic lane detection dataset and Cityscapse
dataset. The results show that SCNN could learn the spatial relationship for
structure output and significantly improves the performance. We show that SCNN
outperforms the recurrent neural network (RNN) based ReNet and MRF+CNN (MRFNet)
in the lane detection dataset by 8.7% and 4.6% respectively. Moreover, our SCNN
won the 1st place on the TuSimple Benchmark Lane Detection Challenge, with an
accuracy of 96.53%.
Using Deep learning methods for generation of a personalized list of shuffled songs
Authors: Rushin Gindra, Srushti Kotak, Asmita Natekar, Grishma Sharma
Date: 2017-12-17 00:00:00

The shuffle mode, where songs are played in a randomized order that is decided upon for all tracks at once, is widely found and known to exist in music player systems. There are only few music enthusiasts who use this mode since it either is too random to suit their mood or it keeps on repeating the same list every time. In this paper, we propose to build a convolutional deep belief network(CDBN) that is trained to perform genre recognition based on audio features retrieved from the records of the Million Song Dataset. The learned parameters shall be used to initialize a multi-layer perceptron which takes extracted features of user's playlist as input alongside the metadata to classify to various categories. These categories will be shuffled retrospectively based on the metadata to autonomously provide with a list that is efficacious in playing songs that are desired by humans in normal conditions.
Benford's Law and First Letter of Word
Authors: Xiao-Yong Yan, Seong-Gyu Yang, Beom Jun Kim, Petter Minnhagen
Date: 2017-12-17 00:00:00

A universal First-Letter Law (FLL) is derived and described. It predicts the
percentages of first letters for words in novels. The FLL is akin to Benford's
law (BL) of first digits, which predicts the percentages of first digits in a
data collection of numbers. Both are universal in the sense that FLL only
depends on the numbers of letters in the alphabet, whereas BL only depends on
the number of digits in the base of the number system. The existence of these
types of universal laws appears counter-intuitive. Nonetheless both describe
data very well. Relations to some earlier works are given. FLL predicts that an
English author on the average starts about 16 out of 100 words with the English
letter `t'. This is corroborated by data, yet an author can freely write
anything. Fuller implications and the applicability of FLL remain for the
future.
Self-adaptation of Genetic Operators Through Genetic Programming Techniques
Authors: Andres Felipe Cruz Salinas, Jonatan Gomez Perdomo
Date: 2017-12-17 00:00:00
Tasks: Diversity

Here we propose an evolutionary algorithm that self modifies its operators at
the same time that candidate solutions are evolved. This tackles convergence
and lack of diversity issues, leading to better solutions. Operators are
represented as trees and are evolved using genetic programming (GP) techniques.
The proposed approach is tested with real benchmark functions and an analysis
of operator evolution is provided.
Acronym Disambiguation: A Domain Independent Approach
Authors: Aditya Thakker, Suhail Barot, Sudhir Bagul
Date: 2017-11-25 00:00:00

Acronyms are omnipresent. They usually express information that is repetitive
and well known. But acronyms can also be ambiguous because there can be
multiple expansions for the same acronym. In this paper, we propose a general
system for acronym disambiguation that can work on any acronym given some
context information. We present methods for retrieving all the possible
expansions of an acronym from Wikipedia and AcronymsFinder.com. We propose to
use these expansions to collect all possible contexts in which these acronyms
are used and then score them using a paragraph embedding technique called
Doc2Vec. This method collectively led to achieving an accuracy of 90.9% in
selecting the correct expansion for given acronym, on a dataset we scraped from
Wikipedia with 707 distinct acronyms and 14,876 disambiguations.
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds
Authors: Igor Halperin
Date: 2017-12-13 00:00:00
Tasks: Benchmarking, Model-based Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents a discrete-time option pricing model that is rooted in Reinforcement Learning (RL), and more specifically in the famous Q-Learning method of RL. We construct a risk-adjusted Markov Decision Process for a discrete-time version of the classical Black-Scholes-Merton (BSM) model, where the option price is an optimal Q-function, while the optimal hedge is a second argument of this optimal Q-function, so that both the price and hedge are parts of the same formula. Pricing is done by learning to dynamically optimize risk-adjusted returns for an option replicating portfolio, as in the Markowitz portfolio theory. Using Q-Learning and related methods, once created in a parametric setting, the model is able to go model-free and learn to price and hedge an option directly from data, and without an explicit model of the world. This suggests that RL may provide efficient data-driven and model-free methods for optimal pricing and hedging of options, once we depart from the academic continuous-time limit, and vice versa, option pricing methods developed in Mathematical Finance may be viewed as special cases of model-based Reinforcement Learning. Further, due to simplicity and tractability of our model which only needs basic linear algebra (plus Monte Carlo simulation, if we work with synthetic data), and its close relation to the original BSM model, we suggest that our model could be used for benchmarking of different RL algorithms for financial trading applications
Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization
Authors: Aditya Devarakonda, Kimon Fountoulakis, James Demmel, Michael W. Mahoney
Date: 2017-12-17 00:00:00
Tasks: subspace methods

Parallel computing has played an important role in speeding up convex
optimization methods for big data analytics and large-scale machine learning
(ML). However, the scalability of these optimization methods is inhibited by
the cost of communicating and synchronizing processors in a parallel setting.
Iterative ML methods are particularly sensitive to communication cost since
they often require communication every iteration. In this work, we extend
well-known techniques from Communication-Avoiding Krylov subspace methods to
first-order, block coordinate descent methods for Support Vector Machines and
Proximal Least-Squares problems. Our Synchronization-Avoiding (SA) variants
reduce the latency cost by a tunable factor of $s$ at the expense of a factor
of $s$ increase in flops and bandwidth costs. We show that the SA-variants are
numerically stable and can attain large speedups of up to $5.1\times$ on a Cray
XC30 supercomputer.
Deep Fully-Connected Networks for Video Compressive Sensing
Authors: Michael Iliadis, Leonidas Spinoulas, Aggelos K. Katsaggelos
Date: 2016-03-16 00:00:00
Tasks: Compressive Sensing, Video Compressive Sensing

In this work we present a deep learning framework for video compressive
sensing. The proposed formulation enables recovery of video frames in a few
seconds at significantly improved reconstruction quality compared to previous
approaches. Our investigation starts by learning a linear mapping between video
sequences and corresponding measured frames which turns out to provide
promising results. We then extend the linear formulation to deep
fully-connected networks and explore the performance gains using deeper
architectures. Our analysis is always driven by the applicability of the
proposed framework on existing compressive video architectures. Extensive
simulations on several video sequences document the superiority of our approach
both quantitatively and qualitatively. Finally, our analysis offers insights
into understanding how dataset sizes and number of layers affect reconstruction
performance while raising a few points for future investigation.
  Code is available at Github: https://github.com/miliadis/DeepVideoCS
A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms
Authors: Bhavya Kailkhura, Jayaraman J. Thiagarajan, Charvi Rastogi, Pramod K. Varshney, Peer-Timo Bremer
Date: 2017-12-16 00:00:00
Tasks: Image Reconstruction

This paper proposes a new approach to construct high quality space-filling
sample designs. First, we propose a novel technique to quantify the
space-filling property and optimally trade-off uniformity and randomness in
sample designs in arbitrary dimensions. Second, we connect the proposed metric
(defined in the spatial domain) to the objective measure of the design
performance (defined in the spectral domain). This connection serves as an
analytic framework for evaluating the qualitative properties of space-filling
designs in general. Using the theoretical insights provided by this
spatial-spectral analysis, we derive the notion of optimal space-filling
designs, which we refer to as space-filling spectral designs. Third, we propose
an efficient estimator to evaluate the space-filling properties of sample
designs in arbitrary dimensions and use it to develop an optimization framework
to generate high quality space-filling designs. Finally, we carry out a
detailed performance comparison on two different applications in 2 to 6
dimensions: a) image reconstruction and b) surrogate modeling on several
benchmark optimization functions and an inertial confinement fusion (ICF)
simulation code. We demonstrate that the propose spectral designs significantly
outperform existing approaches especially in high dimensions.
A smartphone application to measure the quality of pest control spraying machines via image analysis
Authors: Bruno B. Machado, Gabriel Spadon, Mauro S. Arruda, Wesley N. Goncalves, Andre C. P. L. F. Carvalho, Jose F. Rodrigues-Jr
Date: 2017-11-21 00:00:00

The need for higher agricultural productivity has demanded the intensive use
of pesticides. However, their correct use depends on assessment methods that
can accurately predict how well the pesticides' spraying covered the intended
crop region. Some methods have been proposed in the literature, but their high
cost and low portability harm their widespread use. This paper proposes and
experimentally evaluates a new methodology based on the use of a
smartphone-based mobile application, named DropLeaf. Experiments performed
using DropLeaf showed that, in addition to its versatility, it can predict with
high accuracy the pesticide spraying. DropLeaf is a five-fold image-processing
methodology based on: (i) color space conversion, (ii) threshold noise removal,
(iii) convolutional operations of dilation and erosion, (iv) detection of
contour markers in the water-sensitive card, and, (v) identification of
droplets via the marker-controlled watershed transformation. The authors
performed successful experiments over two case studies, the first using a set
of synthetic cards and the second using a real-world crop. The proposed tool
can be broadly used by farmers equipped with conventional mobile phones,
improving the use of pesticides with health, environmental and financial
benefits.
StackInsights: Cognitive Learning for Hybrid Cloud Readiness
Authors: Mu Qiao, Luis Bathen, Simon-Pierre Génot, Sunhwan Lee, Ramani Routray
Date: 2017-12-16 00:00:00
Tasks: Cloud Computing

Hybrid cloud is an integrated cloud computing environment utilizing a mix of
public cloud, private cloud, and on-premise traditional IT infrastructures.
Workload awareness, defined as a detailed full range understanding of each
individual workload, is essential in implementing the hybrid cloud. While it is
critical to perform an accurate analysis to determine which workloads are
appropriate for on-premise deployment versus which workloads can be migrated to
a cloud off-premise, the assessment is mainly performed by rule or policy based
approaches. In this paper, we introduce StackInsights, a novel cognitive system
to automatically analyze and predict the cloud readiness of workloads for an
enterprise. Our system harnesses the critical metrics across the entire stack:
1) infrastructure metrics, 2) data relevance metrics, and 3) application
taxonomy, to identify workloads that have characteristics of a) low sensitivity
with respect to business security, criticality and compliance, and b) low
response time requirements and access patterns. Since the capture of the data
relevance metrics involves an intrusive and in-depth scanning of the content of
storage objects, a machine learning model is applied to perform the business
relevance classification by learning from the meta level metrics harnessed
across stack. In contrast to traditional methods, StackInsights significantly
reduces the total time for hybrid cloud readiness assessment by orders of
magnitude.
DGM: A deep learning algorithm for solving partial differential equations
Authors: Justin Sirignano, Konstantinos Spiliopoulos
Date: 2017-08-24 00:00:00
Tasks: Deep Learning

High-dimensional PDEs have been a longstanding computational challenge. We
propose to solve high-dimensional PDEs by approximating the solution with a
deep neural network which is trained to satisfy the differential operator,
initial condition, and boundary conditions. Our algorithm is meshfree, which is
key since meshes become infeasible in higher dimensions. Instead of forming a
mesh, the neural network is trained on batches of randomly sampled time and
space points. The algorithm is tested on a class of high-dimensional free
boundary PDEs, which we are able to accurately solve in up to $200$ dimensions.
The algorithm is also tested on a high-dimensional Hamilton-Jacobi-Bellman PDE
and Burgers' equation. The deep learning algorithm approximates the general
solution to the Burgers' equation for a continuum of different boundary
conditions and physical conditions (which can be viewed as a high-dimensional
space). We call the algorithm a "Deep Galerkin Method (DGM)" since it is
similar in spirit to Galerkin methods, with the solution approximated by a
neural network instead of a linear combination of basis functions. In addition,
we prove a theorem regarding the approximation power of neural networks for a
class of quasilinear parabolic PDEs.
How well does your sampler really work?
Authors: Ryan Turner, Brady Neal
Date: 2017-12-16 00:00:00
Tasks: Meta-Learning

We present a new data-driven benchmark system to evaluate the performance of
new MCMC samplers. Taking inspiration from the COCO benchmark in optimization,
we view this task as having critical importance to machine learning and
statistics given the rate at which new samplers are proposed. The common
hand-crafted examples to test new samplers are unsatisfactory; we take a
meta-learning-like approach to generate benchmark examples from a large corpus
of data sets and models. Surrogates of posteriors found in real problems are
created using highly flexible density models including modern neural network
based approaches. We provide new insights into the real effective sample size
of various samplers per unit time and the estimation efficiency of the samplers
per sample. Additionally, we provide a meta-analysis to assess the predictive
utility of various MCMC diagnostics and perform a nonparametric regression to
combine them.
Continuous DR-submodular Maximization: Structure and Algorithms
Authors: An Bian, Kfir. Y. Levy, Andreas Krause, Joachim M. Buhmann
Date: 2017-11-04 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Point Processes

DR-submodular continuous functions are important objectives with wide real-world applications spanning MAP inference in determinantal point processes (DPPs), and mean-field inference for probabilistic submodular models, amongst others. DR-submodularity captures a subclass of non-convex functions that enables both exact minimization and approximate maximization in polynomial time. In this work we study the problem of maximizing non-monotone DR-submodular continuous functions under general down-closed convex constraints. We start by investigating geometric properties that underlie such objectives, e.g., a strong relation between (approximately) stationary points and global optimum is proved. These properties are then used to devise two optimization algorithms with provable guarantees. Concretely, we first devise a "two-phase" algorithm with $1/4$ approximation guarantee. This algorithm allows the use of existing methods for finding (approximately) stationary points as a subroutine, thus, harnessing recent progress in non-convex optimization. Then we present a non-monotone Frank-Wolfe variant with $1/e$ approximation guarantee and sublinear convergence rate. Finally, we extend our approach to a broader class of generalized DR-submodular continuous functions, which captures a wider spectrum of applications. Our theoretical findings are validated on synthetic and real-world problem instances.
Characterizing Political Fake News in Twitter by its Meta-Data
Authors: Julio Amador, Axel Oehmichen, Miguel Molina-Solana
Date: 2017-12-16 00:00:00

This article presents a preliminary approach towards characterizing political
fake news on Twitter through the analysis of their meta-data. In particular, we
focus on more than 1.5M tweets collected on the day of the election of Donald
Trump as 45th president of the United States of America. We use the meta-data
embedded within those tweets in order to look for differences between tweets
containing fake news and tweets not containing them. Specifically, we perform
our analysis only on tweets that went viral, by studying proxies for users'
exposure to the tweets, by characterizing accounts spreading fake news, and by
looking at their polarization. We found significant differences on the
distribution of followers, the number of URLs on tweets, and the verification
of the users.
Taming Wild High Dimensional Text Data with a Fuzzy Lash
Authors: Amir Karami
Date: 2017-12-16 00:00:00
Tasks: Clustering, Dimensionality Reduction, Vocal Bursts Intensity Prediction

The bag of words (BOW) represents a corpus in a matrix whose elements are the
frequency of words. However, each row in the matrix is a very high-dimensional
sparse vector. Dimension reduction (DR) is a popular method to address sparsity
and high-dimensionality issues. Among different strategies to develop DR
method, Unsupervised Feature Transformation (UFT) is a popular strategy to map
all words on a new basis to represent BOW. The recent increase of text data and
its challenges imply that DR area still needs new perspectives. Although a wide
range of methods based on the UFT strategy has been developed, the fuzzy
approach has not been considered for DR based on this strategy. This research
investigates the application of fuzzy clustering as a DR method based on the
UFT strategy to collapse BOW matrix to provide a lower-dimensional
representation of documents instead of the words in a corpus. The quantitative
evaluation shows that fuzzy clustering produces superior performance and
features to Principal Components Analysis (PCA) and Singular Value
Decomposition (SVD), two popular DR methods based on the UFT strategy.
Multidimensional Data Tensor Sensing for RF Tomographic Imaging
Authors: Tao Deng, Xiao-Yang Liu, Feng Qian, Anwar Walid
Date: 2017-12-13 00:00:00
Tasks: compressed sensing

Radio-frequency (RF) tomographic imaging is a promising technique for
inferring multi-dimensional physical space by processing RF signals traversed
across a region of interest. However, conventional RF tomography schemes are
generally based on vector compressed sensing, which ignores the geometric
structures of the target spaces and leads to low recovery precision. The
recently proposed transform-based tensor model is more appropriate for sensory
data processing, as it helps exploit the geometric structures of the
three-dimensional target and improve the recovery precision. In this paper, we
propose a novel tensor sensing approach that achieves highly accurate
estimation for real-world three-dimensional spaces. First, we use the
transform-based tensor model to formulate a tensor sensing problem, and propose
a fast alternating minimization algorithm called Alt-Min. Secondly, we drive an
algorithm which is optimized to reduce memory and computation requirements.
Finally, we present evaluation of our Alt-Min approach using IKEA 3D data and
demonstrate significant improvement in recovery error and convergence speed
compared to prior tensor-based compressed sensing.