pwc-archive/papers-with-abstracts huggingface dataset:
Blockwise Parallel Decoding for Deep Autoregressive Models
Authors: Mitchell Stern, Noam Shazeer, Jakob Uszkoreit
Date: 2018-11-07 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Decoder, Image Super-Resolution, Machine Translation, Super-Resolution, Translation

Deep autoregressive sequence-to-sequence models have demonstrated impressive
performance across a wide variety of tasks in recent years. While common
architecture classes such as recurrent, convolutional, and self-attention
networks make different trade-offs between the amount of computation needed per
layer and the length of the critical path at training time, generation still
remains an inherently sequential process. To overcome this limitation, we
propose a novel blockwise parallel decoding scheme in which we make predictions
for multiple time steps in parallel then back off to the longest prefix
validated by a scoring model. This allows for substantial theoretical
improvements in generation speed when applied to architectures that can process
output sequences in parallel. We verify our approach empirically through a
series of experiments using state-of-the-art self-attention models for machine
translation and image super-resolution, achieving iteration reductions of up to
2x over a baseline greedy decoder with no loss in quality, or up to 7x in
exchange for a slight decrease in performance. In terms of wall-clock time, our
fastest models exhibit real-time speedups of up to 4x over standard greedy
decoding.
Degree-$d$ Chow Parameters Robustly Determine Degree-$d$ PTFs (and Algorithmic Applications)
Authors: Ilias Diakonikolas, Daniel M. Kane
Date: 2018-11-07 00:00:00

The degree-$d$ Chow parameters of a Boolean function $f: \{-1,1\}^n \to
\mathbb{R}$ are its degree at most $d$ Fourier coefficients. It is well-known
that degree-$d$ Chow parameters uniquely characterize degree-$d$ polynomial
threshold functions (PTFs) within the space of all bounded functions. In this
paper, we prove a robust version of this theorem: For $f$ any Boolean
degree-$d$ PTF and $g$ any bounded function, if the degree-$d$ Chow parameters
of $f$ are close to the degree-$d$ Chow parameters of $g$ in $\ell_2$-norm,
then $f$ is close to $g$ in $\ell_1$-distance. Notably, our bound relating the
two distances is completely independent of the dimension $n$. That is, we show
that Boolean degree-$d$ PTFs are {\em robustly identifiable} from their
degree-$d$ Chow parameters. Results of this form had been shown for the $d=1$
case~\cite{OS11:chow, DeDFS14}, but no non-trivial bound was previously known
for $d >1$.
  Our robust identifiability result gives the following algorithmic
applications: First, we show that Boolean degree-$d$ PTFs can be efficiently
approximately reconstructed from approximations to their degree-$d$ Chow
parameters. This immediately implies that degree-$d$ PTFs are efficiently
learnable in the uniform distribution $d$-RFA
model~\cite{BenDavidDichterman:98}. As a byproduct of our approach, we also
obtain the first low integer-weight approximations of degree-$d$ PTFs, for
$d>1$. As our second application, our robust identifiability result gives the
first efficient algorithm, with dimension-independent error guarantees, for
malicious learning of Boolean degree-$d$ PTFs under the uniform distribution.
Explaining Deep Learning Models - A Bayesian Non-parametric Approach
Authors: Wenbo Guo, Sui Huang, Yunzhe Tao, Xinyu Xing, Lin Lin
Date: 2018-11-07 00:00:00
Tasks: Deep Learning

Understanding and interpreting how machine learning (ML) models make
decisions have been a big challenge. While recent research has proposed various
technical approaches to provide some clues as to how an ML model makes
individual predictions, they cannot provide users with an ability to inspect a
model as a complete entity. In this work, we propose a novel technical approach
that augments a Bayesian non-parametric regression mixture model with multiple
elastic nets. Using the enhanced mixture model, we can extract generalizable
insights for a target model through a global approximation. To demonstrate the
utility of our approach, we evaluate it on different ML models in the context
of image recognition. The empirical results indicate that our proposed approach
not only outperforms the state-of-the-art techniques in explaining individual
decisions but also provides users with an ability to discover the
vulnerabilities of the target ML models.
Analysis of Multilingual Sequence-to-Sequence speech recognition systems
Authors: Martin Karafiát, Murali Karthick Baskar, Shinji Watanabe, Takaaki Hori, Matthew Wiesner, Jan "Honza'' Černocký
Date: 2018-11-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Sequence-To-Sequence Speech Recognition, speech-recognition, Speech Recognition, Transfer Learning

This paper investigates the applications of various multilingual approaches
developed in conventional hidden Markov model (HMM) systems to
sequence-to-sequence (seq2seq) automatic speech recognition (ASR). On a set
composed of Babel data, we first show the effectiveness of multi-lingual
training with stacked bottle-neck (SBN) features. Then we explore various
architectures and training strategies of multi-lingual seq2seq models based on
CTC-attention networks including combinations of output layer, CTC and/or
attention component re-training. We also investigate the effectiveness of
language-transfer learning in a very low resource scenario when the target
language is not included in the original multi-lingual training data.
Interestingly, we found multilingual features superior to multilingual models,
and this finding suggests that we can efficiently combine the benefits of the
HMM system with the seq2seq system through these multilingual feature
techniques.
Optimized Hidden Markov Model based on Constrained Particle Swarm Optimization
Authors: L. Chang, Yacine Ouzrout, Antoine Nongaillard, Abdelaziz Bouras
Date: 2018-11-07 00:00:00

As one of Bayesian analysis tools, Hidden Markov Model (HMM) has been used to
in extensive applications. Most HMMs are solved by Baum-Welch algorithm (BWHMM)
to predict the model parameters, which is difficult to find global optimal
solutions. This paper proposes an optimized Hidden Markov Model with Particle
Swarm Optimization (PSO) algorithm and so is called PSOHMM. In order to
overcome the statistical constraints in HMM, the paper develops
re-normalization and re-mapping mechanisms to ensure the constraints in HMM.
The experiments have shown that PSOHMM can search better solution than BWHMM,
and has faster convergence speed.
CAAD 2018: Iterative Ensemble Adversarial Attack
Authors: Jiayang Liu, Weiming Zhang, Nenghai Yu
Date: 2018-11-07 00:00:00
Tasks: Adversarial Attack

Deep Neural Networks (DNNs) have recently led to significant improvements in
many fields. However, DNNs are vulnerable to adversarial examples which are
samples with imperceptible perturbations while dramatically misleading the
DNNs. Adversarial attacks can be used to evaluate the robustness of deep
learning models before they are deployed. Unfortunately, most of existing
adversarial attacks can only fool a black-box model with a low success rate. To
improve the success rates for black-box adversarial attacks, we proposed an
iterated adversarial attack against an ensemble of image classifiers. With this
method, we won the 5th place in CAAD 2018 Targeted Adversarial Attack
competition.
Integrative Biological Simulation, Neuropsychology, and AI Safety
Authors: Gopal P. Sarma, Adam Safron, Nick J. Hay
Date: 2018-11-07 00:00:00

We describe a biologically-inspired research agenda with parallel tracks
aimed at AI and AI safety. The bottom-up component consists of building a
sequence of biophysically realistic simulations of simple organisms such as the
nematode $Caenorhabditis$ $elegans$, the fruit fly $Drosophila$ $melanogaster$,
and the zebrafish $Danio$ $rerio$ to serve as platforms for research into AI
algorithms and system architectures. The top-down component consists of an
approach to value alignment that grounds AI goal structures in neuropsychology,
broadly considered. Our belief is that parallel pursuit of these tracks will
inform the development of value-aligned AI systems that have been inspired by
embodied organisms with sensorimotor integration. An important set of side
benefits is that the research trajectories we describe here are grounded in
long-standing intellectual traditions within existing research communities and
funding structures. In addition, these research programs overlap with
significant contemporary themes in the biological and psychological sciences
such as data/model integration and reproducibility.
Multi-channel discourse as an indicator for Bitcoin price and volume movements
Authors: Marvin Aron Kennis
Date: 2018-11-06 00:00:00

This research aims to identify how Bitcoin-related news publications and
online discourse are expressed in Bitcoin exchange movements of price and
volume. Being inherently digital, all Bitcoin-related fundamental data (from
exchanges, as well as transactional data directly from the blockchain) is
available online, something that is not true for traditional businesses or
currencies traded on exchanges. This makes Bitcoin an interesting subject for
such research, as it enables the mapping of sentiment to fundamental events
that might otherwise be inaccessible. Furthermore, Bitcoin discussion largely
takes place on online forums and chat channels. In stock trading, the value of
sentiment data in trading decisions has been demonstrated numerous times [1]
[2] [3], and this research aims to determine whether there is value in such
data for Bitcoin trading models. To achieve this, data over the year 2015 has
been collected from Bitcointalk.org, (the biggest Bitcoin forum in post
volume), established news sources such as Bloomberg and the Wall Street
Journal, the complete /r/btc and /r/Bitcoin subreddits, and the bitcoin-otc and
bitcoin-dev IRC channels. By analyzing this data on sentiment and volume, we
find weak to moderate correlations between forum, news, and Reddit sentiment
and movements in price and volume from 1 to 5 days after the sentiment was
expressed. A Granger causality test confirms the predictive causality of the
sentiment on the daily percentage price and volume movements, and at the same
time underscores the predictive causality of market movements on sentiment
expressions in online communities
Phenotyping Endometriosis through Mixed Membership Models of Self-Tracking Data
Authors: Iñigo Urteaga, Mollie McKillop, Sharon Lipsky-Gorman, Noémie Elhadad
Date: 2018-11-06 00:00:00

We investigate the use of self-tracking data and unsupervised
mixed-membership models to phenotype endometriosis. Endometriosis is a
systemic, chronic condition of women in reproductive age and, at the same time,
a highly enigmatic condition with no known biomarkers to monitor its
progression and no established staging. We leverage data collected through a
self-tracking app in an observational research study of over 2,800 women with
endometriosis tracking their condition over a year and a half (456,900
observations overall). We extend a classical mixed-membership model to
accommodate the idiosyncrasies of the data at hand (i.e., the multimodality of
the tracked variables). Our experiments show that our approach identifies
potential subtypes that are robust in terms of biases of self-tracked data
(e.g., wide variations in tracking frequency amongst participants), as well as
to variations in hyperparameters of the model. Jointly modeling a wide range of
observations about participants (symptoms, quality of life, treatments) yields
clinically meaningful subtypes that both validate what is already known about
endometriosis and suggest new findings.
Data Science as Political Action: Grounding Data Science in a Politics of Justice
Authors: Ben Green
Date: 2018-11-06 00:00:00
Tasks: Ethics

In response to public scrutiny of data-driven algorithms, the field of data science has adopted ethics training and principles. Although ethics can help data scientists reflect on certain normative aspects of their work, such efforts are ill-equipped to generate a data science that avoids social harms and promotes social justice. In this article, I argue that data science must embrace a political orientation. Data scientists must recognize themselves as political actors engaged in normative constructions of society and evaluate their work according to its downstream impacts on people's lives. I first articulate why data scientists must recognize themselves as political actors. In this section, I respond to three arguments that data scientists commonly invoke when challenged to take political positions regarding their work. In confronting these arguments, I describe why attempting to remain apolitical is itself a political stance--a fundamentally conservative one--and why data science's attempts to promote "social good" dangerously rely on unarticulated and incrementalist political assumptions. I then propose a framework for how data science can evolve toward a deliberative and rigorous politics of social justice. I conceptualize the process of developing a politically engaged data science as a sequence of four stages. Pursuing these new approaches will empower data scientists with new methods for thoughtfully and rigorously contributing to social justice.
Deep BV: A Fully Automated System for Brain Ventricle Localization and Segmentation in 3D Ultrasound Images of Embryonic Mice
Authors: Ziming Qiu, Jack Langerman, Nitin Nair, Orlando Aristizabal, Jonathan Mamou, Daniel H. Turnbull, Jeffrey Ketterling, Yao Wang
Date: 2018-11-05 00:00:00
Tasks: Brain Ventricle Localization And Segmentation In 3D Ultrasound Images, Segmentation

Volumetric analysis of brain ventricle (BV) structure is a key tool in the
study of central nervous system development in embryonic mice. High-frequency
ultrasound (HFU) is the only non-invasive, real-time modality available for
rapid volumetric imaging of embryos in utero. However, manual segmentation of
the BV from HFU volumes is tedious, time-consuming, and requires specialized
expertise. In this paper, we propose a novel deep learning based BV
segmentation system for whole-body HFU images of mouse embryos. Our fully
automated system consists of two modules: localization and segmentation. It
first applies a volumetric convolutional neural network on a 3D sliding window
over the entire volume to identify a 3D bounding box containing the entire BV.
It then employs a fully convolutional network to segment the detected bounding
box into BV and background. The system achieves a Dice Similarity Coefficient
(DSC) of 0.8956 for BV segmentation on an unseen 111 HFU volume test set
surpassing the previous state-of-the-art method (DSC of 0.7119) by a margin of
25%.
Integrating Project Spatial Coordinates into Pavement Management Prioritization
Authors: Shadi Hanandeh, Omar Elbagalati, Mustafa Hajij
Date: 2018-11-05 00:00:00
Tasks: Clustering, Decision Making, Management

To date, pavement management software products and studies on optimizing the prioritization of pavement maintenance and rehabilitation (M&R) have been mainly focused on three parameters; the pre-treatment pavement condition, the rehabilitation cost, and the available budget. Yet, the role of the candidate projects' spatial characteristics in the decision-making process has not been deeply considered. Such a limitation, predominately, allows the recommended M&R projects' schedule to involve simultaneously running but spatially scattered construction sites, which are very challenging to monitor and manage. This study introduces a novel approach to integrate pavement segments' spatial coordinates into the M&R prioritization analysis. The introduced approach aims at combining the pavement segments with converged spatial coordinates to be repaired in the same timeframe without compromising the allocated budget levels or the overall target Pavement Condition Index (PCI). Such a combination would result in minimizing the routing of crews, materials and other equipment among the construction sites and would provide better collaborations and communications between the pavement maintenance teams. Proposed herein is a novel spatial clustering algorithm that automatically finds the projects within a certain budget and spatial constrains. The developed algorithm was successfully validated using 1,800 pavement maintenance projects from two real-life examples of the City of Milton, GA and the City of Tyler, TX.
Face Detection Using Adaboosted SVM-Based Component Classifier
Authors: Seyyed Majid Valiollahzadeh, Abolghasem Sayadiyan, Mohammad Nazari
Date: 2008-12-13 00:00:00
Tasks: Face Detection, General Classification, imbalanced classification

Recently, Adaboost has been widely used to improve the accuracy of any given
learning algorithm. In this paper we focus on designing an algorithm to employ
combination of Adaboost with Support Vector Machine as weak component
classifiers to be used in Face Detection Task. To obtain a set of effective
SVM-weaklearner Classifier, this algorithm adaptively adjusts the kernel
parameter in SVM instead of using a fixed one. Proposed combination outperforms
in generalization in comparison with SVM on imbalanced classification problem.
The proposed here method is compared, in terms of classification accuracy, to
other commonly used Adaboost methods, such as Decision Trees and Neural
Networks, on CMU+MIT face database. Results indicate that the performance of
the proposed method is overall superior to previous Adaboost approaches.
Active Testing for Face Detection and Localization
Authors: Raphael Sznitman, Bruno Jedynak
Date: 2010-03-27 00:00:00
Tasks: Face Detection

We provide a novel search technique, which uses a hierarchical model and a
mutual information gain heuristic to efficiently prune the search space when
localizing faces in images. We show exponential gains in computation over
traditional sliding window approaches, while keeping similar performance
levels.
Rotation Invariant Face Detection Using Wavelet, PCA and Radial Basis Function Networks
Authors: S. M. Kamruzzaman, Firoz Ahmed Siddiqi, Md. Saiful Islam, Md. Emdadul Haque, Mohammad Shamsul Alam
Date: 2010-09-25 00:00:00
Tasks: Face Detection

This paper introduces a novel method for human face detection with its
orientation by using wavelet, principle component analysis (PCA) and redial
basis networks. The input image is analyzed by two-dimensional wavelet and a
two-dimensional stationary wavelet. The common goals concern are the image
clearance and simplification, which are parts of de-noising or compression. We
applied an effective procedure to reduce the dimension of the input vectors
using PCA. Radial Basis Function (RBF) neural network is then used as a
function approximation network to detect where either the input image is
contained a face or not and if there is a face exists then tell about its
orientation. We will show how RBF can perform well then back-propagation
algorithm and give some solution for better regularization of the RBF (GRNN)
network. Compared with traditional RBF networks, the proposed network
demonstrates better capability of approximation to underlying functions, faster
learning speed, better size of network, and high robustness to outliers.
Face Detection with Effective Feature Extraction
Authors: Sakrapee Paisitkriangkrai, Chunhua Shen, Jian Zhang
Date: 2010-09-29 00:00:00
Tasks: Face Detection

There is an abundant literature on face detection due to its important role
in many vision applications. Since Viola and Jones proposed the first real-time
AdaBoost based face detector, Haar-like features have been adopted as the
method of choice for frontal face detection. In this work, we show that simple
features other than Haar-like features can also be applied for training an
effective face detector. Since, single feature is not discriminative enough to
separate faces from difficult non-faces, we further improve the generalization
performance of our simple features by introducing feature co-occurrences. We
demonstrate that our proposed features yield a performance improvement compared
to Haar-like features. In addition, our findings indicate that features play a
crucial role in the ability of the system to generalize.
Hamiltonian Streamline Guided Feature Extraction with Applications to Face Detection
Authors: Yingjie Miao, Jason J. Corso
Date: 2011-08-17 00:00:00
Tasks: Face Detection, object-detection, Object Detection

We propose a new feature extraction method based on two dynamical systems
induced by intensity landscape: the negative gradient system and the
Hamiltonian system. We build features based on the Hamiltonian streamlines.
These features contain nice global topological information about the intensity
landscape, and can be used for object detection. We show that for training
images of same size, our feature space is much smaller than that generated by
Haar-like features. The training time is extremely short, and detection speed
and accuracy is similar to Haar-like feature based classifiers.
A robust, low-cost approach to Face Detection and Face Recognition
Authors: Divya Jyoti, Aman Chadha, Pallavi Vaidya, M. Mani Roja
Date: 2011-11-04 00:00:00
Tasks: Face Detection, Face Recognition

In the domain of Biometrics, recognition systems based on iris, fingerprint
or palm print scans etc. are often considered more dependable due to extremely
low variance in the properties of these entities with respect to time. However,
over the last decade data processing capability of computers has increased
manifold, which has made real-time video content analysis possible. This shows
that the need of the hour is a robust and highly automated Face Detection and
Recognition algorithm with credible accuracy rate. The proposed Face Detection
and Recognition system using Discrete Wavelet Transform (DWT) accepts face
frames as input from a database containing images from low cost devices such as
VGA cameras, webcams or even CCTV's, where image quality is inferior. Face
region is then detected using properties of L*a*b* color space and only Frontal
Face is extracted such that all additional background is eliminated. Further,
this extracted image is converted to grayscale and its dimensions are resized
to 128 x 128 pixels. DWT is then applied to entire image to obtain the
coefficients. Recognition is carried out by comparison of the DWT coefficients
belonging to the test image with those of the registered reference image. On
comparison, Euclidean distance classifier is deployed to validate the test
image from the database. Accuracy for various levels of DWT Decomposition is
obtained and hence, compared.
Novel Architecture for 3D model in virtual communities from detected face
Authors: Vibekananda Dutta, Dr Nishtha Kesswani, Deepti Gahalot
Date: 2012-10-23 00:00:00

In this research paper we suggest how to extract a face from an image, modify
it, characterize it in terms of high-level properties, and apply it to the
creation of a personalized avatar. In this research work we tested, we
implemented the algorithm on several hundred facial images, including many
taken under uncontrolled acquisition conditions, and found to exhibit
satisfactory performance for immediate practical use.