pwc-archive/papers-with-abstracts huggingface dataset:
A multi-task convolutional neural network for mega-city analysis using very high resolution satellite imagery and geospatial data
Authors: Fan Zhang, Bo Du, Liangpei Zhang
Date: 2017-02-26 00:00:00

Mega-city analysis with very high resolution (VHR) satellite images has been
drawing increasing interest in the fields of city planning and social
investigation. It is known that accurate land-use, urban density, and
population distribution information is the key to mega-city monitoring and
environmental studies. Therefore, how to generate land-use, urban density, and
population distribution maps at a fine scale using VHR satellite images has
become a hot topic. Previous studies have focused solely on individual tasks
with elaborate hand-crafted features and have ignored the relationship between
different tasks. In this study, we aim to propose a universal framework which
can: 1) automatically learn the internal feature representation from the raw
image data; and 2) simultaneously produce fine-scale land-use, urban density,
and population distribution maps. For the first target, a deep convolutional
neural network (CNN) is applied to learn the hierarchical feature
representation from the raw image data. For the second target, a novel
CNN-based universal framework is proposed to process the VHR satellite images
and generate the land-use, urban density, and population distribution maps. To
the best of our knowledge, this is the first CNN-based mega-city analysis
method which can process a VHR remote sensing image with such a large data
volume. A VHR satellite image (1.2 m spatial resolution) of the center of Wuhan
covering an area of 2606 km2 was used to evaluate the proposed method. The
experimental results confirm that the proposed method can achieve a promising
accuracy for land-use, urban density, and population distribution maps.
Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
Authors: Tong Che, Yan-ran Li, Ruixiang Zhang, R. Devon Hjelm, Wenjie Li, Yangqiu Song, Yoshua Bengio
Date: 2017-02-26 00:00:00

Despite the successes in capturing continuous distributions, the application
of generative adversarial networks (GANs) to discrete settings, like natural
language tasks, is rather restricted. The fundamental reason is the difficulty
of back-propagation through discrete random variables combined with the
inherent instability of the GAN training objective. To address these problems,
we propose Maximum-Likelihood Augmented Discrete Generative Adversarial
Networks. Instead of directly optimizing the GAN objective, we derive a novel
and low-variance objective using the discriminator's output that follows
corresponds to the log-likelihood. Compared with the original, the new
objective is proved to be consistent in theory and beneficial in practice. The
experimental results on various discrete datasets demonstrate the effectiveness
of the proposed approach.
Ratio Utility and Cost Analysis for Privacy Preserving Subspace Projection
Authors: Mert Al, Shibiao Wan, Sun-Yuan Kung
Date: 2017-02-26 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Privacy Preserving

With a rapidly increasing number of devices connected to the internet, big
data has been applied to various domains of human life. Nevertheless, it has
also opened new venues for breaching users' privacy. Hence it is highly
required to develop techniques that enable data owners to privatize their data
while keeping it useful for intended applications. Existing methods, however,
do not offer enough flexibility for controlling the utility-privacy trade-off
and may incur unfavorable results when privacy requirements are high. To tackle
these drawbacks, we propose a compressive-privacy based method, namely RUCA
(Ratio Utility and Cost Analysis), which can not only maximize performance for
a privacy-insensitive classification task but also minimize the ability of any
classifier to infer private information from the data. Experimental results on
Census and Human Activity Recognition data sets demonstrate that RUCA
significantly outperforms existing privacy preserving data projection
techniques for a wide range of privacy pricings.
Building Fast and Compact Convolutional Neural Networks for Offline Handwritten Chinese Character Recognition
Authors: Xuefeng Xiao, Lianwen Jin, Yafeng Yang, Weixin Yang, Jun Sun, Tianhai Chang
Date: 2017-02-26 00:00:00
Tasks: CPU, Offline Handwritten Chinese Character Recognition

Like other problems in computer vision, offline handwritten Chinese character
recognition (HCCR) has achieved impressive results using convolutional neural
network (CNN)-based methods. However, larger and deeper networks are needed to
deliver state-of-the-art results in this domain. Such networks intuitively
appear to incur high computational cost, and require the storage of a large
number of parameters, which renders them unfeasible for deployment in portable
devices. To solve this problem, we propose a Global Supervised Low-rank
Expansion (GSLRE) method and an Adaptive Drop-weight (ADW) technique to solve
the problems of speed and storage capacity. We design a nine-layer CNN for HCCR
consisting of 3,755 classes, and devise an algorithm that can reduce the
networks computational cost by nine times and compress the network to 1/18 of
the original size of the baseline model, with only a 0.21% drop in accuracy. In
tests, the proposed algorithm surpassed the best single-network performance
reported thus far in the literature while requiring only 2.3 MB for storage.
Furthermore, when integrated with our effective forward implementation, the
recognition of an offline character image took only 9.7 ms on a CPU. Compared
with the state-of-the-art CNN model for HCCR, our approach is approximately 30
times faster, yet 10 times more cost efficient.
Seeing What Is Not There: Learning Context to Determine Where Objects Are Missing
Authors: Jin Sun, David W. Jacobs
Date: 2017-02-26 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, object-detection, Object Detection

Most of computer vision focuses on what is in an image. We propose to train a
standalone object-centric context representation to perform the opposite task:
seeing what is not there. Given an image, our context model can predict where
objects should exist, even when no object instances are present. Combined with
object detection results, we can perform a novel vision task: finding where
objects are missing in an image. Our model is based on a convolutional neural
network structure. With a specially designed training strategy, the model
learns to ignore objects and focus on context only. It is fully convolutional
thus highly efficient. Experiments show the effectiveness of the proposed
approach in one important accessibility task: finding city street regions where
curb ramps are missing, which could help millions of people with mobility
disabilities.
Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs
Authors: Alon Brutzkus, Amir Globerson
Date: 2017-02-26 00:00:00
Proceeding: ICML 2017 8

Deep learning models are often successfully trained using gradient descent,
despite the worst case hardness of the underlying non-convex optimization
problem. The key question is then under what conditions can one prove that
optimization will succeed. Here we provide a strong result of this kind. We
consider a neural net with one hidden layer and a convolutional structure with
no overlap and a ReLU activation function. For this architecture we show that
learning is NP-complete in the general case, but that when the input
distribution is Gaussian, gradient descent converges to the global optimum in
polynomial time. To the best of our knowledge, this is the first global
optimality guarantee of gradient descent on a convolutional neural network with
ReLU activations.
Structured Prediction by Conditional Risk Minimization
Authors: Chong Yang Goh, Patrick Jaillet
Date: 2016-11-21 00:00:00
Tasks: Prediction, Structured Prediction

We propose a general approach for supervised learning with structured output
spaces, such as combinatorial and polyhedral sets, that is based on minimizing
estimated conditional risk functions. Given a loss function defined over pairs
of output labels, we first estimate the conditional risk function by solving a
(possibly infinite) collection of regularized least squares problems. A
prediction is made by solving an inference problem that minimizes the estimated
conditional risk function over the output space. We show that this approach
enables, in some cases, efficient training and inference without explicitly
introducing a convex surrogate for the original loss function, even when it is
discontinuous. Empirical evaluations on real-world and synthetic data sets
demonstrate the effectiveness of our method in adapting to a variety of loss
functions.
Spatially Aware Melanoma Segmentation Using Hybrid Deep Learning Techniques
Authors: M. Attia, M. Hossny, S. Nahavandi, A. Yazdabadi
Date: 2017-02-26 00:00:00
Tasks: Deep Learning, Lesion Segmentation, Segmentation

In this paper, we proposed using a hybrid method that utilises deep
convolutional and recurrent neural networks for accurate delineation of skin
lesion of images supplied with ISBI 2017 lesion segmentation challenge. The
proposed method was trained using 1800 images and tested on 150 images from
ISBI 2017 challenge.
Enforcing Relational Matching Dependencies with Datalog for Entity Resolution
Authors: Zeinab Bahmani, Leopoldo Bertossi
Date: 2016-11-21 00:00:00
Tasks: Entity Resolution

Entity resolution (ER) is about identifying and merging records in a database
that represent the same real-world entity. Matching dependencies (MDs) have
been introduced and investigated as declarative rules that specify ER policies.
An ER process induced by MDs over a dirty instance leads to multiple clean
instances, in general. General "answer sets programs" have been proposed to
specify the MD-based cleaning task and its results. In this work, we extend MDs
to "relational MDs", which capture more application semantics, and identify
classes of relational MDs for which the general ASP can be automatically
rewritten into a stratified Datalog program, with the single clean instance as
its standard model.
Visual Closed-Loop Control for Pouring Liquids
Authors: Connor Schenck, Dieter Fox
Date: 2016-10-09 00:00:00

Pouring a specific amount of liquid is a challenging task. In this paper we
develop methods for robots to use visual feedback to perform closed-loop
control for pouring liquids. We propose both a model-based and a model-free
method utilizing deep learning for estimating the volume of liquid in a
container. Our results show that the model-free method is better able to
estimate the volume. We combine this with a simple PID controller to pour
specific amounts of liquid, and show that the robot is able to achieve an
average 38ml deviation from the target amount. To our knowledge, this is the
first use of raw visual feedback to pour liquids in robotics.
BARCHAN: Blob Alignment for Robust CHromatographic ANalysis
Authors: Camille Couprie, Laurent Duval, Maxime Moreaud, Sophie Hénon, Mélinda Tebib, Vincent Souchon
Date: 2017-02-25 00:00:00

Comprehensive Two dimensional gas chromatography (GCxGC) plays a central role
into the elucidation of complex samples. The automation of the identification
of peak areas is of prime interest to obtain a fast and repeatable analysis of
chromatograms. To determine the concentration of compounds or pseudo-compounds,
templates of blobs are defined and superimposed on a reference chromatogram.
The templates then need to be modified when different chromatograms are
recorded. In this study, we present a chromatogram and template alignment
method based on peak registration called BARCHAN. Peaks are identified using a
robust mathematical morphology tool. The alignment is performed by a
probabilistic estimation of a rigid transformation along the first dimension,
and a non-rigid transformation in the second dimension, taking into account
noise, outliers and missing peaks in a fully automated way. Resulting aligned
chromatograms and masks are presented on two datasets. The proposed algorithm
proves to be fast and reliable. It significantly reduces the time to results
for GCxGC analysis.
Ex Machina: Personal Attacks Seen at Scale
Authors: Ellery Wulczyn, Nithum Thain, Lucas Dixon
Date: 2016-10-27 00:00:00

The damage personal attacks cause to online discourse motivates many
platforms to try to curb the phenomenon. However, understanding the prevalence
and impact of personal attacks in online platforms at scale remains
surprisingly difficult. The contribution of this paper is to develop and
illustrate a method that combines crowdsourcing and machine learning to analyze
personal attacks at scale. We show an evaluation method for a classifier in
terms of the aggregated number of crowd-workers it can approximate. We apply
our methodology to English Wikipedia, generating a corpus of over 100k high
quality human-labeled comments and 63M machine-labeled ones from a classifier
that is as good as the aggregate of 3 crowd-workers, as measured by the area
under the ROC curve and Spearman correlation. Using this corpus of
machine-labeled scores, our methodology allows us to explore some of the open
questions about the nature of online personal attacks. This reveals that the
majority of personal attacks on Wikipedia are not the result of a few malicious
users, nor primarily the consequence of allowing anonymous contributions from
unregistered users.
Characterization of the equivalence of robustification and regularization in linear and matrix regression
Authors: Dimitris Bertsimas, Martin S. Copenhaver
Date: 2014-11-22 00:00:00
Tasks: Matrix Completion, regression

The notion of developing statistical methods in machine learning which are
robust to adversarial perturbations in the underlying data has been the subject
of increasing interest in recent years. A common feature of this work is that
the adversarial robustification often corresponds exactly to regularization
methods which appear as a loss function plus a penalty. In this paper we deepen
and extend the understanding of the connection between robustification and
regularization (as achieved by penalization) in regression problems.
Specifically, (a) in the context of linear regression, we characterize
precisely under which conditions on the model of uncertainty used and on the
loss function penalties robustification and regularization are equivalent, and
(b) we extend the characterization of robustification and regularization to
matrix regression problems (matrix completion and Principal Component
Analysis).
CHAOS: A Parallelization Scheme for Training Convolutional Neural Networks on Intel Xeon Phi
Authors: Andre Viebke, Suejb Memeti, Sabri Pllana, Ajith Abraham
Date: 2017-02-25 00:00:00
Tasks: Self-Driving Cars, speech-recognition, Speech Recognition

Deep learning is an important component of big-data analytic tools and
intelligent applications, such as, self-driving cars, computer vision, speech
recognition, or precision medicine. However, the training process is
computationally intensive, and often requires a large amount of time if
performed sequentially. Modern parallel computing systems provide the
capability to reduce the required training time of deep neural networks. In
this paper, we present our parallelization scheme for training convolutional
neural networks (CNN) named Controlled Hogwild with Arbitrary Order of
Synchronization (CHAOS). Major features of CHAOS include the support for thread
and vector parallelism, non-instant updates of weight parameters during
back-propagation without a significant delay, and implicit synchronization in
arbitrary order. CHAOS is tailored for parallel computing systems that are
accelerated with the Intel Xeon Phi. We evaluate our parallelization approach
empirically using measurement techniques and performance modeling for various
numbers of threads and CNN architectures. Experimental results for the MNIST
dataset of handwritten digits using the total number of threads on the Xeon Phi
show speedups of up to 103x compared to the execution on one thread of the Xeon
Phi, 14x compared to the sequential execution on Intel Xeon E5, and 58x
compared to the sequential execution on Intel Core i5.
Coarse Grained Exponential Variational Autoencoders
Authors: Ke Sun, Xiangliang Zhang
Date: 2017-02-25 00:00:00

Variational autoencoders (VAE) often use Gaussian or category distribution to
model the inference process. This puts a limit on variational learning because
this simplified assumption does not match the true posterior distribution,
which is usually much more sophisticated. To break this limitation and apply
arbitrary parametric distribution during inference, this paper derives a
\emph{semi-continuous} latent representation, which approximates a continuous
density up to a prescribed precision, and is much easier to analyze than its
continuous counterpart because it is fundamentally discrete. We showcase the
proposition by applying polynomial exponential family distributions as the
posterior, which are universal probability density function generators. Our
experimental results show consistent improvements over commonly used VAE
models.
Bayesian Boolean Matrix Factorisation
Authors: Tammo Rukat, Chris C. Holmes, Michalis K. Titsias, Christopher Yau
Date: 2017-02-20 00:00:00
Proceeding: ICML 2017 8
Tasks: Collaborative Filtering

Boolean matrix factorisation aims to decompose a binary data matrix into an
approximate Boolean product of two low rank, binary matrices: one containing
meaningful patterns, the other quantifying how the observations can be
expressed as a combination of these patterns. We introduce the OrMachine, a
probabilistic generative model for Boolean matrix factorisation and derive a
Metropolised Gibbs sampler that facilitates efficient parallel posterior
inference. On real world and simulated data, our method outperforms all
currently existing approaches for Boolean matrix factorisation and completion.
This is the first method to provide full posterior inference for Boolean Matrix
factorisation which is relevant in applications, e.g. for controlling false
positive rates in collaborative filtering and, crucially, improves the
interpretability of the inferred patterns. The proposed algorithm scales to
large datasets as we demonstrate by analysing single cell gene expression data
in 1.3 million mouse brain cells across 11 thousand genes on commodity
hardware.
Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?
Authors: Matthew Johnson-Roberson, Charles Barto, Rounak Mehta, Sharath Nittur Sridhar, Karl Rosaen, Ram Vasudevan
Date: 2016-10-06 00:00:00
Tasks: BIG-bench Machine Learning, Self-Driving Cars, vehicle detection

Deep learning has rapidly transformed the state of the art algorithms used to
address a variety of problems in computer vision and robotics. These
breakthroughs have relied upon massive amounts of human annotated training
data. This time consuming process has begun impeding the progress of these deep
learning efforts. This paper describes a method to incorporate photo-realistic
computer images from a simulation engine to rapidly generate annotated data
that can be used for the training of machine learning algorithms. We
demonstrate that a state of the art architecture, which is trained only using
these synthetic annotations, performs better than the identical architecture
trained on human annotated real-world data, when tested on the KITTI data set
for vehicle detection. By training machine learning algorithms on a rich
virtual world, real objects in real scenes can be learned and classified using
synthetic data. This approach offers the possibility of accelerating deep
learning's application to sensor-based classification problems like those that
appear in self-driving cars. The source code and data to train and validate the
networks described in this paper are made available for researchers.
Contractibility for Open Global Constraints
Authors: Michael J. Maher
Date: 2017-02-25 00:00:00

Open forms of global constraints allow the addition of new variables to an
argument during the execution of a constraint program. Such forms are needed
for difficult constraint programming problems where problem construction and
problem solving are interleaved, and fit naturally within constraint logic
programming. However, in general, filtering that is sound for a global
constraint can be unsound when the constraint is open. This paper provides a
simple characterization, called contractibility, of the constraints where
filtering remains sound when the constraint is open. With this characterization
we can easily determine whether a constraint has this property or not. In the
latter case, we can use it to derive a contractible approximation to the
constraint. We demonstrate this work on both hard and soft constraints. In the
process, we formulate two general classes of soft constraints.
An EM Based Probabilistic Two-Dimensional CCA with Application to Face Recognition
Authors: Mehran Safayani, Seyed Hashem Ahmadi, Homayun Afrabandpey, Abdolreza Mirzaei
Date: 2017-02-25 00:00:00
Tasks: Face Recognition

Recently, two-dimensional canonical correlation analysis (2DCCA) has been
successfully applied for image feature extraction. The method instead of
concatenating the columns of the images to the one-dimensional vectors,
directly works with two-dimensional image matrices. Although 2DCCA works well
in different recognition tasks, it lacks a probabilistic interpretation. In
this paper, we present a probabilistic framework for 2DCCA called probabilistic
2DCCA (P2DCCA) and an iterative EM based algorithm for optimizing the
parameters. Experimental results on synthetic and real data demonstrate
superior performance in loading factor estimation for P2DCCA compared to 2DCCA.
For real data, three subsets of AR face database and also the UMIST face
database confirm the robustness of the proposed algorithm in face recognition
tasks with different illumination conditions, facial expressions, poses and
occlusions.
Monte Carlo Action Programming
Authors: Lenz Belzner
Date: 2017-02-25 00:00:00

This paper proposes Monte Carlo Action Programming, a programming language
framework for autonomous systems that act in large probabilistic state spaces
with high branching factors. It comprises formal syntax and semantics of a
nondeterministic action programming language. The language is interpreted
stochastically via Monte Carlo Tree Search. Effectiveness of the approach is
shown empirically.
Online Learning with Many Experts
Authors: Alon Cohen, Shie Mannor
Date: 2017-02-25 00:00:00

We study the problem of prediction with expert advice when the number of
experts in question may be extremely large or even infinite. We devise an
algorithm that obtains a tight regret bound of $\widetilde{O}(\epsilon T + N +
\sqrt{NT})$, where $N$ is the empirical $\epsilon$-covering number of the
sequence of loss functions generated by the environment. In addition, we
present a hedging procedure that allows us to find the optimal $\epsilon$ in
hindsight.
  Finally, we discuss a few interesting applications of our algorithm. We show
how our algorithm is applicable in the approximately low rank experts model of
Hazan et al. (2016), and discuss the case of experts with bounded variation, in
which there is a surprisingly large gap between the regret bounds obtained in
the statistical and online settings.