pwc-archive/papers-with-abstracts huggingface dataset:
Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations
Authors: Maziar Raissi, Paris Perdikaris, George Em. Karniadakis
Date: 2017-03-29 00:00:00
Tasks: Gaussian Processes

We introduce the concept of numerical Gaussian processes, which we define as
Gaussian processes with covariance functions resulting from temporal
discretization of time-dependent partial differential equations. Numerical
Gaussian processes, by construction, are designed to deal with cases where: (1)
all we observe are noisy data on black-box initial conditions, and (2) we are
interested in quantifying the uncertainty associated with such noisy data in
our solutions to time-dependent partial differential equations. Our method
circumvents the need for spatial discretization of the differential operators
by proper placement of Gaussian process priors. This is an attempt to construct
structured and data-efficient learning machines, which are explicitly informed
by the underlying physics that possibly generated the observed data. The
effectiveness of the proposed approach is demonstrated through several
benchmark problems involving linear and nonlinear time-dependent operators. In
all examples, we are able to recover accurate approximations of the latent
solutions, and consistently propagate uncertainty, even in cases involving very
long time integration.
Detecting Human Interventions on the Landscape: KAZE Features, Poisson Point Processes, and a Construction Dataset
Authors: Edward Boyda, Colin McCormick, Dan Hammer
Date: 2017-03-29 00:00:00
Tasks: Point Processes

We present an algorithm capable of identifying a wide variety of
human-induced change on the surface of the planet by analyzing matches between
local features in time-sequenced remote sensing imagery. We evaluate feature
sets, match protocols, and the statistical modeling of feature matches. With
application of KAZE features, k-nearest-neighbor descriptor matching, and
geometric proximity and bi-directional match consistency checks, average match
rates increase more than two-fold over the previous standard. In testing our
platform, we developed a small, labeled benchmark dataset expressing
large-scale residential, industrial, and civic construction, along with null
instances, in California between the years 2010 and 2012. On the benchmark set,
our algorithm makes precise, accurate change proposals on two-thirds of scenes.
Further, the detection threshold can be tuned so that all or almost all
proposed detections are true positives.
Automatic Argumentative-Zoning Using Word2vec
Authors: Haixia Liu
Date: 2017-03-29 00:00:00
Tasks: Articles, Classification, Document Summarization, Feature Engineering, General Classification, Sentence, Sentence Classification, Word Embeddings

In comparison with document summarization on the articles from social media
and newswire, argumentative zoning (AZ) is an important task in scientific
paper analysis. Traditional methodology to carry on this task relies on feature
engineering from different levels. In this paper, three models of generating
sentence vectors for the task of sentence classification were explored and
compared. The proposed approach builds sentence representations using learned
embeddings based on neural network. The learned word embeddings formed a
feature space, to which the examined sentence is mapped to. Those features are
input into the classifiers for supervised classification. Using
10-cross-validation scheme, evaluation was conducted on the
Argumentative-Zoning (AZ) annotated articles. The results showed that simply
averaging the word vectors in a sentence works better than the paragraph to
vector algorithm and by integrating specific cuewords into the loss function of
the neural network can improve the classification performance. In comparison
with the hand-crafted features, the word2vec method won for most of the
categories. However, the hand-crafted features showed their strength on
classifying some of the categories.
Community Detection and Stochastic Block Models
Authors: Emmanuel Abbe
Date: 2017-03-29 00:00:00
Tasks: Clustering, Community Detection, Stochastic Block Model

The stochastic block model (SBM) is a random graph model with different group of vertices connecting differently. It is widely employed as a canonical model to study clustering and community detection, and provides a fertile ground to study the information-theoretic and computational tradeoffs that arise in combinatorial statistics and more generally data science. This monograph surveys the recent developments that establish the fundamental limits for community detection in the SBM, both with respect to information-theoretic and computational tradeoffs, and for various recovery requirements such as exact, partial and weak recovery. The main results discussed are the phase transitions for exact recovery at the Chernoff-Hellinger threshold, the phase transition for weak recovery at the Kesten-Stigum threshold, the optimal SNR-mutual information tradeoff for partial recovery, and the gap between information-theoretic and computational thresholds. The monograph gives a principled derivation of the main algorithms developed in the quest of achieving the limits, in particular two-round algorithms via graph-splitting, semi-definite programming, (linearized) belief propagation, classical/nonbacktracking spectral methods and graph powering. Extensions to other block models, such as geometric block models, and a few open problems are also discussed.
Google Map Aided Visual Navigation for UAVs in GPS-denied Environment
Authors: Mo Shan, Fei Wang, Feng Lin, Zhi Gao, Ya Z. Tang, Ben M. Chen
Date: 2017-03-29 00:00:00
Tasks: Optical Flow Estimation, Pose Tracking, Position, Translation, Visual Navigation

We propose a framework for Google Map aided UAV navigation in GPS-denied
environment. Geo-referenced navigation provides drift-free localization and
does not require loop closures. The UAV position is initialized via
correlation, which is simple and efficient. We then use optical flow to predict
its position in subsequent frames. During pose tracking, we obtain inter-frame
translation either by motion field or homography decomposition, and we use HOG
features for registration on Google Map. We employ particle filter to conduct a
coarse to fine search to localize the UAV. Offline test using aerial images
collected by our quadrotor platform shows promising results as our approach
eliminates the drift in dead-reckoning, and the small localization error
indicates the superiority of our approach as a supplement to GPS.
Semantic Perceptual Image Compression using Deep Convolution Networks
Authors: Aaditya Prakash, Nick Moran, Solomon Garber, Antonella DiLillo, James Storer
Date: 2016-12-27 00:00:00
Tasks: Image Compression, object-detection, Object Detection, Video Compression

It has long been considered a significant problem to improve the visual
quality of lossy image and video compression. Recent advances in computing
power together with the availability of large training data sets has increased
interest in the application of deep learning cnns to address image recognition
and image processing tasks. Here, we present a powerful cnn tailored to the
specific task of semantic image understanding to achieve higher visual quality
in lossy compression. A modest increase in complexity is incorporated to the
encoder which allows a standard, off-the-shelf jpeg decoder to be used. While
jpeg encoding may be optimized for generic images, the process is ultimately
unaware of the specific content of the image to be compressed. Our technique
makes jpeg content-aware by designing and training a model to identify multiple
semantic regions in a given image. Unlike object detection techniques, our
model does not require labeling of object positions and is able to identify
objects in a single pass. We present a new cnn architecture directed
specifically to image compression, which generates a map that highlights
semantically-salient regions so that they can be encoded at higher quality as
compared to background regions. By adding a complete set of features for every
class, and then taking a threshold over the sum of all feature activations, we
generate a map that highlights semantically-salient regions so that they can be
encoded at a better quality compared to background regions. Experiments are
presented on the Kodak PhotoCD dataset and the MIT Saliency Benchmark dataset,
in which our algorithm achieves higher visual quality for the same compressed
size.
The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study
Authors: Patrick Glauner, Manxing Du, Victor Paraschiv, Andrey Boytsov, Isabel Lopez Andrade, Jorge Meira, Petko Valtchev, Radu State
Date: 2017-03-29 00:00:00
Tasks: BIG-bench Machine Learning

Which topics of machine learning are most commonly addressed in research?
This question was initially answered in 2007 by doing a qualitative survey
among distinguished researchers. In our study, we revisit this question from a
quantitative perspective. Concretely, we collect 54K abstracts of papers
published between 2007 and 2016 in leading machine learning journals and
conferences. We then use machine learning in order to determine the top 10
topics in machine learning. We not only include models, but provide a holistic
view across optimization, data, features, etc. This quantitative approach
allows reducing the bias of surveys. It reveals new and up-to-date insights
into what the 10 most prolific topics in machine learning research are. This
allows researchers to identify popular topics as well as new and rising topics
for their research.
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
Authors: Nick Johnston, Damien Vincent, David Minnen, Michele Covell, Saurabh Singh, Troy Chinen, Sung Jin Hwang, Joel Shor, George Toderici
Date: 2017-03-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Compression, MS-SSIM, SSIM

We propose a method for lossy image compression based on recurrent,
convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000,
and JPEG as measured by MS-SSIM. We introduce three improvements over previous
research that lead to this state-of-the-art result. First, we show that
training with a pixel-wise loss weighted by SSIM increases reconstruction
quality according to several metrics. Second, we modify the recurrent
architecture to improve spatial diffusion, which allows the network to more
effectively capture and propagate image information through the network's
hidden state. Finally, in addition to lossless entropy coding, we use a
spatially adaptive bit allocation algorithm to more efficiently use the limited
number of bits to encode visually complex image regions. We evaluate our method
on the Kodak and Tecnick image sets and compare against standard codecs as well
recently published methods based on deep neural networks.
Rational Choice and Artificial Intelligence
Authors: Tshilidzi Marwala
Date: 2017-03-29 00:00:00

The theory of rational choice assumes that when people make decisions they do
so in order to maximize their utility. In order to achieve this goal they ought
to use all the information available and consider all the choices available to
choose an optimal choice. This paper investigates what happens when decisions
are made by artificially intelligent machines in the market rather than human
beings. Firstly, the expectations of the future are more consistent if they are
made by an artificially intelligent machine and the decisions are more rational
and thus marketplace becomes more rational.
A Short Review of Ethical Challenges in Clinical Natural Language Processing
Authors: Simon Šuster, Stéphan Tulkens, Walter Daelemans
Date: 2017-03-29 00:00:00
Proceeding: WS 2017 4

Clinical NLP has an immense potential in contributing to how clinical
practice will be revolutionized by the advent of large scale processing of
clinical records. However, this potential has remained largely untapped due to
slow progress primarily caused by strict data access policies for researchers.
In this paper, we discuss the concern for privacy and the measures it entails.
We also suggest sources of less sensitive data. Finally, we draw attention to
biases that can compromise the validity of empirical research and lead to
socially harmful applications.
Hierarchical Classification for Spoken Arabic Dialect Identification using Prosody: Case of Algerian Dialects
Authors: Soumia Bougrine, Hadda Cherroun, Djelloul Ziadi
Date: 2017-03-29 00:00:00
Tasks: Classification, Dialect Identification, General Classification

In daily communications, Arabs use local dialects which are hard to identify
automatically using conventional classification methods. The dialect
identification challenging task becomes more complicated when dealing with an
under-resourced dialects belonging to a same county/region. In this paper, we
start by analyzing statistically Algerian dialects in order to capture their
specificities related to prosody information which are extracted at utterance
level after a coarse-grained consonant/vowel segmentation. According to these
analysis findings, we propose a Hierarchical classification approach for spoken
Arabic algerian Dialect IDentification (HADID). It takes advantage from the
fact that dialects have an inherent property of naturally structured into
hierarchy. Within HADID, a top-down hierarchical classification is applied, in
which we use Deep Neural Networks (DNNs) method to build a local classifier for
every parent node into the hierarchy dialect structure. Our framework is
implemented and evaluated on Algerian Arabic dialects corpus. Whereas, the
hierarchy dialect structure is deduced from historic and linguistic knowledges.
The results reveal that within {\HD}, the best classifier is DNNs compared to
Support Vector Machine. In addition, compared with a baseline Flat
classification system, our HADID gives an improvement of 63.5% in term of
precision. Furthermore, overall results evidence the suitability of our
prosody-based HADID for speaker independent dialect identification while
requiring less than 6s test utterances.
Exploring Heritability of Functional Brain Networks with Inexact Graph Matching
Authors: Sofia Ira Ktena, Salim Arslan, Sarah Parisot, Daniel Rueckert
Date: 2017-03-29 00:00:00
Tasks: Functional Connectivity, Graph Matching

Data-driven brain parcellations aim to provide a more accurate representation
of an individual's functional connectivity, since they are able to capture
individual variability that arises due to development or disease. This renders
comparisons between the emerging brain connectivity networks more challenging,
since correspondences between their elements are not preserved. Unveiling these
correspondences is of major importance to keep track of local functional
connectivity changes. We propose a novel method based on graph edit distance
for the comparison of brain graphs directly in their domain, that can
accurately reflect similarities between individual networks while providing the
network element correspondences. This method is validated on a dataset of 116
twin subjects provided by the Human Connectome Project.
Optimal Policies for Observing Time Series and Related Restless Bandit Problems
Authors: Christopher R. Dance, Tomi Silander
Date: 2017-03-29 00:00:00
Tasks: Time Series, Time Series Analysis

The trade-off between the cost of acquiring and processing data, and
uncertainty due to a lack of data is fundamental in machine learning. A basic
instance of this trade-off is the problem of deciding when to make noisy and
costly observations of a discrete-time Gaussian random walk, so as to minimise
the posterior variance plus observation costs. We present the first proof that
a simple policy, which observes when the posterior variance exceeds a
threshold, is optimal for this problem. The proof generalises to a wide range
of cost functions other than the posterior variance.
  This result implies that optimal policies for linear-quadratic-Gaussian
control with costly observations have a threshold structure. It also implies
that the restless bandit problem of observing multiple such time series, has a
well-defined Whittle index. We discuss computation of that index, give
closed-form formulae for it, and compare the performance of the associated
index policy with heuristic policies.
  The proof is based on a new verification theorem that demonstrates threshold
structure for Markov decision processes, and on the relation between binary
sequences known as mechanical words and the dynamics of discontinuous nonlinear
maps, which frequently arise in physics, control and biology.
Object categorization in finer levels requires higher spatial frequencies, and therefore takes longer
Authors: Matin N. Ashtiani, Saeed Reza Kheradpisheh, Timothée Masquelier, Mohammad Ganjtabesh
Date: 2017-03-29 00:00:00
Tasks: Object Categorization, Object Recognition

The human visual system contains a hierarchical sequence of modules that take
part in visual perception at different levels of abstraction, i.e.,
superordinate, basic, and subordinate levels. One important question is to
identify the "entry" level at which the visual representation is commenced in
the process of object recognition. For a long time, it was believed that the
basic level had advantage over two others; a claim that has been challenged
recently. Here we used a series of psychophysics experiments, based on a rapid
presentation paradigm, as well as two computational models, with bandpass
filtered images to study the processing order of the categorization levels. In
these experiments, we investigated the type of visual information required for
categorizing objects in each level by varying the spatial frequency bands of
the input image. The results of our psychophysics experiments and computational
models are consistent. They indicate that the different spatial frequency
information had different effects on object categorization in each level. In
the absence of high frequency information, subordinate and basic level
categorization are performed inaccurately, while superordinate level is
performed well. This means that, low frequency information is sufficient for
superordinate level, but not for the basic and subordinate levels. These finer
levels require high frequency information, which appears to take longer to be
processed, leading to longer reaction times. Finally, to avoid the ceiling
effect, we evaluated the robustness of the results by adding different amounts
of noise to the input images and repeating the experiments. As expected, the
categorization accuracy decreased and the reaction time increased
significantly, but the trends were the same.This shows that our results are not
due to a ceiling effect.
Iterative Object and Part Transfer for Fine-Grained Recognition
Authors: Zhiqiang Shen, Yu-Gang Jiang, Dequan Wang, xiangyang xue
Date: 2017-03-29 00:00:00
Tasks: Object

The aim of fine-grained recognition is to identify sub-ordinate categories in
images like different species of birds. Existing works have confirmed that, in
order to capture the subtle differences across the categories, automatic
localization of objects and parts is critical. Most approaches for object and
part localization relied on the bottom-up pipeline, where thousands of region
proposals are generated and then filtered by pre-trained object/part models.
This is computationally expensive and not scalable once the number of
objects/parts becomes large. In this paper, we propose a nonparametric
data-driven method for object and part localization. Given an unlabeled test
image, our approach transfers annotations from a few similar images retrieved
in the training set. In particular, we propose an iterative transfer strategy
that gradually refine the predicted bounding boxes. Based on the located
objects and parts, deep convolutional features are extracted for recognition.
We evaluate our approach on the widely-used CUB200-2011 dataset and a new and
large dataset called Birdsnap. On both datasets, we achieve better results than
many state-of-the-art approaches, including a few using oracle (manually
annotated) bounding boxes in the test images.
Thermodynamic-RAM Technology Stack
Authors: M. Alexander Nugent, Timothy W. Molter
Date: 2014-06-21 00:00:00

We introduce a technology stack or specification describing the multiple
levels of abstraction and specialization needed to implement a neuromorphic
processor (NPU) based on the previously-described concept of AHaH Computing and
integrate it into today's digital computing systems. The general purpose NPU
implementation described here is called Thermodynamic-RAM (kT-RAM) and is just
one of many possible architectures, each with varying advantages and trade
offs. Bringing us closer to brain-like neural computation, kT-RAM will provide
a general-purpose adaptive hardware resource to existing computing platforms
enabling fast and low-power machine learning capabilities that are currently
hampered by the separation of memory and processing, a.k.a the von Neumann
bottleneck. Because understanding such a processor based on non-traditional
principles can be difficult, by presenting the various levels of the stack from
the bottom up, layer by layer, explaining kT-RAM becomes a much easier task.
The levels of the Thermodynamic-RAM technology stack include the memristor,
synapse, AHaH node, kT-RAM, instruction set, sparse spike encoding, kT-RAM
emulator, and SENSE server.
Improving Spectral Clustering using the Asymptotic Value of the Normalised Cut
Authors: David Hofmeyr
Date: 2017-03-29 00:00:00
Tasks: Clustering

Spectral clustering is a popular and versatile clustering method based on a relaxation of the normalised graph cut objective. Despite its popularity, however, there is no single agreed upon method for tuning the important scaling parameter, nor for determining automatically the number of clusters to extract. Popular heuristics exist, but corresponding theoretical results are scarce. In this paper we investigate the asymptotic value of the normalised cut for an increasing sample assumed to arise from an underlying probability distribution, and based on this result provide recommendations for improving spectral clustering methodology. A corresponding algorithm is proposed with strong empirical performance.
Image Restoration using Autoencoding Priors
Authors: Siavash Arjomand Bigdeli, Matthias Zwicker
Date: 2017-03-29 00:00:00
Tasks: Denoising, Image Restoration, Super-Resolution

We propose to leverage denoising autoencoder networks as priors to address
image restoration problems. We build on the key observation that the output of
an optimal denoising autoencoder is a local mean of the true data density, and
the autoencoder error (the difference between the output and input of the
trained autoencoder) is a mean shift vector. We use the magnitude of this mean
shift vector, that is, the distance to the local mean, as the negative log
likelihood of our natural image prior. For image restoration, we maximize the
likelihood using gradient descent by backpropagating the autoencoder error. A
key advantage of our approach is that we do not need to train separate networks
for different image restoration tasks, such as non-blind deconvolution with
different kernels, or super-resolution at different magnification factors. We
demonstrate state of the art results for non-blind deconvolution and
super-resolution using the same autoencoding prior.