pwc-archive/papers-with-abstracts huggingface dataset:
Statistical Inference, Learning and Models in Big Data
Authors: Beate Franke, Jean-François Plante, Ribana Roscher, Annie Lee, Cathal Smyth, Armin Hatefi, Fuqi Chen, Einat Gil, Alexander Schwing, Alessandro Selvitella, Michael M. Hoffman, Roger Grosse, Dieter Hendricks, Nancy Reid
Date: 2015-09-09 00:00:00

The need for new methods to deal with big data is a common theme in most
scientific fields, although its definition tends to vary with the context.
Statistical ideas are an essential part of this, and as a partial response, a
thematic program on statistical inference, learning, and models in big data was
held in 2015 in Canada, under the general direction of the Canadian Statistical
Sciences Institute, with major funding from, and most activities located at,
the Fields Institute for Research in Mathematical Sciences. This paper gives an
overview of the topics covered, describing challenges and strategies that seem
common to many different areas of application, and including some examples of
applications to make these challenges and strategies more concrete.
Geo-distinctive Visual Element Matching for Location Estimation of Images
Authors: Xinchao Li, Martha A. Larson, Alan Hanjalic
Date: 2016-01-28 00:00:00
Tasks: Common Sense Reasoning

We propose an image representation and matching approach that substantially
improves visual-based location estimation for images. The main novelty of the
approach, called distinctive visual element matching (DVEM), is its use of
representations that are specific to the query image whose location is being
predicted. These representations are based on visual element clouds, which
robustly capture the connection between the query and visual evidence from
candidate locations. We then maximize the influence of visual elements that are
geo-distinctive because they do not occur in images taken at many other
locations. We carry out experiments and analysis for both geo-constrained and
geo-unconstrained location estimation cases using two large-scale,
publicly-available datasets: the San Francisco Landmark dataset with $1.06$
million street-view images and the MediaEval '15 Placing Task dataset with
$5.6$ million geo-tagged images from Flickr. We present examples that
illustrate the highly-transparent mechanics of the approach, which are based on
common sense observations about the visual patterns in image collections. Our
results show that the proposed method delivers a considerable performance
improvement compared to the state of the art.
Towards the Design of an End-to-End Automated System for Image and Video-based Recognition
Authors: Rama Chellappa, Jun-Cheng Chen, Rajeev Ranjan, Swami Sankaranarayanan, Amit Kumar, Vishal M. Patel, Carlos D. Castillo
Date: 2016-01-28 00:00:00
Tasks: Face Verification, Object, object-detection, Object Detection, Object Recognition

Over many decades, researchers working in object recognition have longed for
an end-to-end automated system that will simply accept 2D or 3D image or videos
as inputs and output the labels of objects in the input data. Computer vision
methods that use representations derived based on geometric, radiometric and
neural considerations and statistical and structural matchers and artificial
neural network-based methods where a multi-layer network learns the mapping
from inputs to class labels have provided competing approaches for image
recognition problems. Over the last four years, methods based on Deep
Convolutional Neural Networks (DCNNs) have shown impressive performance
improvements on object detection/recognition challenge problems. This has been
made possible due to the availability of large annotated data, a better
understanding of the non-linear mapping between image and class labels as well
as the affordability of GPUs. In this paper, we present a brief history of
developments in computer vision and artificial neural networks over the last
forty years for the problem of image-based recognition. We then present the
design details of a deep learning system for end-to-end unconstrained face
verification/recognition. Some open issues regarding DCNNs for object
recognition problems are then discussed. We caution the readers that the views
expressed in this paper are from the authors and authors only!
Unsupervised Learning on Neural Network Outputs: with Application in Zero-shot Learning
Authors: Yao Lu
Date: 2015-06-02 00:00:00
Tasks: Zero-Shot Learning

The outputs of a trained neural network contain much richer information than
just an one-hot classifier. For example, a neural network might give an image
of a dog the probability of one in a million of being a cat but it is still
much larger than the probability of being a car. To reveal the hidden structure
in them, we apply two unsupervised learning algorithms, PCA and ICA, to the
outputs of a deep Convolutional Neural Network trained on the ImageNet of 1000
classes. The PCA/ICA embedding of the object classes reveals their visual
similarity and the PCA/ICA components can be interpreted as common visual
features shared by similar object classes. For an application, we proposed a
new zero-shot learning method, in which the visual features learned by PCA/ICA
are employed. Our zero-shot learning method achieves the state-of-the-art
results on the ImageNet of over 20000 classes.
Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition
Authors: Priyadarshini Panda, Abhronil Sengupta, Kaushik Roy
Date: 2015-09-29 00:00:00
Tasks: Classification, Deep Learning, General Classification

Deep learning neural networks have emerged as one of the most powerful
classification tools for vision related applications. However, the
computational and energy requirements associated with such deep nets can be
quite high, and hence their energy-efficient implementation is of great
interest. Although traditionally the entire network is utilized for the
recognition of all inputs, we observe that the classification difficulty varies
widely across inputs in real-world datasets; only a small fraction of inputs
require the full computational effort of a network, while a large majority can
be classified correctly with very low effort. In this paper, we propose
Conditional Deep Learning (CDL) where the convolutional layer features are used
to identify the variability in the difficulty of input instances and
conditionally activate the deeper layers of the network. We achieve this by
cascading a linear network of output neurons for each convolutional layer and
monitoring the output of the linear network to decide whether classification
can be terminated at the current stage or not. The proposed methodology thus
enables the network to dynamically adjust the computational effort depending
upon the difficulty of the input data while maintaining competitive
classification accuracy. We evaluate our approach on the MNIST dataset. Our
experiments demonstrate that our proposed CDL yields 1.91x reduction in average
number of operations per input, which translates to 1.84x improvement in
energy. In addition, our results show an improvement in classification accuracy
from 97.5% to 98.9% as compared to the original network.
An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning
Authors: Nabin K. Mishra, M. Emre Celebi
Date: 2016-01-28 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic, Lesion Segmentation

The incidence of malignant melanoma continues to increase worldwide. This
cancer can strike at any age; it is one of the leading causes of loss of life
in young persons. Since this cancer is visible on the skin, it is potentially
detectable at a very early stage when it is curable. New developments have
converged to make fully automatic early melanoma detection a real possibility.
First, the advent of dermoscopy has enabled a dramatic boost in clinical
diagnostic ability to the point that melanoma can be detected in the clinic at
the very earliest stages. The global adoption of this technology has allowed
accumulation of large collections of dermoscopy images of melanomas and benign
lesions validated by histopathology. The development of advanced technologies
in the areas of image processing and machine learning have given us the ability
to allow distinction of malignant melanoma from the many benign mimics that
require no biopsy. These new technologies should allow not only earlier
detection of melanoma, but also reduction of the large number of needless and
costly biopsy procedures. Although some of the new systems reported for these
technologies have shown promise in preliminary trials, widespread
implementation must await further technical progress in accuracy and
reproducibility. In this paper, we provide an overview of computerized
detection of melanoma in dermoscopy images. First, we discuss the various
aspects of lesion segmentation. Then, we provide a brief overview of clinical
feature segmentation. Finally, we discuss the classification stage where
machine learning algorithms are applied to the attributes generated from the
segmented features to predict the existence of melanoma.
The Singularity Controversy, Part I: Lessons Learned and Open Questions: Conclusions from the Battle on the Legitimacy of the Debate
Authors: Amnon H. Eden
Date: 2016-01-22 00:00:00
Tasks: Misconceptions

This report seeks to inform policy makers on the nature and the merit of the
arguments for and against the concerns associated with a potential
technological singularity.
  Part I describes the lessons learned from our investigation of the subject,
separating the argu-ments of merit from the fallacies and misconceptions that
confuse the debate and undermine its rational resolution.
Joint Sensing Matrix and Sparsifying Dictionary Optimization for Tensor Compressive Sensing
Authors: Xin Ding, Wei Chen, Ian J. Wassell
Date: 2016-01-28 00:00:00
Tasks: Compressive Sensing, Dictionary Learning

Tensor Compressive Sensing (TCS) is a multidimensional framework of
Compressive Sensing (CS), and it is advantageous in terms of reducing the
amount of storage, easing hardware implementations and preserving
multidimensional structures of signals in comparison to a conventional CS
system. In a TCS system, instead of using a random sensing matrix and a
predefined dictionary, the average-case performance can be further improved by
employing an optimized multidimensional sensing matrix and a learned
multilinear sparsifying dictionary. In this paper, we propose a joint
optimization approach of the sensing matrix and dictionary for a TCS system.
For the sensing matrix design in TCS, an extended separable approach with a
closed form solution and a novel iterative non-separable method are proposed
when the multilinear dictionary is fixed. In addition, a multidimensional
dictionary learning method that takes advantages of the multidimensional
structure is derived, and the influence of sensing matrices is taken into
account in the learning process. A joint optimization is achieved via
alternately iterating the optimization of the sensing matrix and dictionary.
Numerical experiments using both synthetic data and real images demonstrate the
superiority of the proposed approaches.
Is Image Super-resolution Helpful for Other Vision Tasks?
Authors: Dengxin Dai, Yujian Wang, Yuhua Chen, Luc van Gool
Date: 2015-09-23 00:00:00
Tasks: Edge Detection, Image Segmentation, Image Super-Resolution, Scene Recognition, Semantic Segmentation, SSIM, Super-Resolution

Despite the great advances made in the field of image super-resolution (ISR)
during the last years, the performance has merely been evaluated perceptually.
Thus, it is still unclear whether ISR is helpful for other vision tasks. In
this paper, we present the first comprehensive study and analysis of the
usefulness of ISR for other vision applications. In particular, six ISR methods
are evaluated on four popular vision tasks, namely edge detection, semantic
image segmentation, digit recognition, and scene recognition. We show that
applying ISR to input images of other vision systems does improve their
performance when the input images are of low-resolution. We also study the
correlation between four standard perceptual evaluation criteria (namely PSNR,
SSIM, IFC, and NQM) and the usefulness of ISR to the vision tasks. Experiments
show that they correlate well with each other in general, but perceptual
criteria are still not accurate enough to be used as full proxies for the
usefulness. We hope this work will inspire the community to evaluate ISR
methods also in real vision applications, and to adopt ISR as a pre-processing
step of other vision tasks if the resolution of their input images is low.
Character-Level Incremental Speech Recognition with Recurrent Neural Networks
Authors: Kyuyeon Hwang, Wonyong Sung
Date: 2016-01-25 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

In real-time speech recognition applications, the latency is an important
issue. We have developed a character-level incremental speech recognition (ISR)
system that responds quickly even during the speech, where the hypotheses are
gradually improved while the speaking proceeds. The algorithm employs a
speech-to-character unidirectional recurrent neural network (RNN), which is
end-to-end trained with connectionist temporal classification (CTC), and an
RNN-based character-level language model (LM). The output values of the
CTC-trained RNN are character-level probabilities, which are processed by beam
search decoding. The RNN LM augments the decoding by providing long-term
dependency information. We propose tree-based online beam search with
additional depth-pruning, which enables the system to process infinitely long
input speech with low latency. This system not only responds quickly on speech
but also can dictate out-of-vocabulary (OOV) words according to pronunciation.
The proposed model achieves the word error rate (WER) of 8.90% on the Wall
Street Journal (WSJ) Nov'92 20K evaluation set when trained on the WSJ SI-284
training set.
Distributed Low Rank Approximation of Implicit Functions of a Matrix
Authors: David P. Woodruff, Peilin Zhong
Date: 2016-01-28 00:00:00

We study distributed low rank approximation in which the matrix to be
approximated is only implicitly represented across the different servers. For
example, each of $s$ servers may have an $n \times d$ matrix $A^t$, and we may
be interested in computing a low rank approximation to $A = f(\sum_{t=1}^s
A^t)$, where $f$ is a function which is applied entrywise to the matrix
$\sum_{t=1}^s A^t$. We show for a wide class of functions $f$ it is possible to
efficiently compute a $d \times d$ rank-$k$ projection matrix $P$ for which
$\|A - AP\|_F^2 \leq \|A - [A]_k\|_F^2 + \varepsilon \|A\|_F^2$, where $AP$
denotes the projection of $A$ onto the row span of $P$, and $[A]_k$ denotes the
best rank-$k$ approximation to $A$ given by the singular value decomposition.
The communication cost of our protocols is $d \cdot (sk/\varepsilon)^{O(1)}$,
and they succeed with high probability. Our framework allows us to efficiently
compute a low rank approximation to an entry-wise softmax, to a Gaussian kernel
expansion, and to $M$-Estimators applied entrywise (i.e., forms of robust low
rank approximation). We also show that our additive error approximation is best
possible, in the sense that any protocol achieving relative error for these
problems requires significantly more communication. Finally, we experimentally
validate our algorithms on real datasets.
Log-Normal Matrix Completion for Large Scale Link Prediction
Authors: Brian Mohtashemi, Thomas Ketseoglou
Date: 2016-01-28 00:00:00
Tasks: Descriptive, Link Prediction, Matrix Completion, Prediction

The ubiquitous proliferation of online social networks has led to the
widescale emergence of relational graphs expressing unique patterns in link
formation and descriptive user node features. Matrix Factorization and
Completion have become popular methods for Link Prediction due to the low rank
nature of mutual node friendship information, and the availability of parallel
computer architectures for rapid matrix processing. Current Link Prediction
literature has demonstrated vast performance improvement through the
utilization of sparsity in addition to the low rank matrix assumption. However,
the majority of research has introduced sparsity through the limited L1 or
Frobenius norms, instead of considering the more detailed distributions which
led to the graph formation and relationship evolution. In particular, social
networks have been found to express either Pareto, or more recently discovered,
Log Normal distributions. Employing the convexity-inducing Lovasz Extension, we
demonstrate how incorporating specific degree distribution information can lead
to large scale improvements in Matrix Completion based Link prediction. We
introduce Log-Normal Matrix Completion (LNMC), and solve the complex
optimization problem by employing Alternating Direction Method of Multipliers.
Using data from three popular social networks, our experiments yield up to 5%
AUC increase over top-performing non-structured sparsity based methods.
Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation
Authors: Alaa Saade, Florent Krzakala, Lenka Zdeborová
Date: 2015-06-10 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Matrix Completion

The completion of low rank matrices from few entries is a task with many
practical applications. We consider here two aspects of this problem:
detectability, i.e. the ability to estimate the rank $r$ reliably from the
fewest possible random entries, and performance in achieving small
reconstruction error. We propose a spectral algorithm for these two tasks
called MaCBetH (for Matrix Completion with the Bethe Hessian). The rank is
estimated as the number of negative eigenvalues of the Bethe Hessian matrix,
and the corresponding eigenvectors are used as initial condition for the
minimization of the discrepancy between the estimated matrix and the revealed
entries. We analyze the performance in a random matrix setting using results
from the statistical mechanics of the Hopfield neural network, and show in
particular that MaCBetH efficiently detects the rank $r$ of a large $n\times m$
matrix from $C(r)r\sqrt{nm}$ entries, where $C(r)$ is a constant close to $1$.
We also evaluate the corresponding root-mean-square error empirically and show
that MaCBetH compares favorably to other existing approaches.
Combining persistent homology and invariance groups for shape comparison
Authors: Patrizio Frosini, Grzegorz Jablonski
Date: 2013-12-27 00:00:00

In many applications concerning the comparison of data expressed by
$\mathbb{R}^m$-valued functions defined on a topological space $X$, the
invariance with respect to a given group $G$ of self-homeomorphisms of $X$ is
required. While persistent homology is quite efficient in the topological and
qualitative comparison of this kind of data when the invariance group $G$ is
the group $\mathrm{Homeo}(X)$ of all self-homeomorphisms of $X$, this theory is
not tailored to manage the case in which $G$ is a proper subgroup of
$\mathrm{Homeo}(X)$, and its invariance appears too general for several tasks.
This paper proposes a way to adapt persistent homology in order to get
invariance just with respect to a given group of self-homeomorphisms of $X$.
The main idea consists in a dual approach, based on considering the set of all
$G$-invariant non-expanding operators defined on the space of the admissible
filtering functions on $X$. Some theoretical results concerning this approach
are proven and two experiments are presented. An experiment illustrates the
application of the proposed technique to compare 1D-signals, when the
invariance is expressed by the group of affinities, the group of
orientation-preserving affinities, the group of isometries, the group of
translations and the identity group. Another experiment shows how our technique
can be used for image comparison.
Obtaining A Linear Combination of the Principal Components of a Matrix on Quantum Computers
Authors: Anmer Daskin
Date: 2015-11-26 00:00:00

Principal component analysis is a multivariate statistical method frequently
used in science and engineering to reduce the dimension of a problem or extract
the most significant features from a dataset. In this paper, using a similar
notion to the quantum counting, we show how to apply the amplitude
amplification together with the phase estimation algorithm to an operator in
order to procure the eigenvectors of the operator associated to the eigenvalues
defined in the range $\left[a, b\right]$, where $a$ and $b$ are real and $0
\leq a \leq b \leq 1$. This makes possible to obtain a combination of the
eigenvectors associated to the largest eigenvalues and so can be used to do
principal component analysis on quantum computers.
Robustness Analysis of Preconditioned Successive Projection Algorithm for General Form of Separable NMF Problem
Authors: Tomohiko Mizutani
Date: 2015-06-28 00:00:00
Tasks: Form, Single Particle Analysis

The successive projection algorithm (SPA) has been known to work well for
separable nonnegative matrix factorization (NMF) problems arising in
applications, such as topic extraction from documents and endmember detection
in hyperspectral images. One of the reasons is in that the algorithm is robust
to noise. Gillis and Vavasis showed in [SIAM J. Optim., 25(1), pp. 677-698,
2015] that a preconditioner can further enhance its noise robustness. The proof
rested on the condition that the dimension $d$ and factorization rank $r$ in
the separable NMF problem coincide with each other. However, it may be
unrealistic to expect that the condition holds in separable NMF problems
appearing in actual applications; in such problems, $d$ is usually greater than
$r$. This paper shows, without the condition $d=r$, that the preconditioned SPA
is robust to noise.
TEI and LMF crosswalks
Authors: Laurent Romary
Date: 2013-01-11 00:00:00

The present paper explores various arguments in favour of making the Text
Encoding Initia-tive (TEI) guidelines an appropriate serialisation for ISO
standard 24613:2008 (LMF, Lexi-cal Mark-up Framework) . It also identifies the
issues that would have to be resolved in order to reach an appropriate
implementation of these ideas, in particular in terms of infor-mational
coverage. We show how the customisation facilities offered by the TEI
guidelines can provide an adequate background, not only to cover missing
components within the current Dictionary chapter of the TEI guidelines, but
also to allow specific lexical projects to deal with local constraints. We
expect this proposal to be a basis for a future ISO project in the context of
the on going revision of LMF.
Non-Gaussian Component Analysis with Log-Density Gradient Estimation
Authors: Hiroaki Sasaki, Gang Niu, Masashi Sugiyama
Date: 2016-01-28 00:00:00

Non-Gaussian component analysis (NGCA) is aimed at identifying a linear
subspace such that the projected data follows a non-Gaussian distribution. In
this paper, we propose a novel NGCA algorithm based on log-density gradient
estimation. Unlike existing methods, the proposed NGCA algorithm identifies the
linear subspace by using the eigenvalue decomposition without any iterative
procedures, and thus is computationally reasonable. Furthermore, through
theoretical analysis, we prove that the identified subspace converges to the
true subspace at the optimal parametric rate. Finally, the practical
performance of the proposed algorithm is demonstrated on both artificial and
benchmark datasets.