pwc-archive/papers-with-abstracts huggingface dataset:
Correlation Preserving Sparse Coding Over Multi-level Dictionaries for Image Denoising
Authors: Rui Chen, Huizhu Jia, Xiaodong Xie, Wen Gao
Date: 2016-12-23 00:00:00
Tasks: Denoising, Image Denoising

In this letter, we propose a novel image denoising method based on
correlation preserving sparse coding. Because the instable and unreliable
correlations among basis set can limit the performance of the dictionary-driven
denoising methods, two effective regularized strategies are employed in the
coding process. Specifically, a graph-based regularizer is built for preserving
the global similarity correlations, which can adaptively capture both the
geometrical structures and discriminative features of textured patches. In
particular, edge weights in the graph are obtained by seeking a nonnegative
low-rank construction. Besides, a robust locality-constrained coding can
automatically preserve not only spatial neighborhood information but also
internal consistency present in noisy patches while learning overcomplete
dictionary. Experimental results demonstrate that our proposed method achieves
state-of-the-art denoising performance in terms of both PSNR and subjective
visual quality.
Blind restoration for non-uniform aerial images using non-local Retinex model and shearlet-based higher-order regularization
Authors: Rui Chen, Huizhu Jia, Xiaodong Xie, Wen Gao
Date: 2016-12-23 00:00:00
Tasks: Deblurring

Aerial images are often degraded by space-varying motion blur and
simultaneous uneven illumination. To recover high-quality aerial image from its
non-uniform version, we propose a novel patch-wise restoration approach based
on a key observation that the degree of blurring is inevitably affected by the
illuminated conditions. A non-local Retinex model is developed to accurately
estimate the reflectance component from the degraded aerial image. Thereafter
the uneven illumination is corrected well. And then non-uniform coupled
blurring in the enhanced reflectance image is alleviated and transformed
towards uniform distribution, which will facilitate the subsequent deblurring.
For constructing the multi-scale sparsified regularizer, the discrete shearlet
transform is improved to better represent anisotropic image features in term of
directional sensitivity and selectivity. In addition, a new adaptive variant of
total generalized variation is proposed for the structure-preserving
regularizer. These complementary regularizers are elegantly integrated into an
objective function. The final deblurred image with uniform illumination can be
extracted by applying the fast alternating direction scheme to solve the
derived function. The experimental results demonstrate that our algorithm can
not only remove both the space-varying illumination and motion blur in the
aerial image effectively but also recover the abundant details of aerial scenes
with top-level objective and subjective quality, and outperforms other
state-of-the-art restoration methods.
RSSL: Semi-supervised Learning in R
Authors: Jesse H. Krijthe
Date: 2016-12-23 00:00:00

In this paper, we introduce a package for semi-supervised learning research
in the R programming language called RSSL. We cover the purpose of the package,
the methods it includes and comment on their use and implementation. We then
show, using several code examples, how the package can be used to replicate
well-known results from the semi-supervised learning literature.
Automated Inference on Sociopsychological Impressions of Attractive Female Faces
Authors: Xiaolin Wu, Xi Zhang, Chang Liu
Date: 2016-12-11 00:00:00
Tasks: BIG-bench Machine Learning

This article is a sequel to our earlier work [25]. The main objective of our
research is to explore the potential of supervised machine learning in
face-induced social computing and cognition, riding on the momentum of much
heralded successes of face processing, analysis and recognition on the tasks of
biometric-based identification. We present a case study of automated
statistical inference on sociopsychological perceptions of female faces
controlled for race, attractiveness, age and nationality. Our empirical
evidences point to the possibility of training machine learning algorithms,
using example face images characterized by internet users, to predict
perceptions of personality traits and demeanors.
Two-stream convolutional neural network for accurate RGB-D fingertip detection using depth and edge information
Authors: Hengkai Guo, Guijin Wang, Xinghao Chen
Date: 2016-12-23 00:00:00
Tasks: Fingertip Detection

Accurate detection of fingertips in depth image is critical for
human-computer interaction. In this paper, we present a novel two-stream
convolutional neural network (CNN) for RGB-D fingertip detection. Firstly edge
image is extracted from raw depth image using random forest. Then the edge
information is combined with depth information in our CNN structure. We study
several fusion approaches and suggest a slow fusion strategy as a promising way
of fingertip detection. As shown in our experiments, our real-time algorithm
outperforms state-of-the-art fingertip detection methods on the public dataset
HandNet with an average 3D error of 9.9mm, and shows comparable accuracy of
fingertip estimation on NYU hand dataset.
A CRF Based POS Tagger for Code-mixed Indian Social Media Text
Authors: Kamal Sarkar
Date: 2016-12-23 00:00:00
Tasks: Part-Of-Speech Tagging, POS, POS Tagging

In this work, we describe a conditional random fields (CRF) based system for
Part-Of- Speech (POS) tagging of code-mixed Indian social media text as part of
our participation in the tool contest on POS tagging for codemixed Indian
social media text, held in conjunction with the 2016 International Conference
on Natural Language Processing, IIT(BHU), India. We participated only in
constrained mode contest for all three language pairs, Bengali-English,
Hindi-English and Telegu-English. Our system achieves the overall average F1
score of 79.99, which is the highest overall average F1 score among all 16
systems participated in constrained mode contest.
Bayesian Differential Privacy through Posterior Sampling
Authors: Christos Dimitrakakis, Blaine Nelson, and Zuhe Zhang, Aikaterini Mitrokotsa, Benjamin Rubinstein
Date: 2013-06-05 00:00:00
Tasks: Bayesian Inference, Privacy Preserving

Differential privacy formalises privacy-preserving mechanisms that provide
access to a database. We pose the question of whether Bayesian inference itself
can be used directly to provide private access to data, with no modification.
The answer is affirmative: under certain conditions on the prior, sampling from
the posterior distribution can be used to achieve a desired level of privacy
and utility. To do so, we generalise differential privacy to arbitrary dataset
metrics, outcome spaces and distribution families. This allows us to also deal
with non-i.i.d or non-tabular datasets. We prove bounds on the sensitivity of
the posterior to the data, which gives a measure of robustness. We also show
how to use posterior sampling to provide differentially private responses to
queries, within a decision-theoretic framework. Finally, we provide bounds on
the utility and on the distinguishability of datasets. The latter are
complemented by a novel use of Le Cam's method to obtain lower bounds. All our
general results hold for arbitrary database metrics, including those for the
common definition of differential privacy. For specific choices of the metric,
we give a number of examples satisfying our assumptions.
Supervised Opinion Aspect Extraction by Exploiting Past Extraction Results
Authors: Lei Shu, Bing Liu, Hu Xu, Annice Kim
Date: 2016-12-23 00:00:00
Tasks: Aspect Extraction, Sentiment Analysis

One of the key tasks of sentiment analysis of product reviews is to extract
product aspects or features that users have expressed opinions on. In this
work, we focus on using supervised sequence labeling as the base approach to
performing the task. Although several extraction methods using sequence
labeling methods such as Conditional Random Fields (CRF) and Hidden Markov
Models (HMM) have been proposed, we show that this supervised approach can be
significantly improved by exploiting the idea of concept sharing across
multiple domains. For example, "screen" is an aspect in iPhone, but not only
iPhone has a screen, many electronic devices have screens too. When "screen"
appears in a review of a new domain (or product), it is likely to be an aspect
too. Knowing this information enables us to do much better extraction in the
new domain. This paper proposes a novel extraction method exploiting this idea
in the context of supervised sequence labeling. Experimental results show that
it produces markedly better results than without using the past information.
Understanding Non-optical Remote-sensed Images: Needs, Challenges and Ways Forward
Authors: Amit Kumar Mishra
Date: 2016-12-23 00:00:00

Non-optical remote-sensed images are going to be used more often in man-
aging disaster, crime and precision agriculture. With more small satellites and
unmanned air vehicles planning to carry radar and hyperspectral image sensors
there is going to be an abundance of such data in the recent future.
Understanding these data in real-time will be crucial in attaining some of the
important sustain- able development goals. Processing non-optical images is, in
many ways, different from that of optical images. Most of the recent advances
in the domain of image understanding has been using optical images. In this
article we shall explain the needs for image understanding in non-optical
domain and the typical challenges. Then we shall describe the existing
approaches and how we can move from there to the desired goal of a reliable
real-time image understanding system.
Bipolar Weighted Argumentation Graphs
Authors: Till Mossakowski, Fabian Neuhaus
Date: 2016-11-25 00:00:00

This paper discusses the semantics of weighted argumentation graphs that are
biplor, i.e. contain both attacks and support graphs. The work builds on
previous work by Amgoud, Ben-Naim et. al., which presents and compares several
semantics for argumentation graphs that contain only supports or only attacks
relationships, respectively.
Optimality and Sub-optimality of PCA for Spiked Random Matrices and Synchronization
Authors: Amelia Perry, Alexander S. Wein, Afonso S. Bandeira, Ankur Moitra
Date: 2016-09-19 00:00:00

A central problem of random matrix theory is to understand the eigenvalues of
spiked random matrix models, in which a prominent eigenvector is planted into a
random matrix. These distributions form natural statistical models for
principal component analysis (PCA) problems throughout the sciences. Baik, Ben
Arous and P\'ech\'e showed that the spiked Wishart ensemble exhibits a sharp
phase transition asymptotically: when the signal strength is above a critical
threshold, it is possible to detect the presence of a spike based on the top
eigenvalue, and below the threshold the top eigenvalue provides no information.
Such results form the basis of our understanding of when PCA can detect a
low-rank signal in the presence of noise.
  However, not all the information about the spike is necessarily contained in
the spectrum. We study the fundamental limitations of statistical methods,
including non-spectral ones. Our results include:
  I) For the Gaussian Wigner ensemble, we show that PCA achieves the optimal
detection threshold for a variety of benign priors for the spike. We extend
previous work on the spherically symmetric and i.i.d. Rademacher priors through
an elementary, unified analysis.
  II) For any non-Gaussian Wigner ensemble, we show that PCA is always
suboptimal for detection. However, a variant of PCA achieves the optimal
threshold (for benign priors) by pre-transforming the matrix entries according
to a carefully designed function. This approach has been stated before, and we
give a rigorous and general analysis.
  III) For both the Gaussian Wishart ensemble and various synchronization
problems over groups, we show that inefficient procedures can work below the
threshold where PCA succeeds, whereas no known efficient algorithm achieves
this. This conjectural gap between what is statistically possible and what can
be done efficiently remains open.
A Base Camp for Scaling AI
Authors: C. J. C. Burges, T. Hart, Z. Yang, S. Cucerzan, R. W. White, A. Pastusiak, J. Lewis
Date: 2016-12-23 00:00:00
Tasks: Dialog Learning, Intent Detection

Modern statistical machine learning (SML) methods share a major limitation
with the early approaches to AI: there is no scalable way to adapt them to new
domains. Human learning solves this in part by leveraging a rich, shared,
updateable world model. Such scalability requires modularity: updating part of
the world model should not impact unrelated parts. We have argued that such
modularity will require both "correctability" (so that errors can be corrected
without introducing new errors) and "interpretability" (so that we can
understand what components need correcting).
  To achieve this, one could attempt to adapt state of the art SML systems to
be interpretable and correctable; or one could see how far the simplest
possible interpretable, correctable learning methods can take us, and try to
control the limitations of SML methods by applying them only where needed. Here
we focus on the latter approach and we investigate two main ideas: "Teacher
Assisted Learning", which leverages crowd sourcing to learn language; and
"Factored Dialog Learning", which factors the process of application
development into roles where the language competencies needed are isolated,
enabling non-experts to quickly create new applications.
  We test these ideas in an "Automated Personal Assistant" (APA) setting, with
two scenarios: that of detecting user intent from a user-APA dialog; and that
of creating a class of event reminder applications, where a non-expert
"teacher" can then create specific apps. For the intent detection task, we use
a dataset of a thousand labeled utterances from user dialogs with Cortana, and
we show that our approach matches state of the art SML methods, but in addition
provides full transparency: the whole (editable) model can be summarized on one
human-readable page. For the reminder app task, we ran small user studies to
verify the efficacy of the approach.
Spectral algorithms for tensor completion
Authors: Andrea Montanari, Nike Sun
Date: 2016-12-23 00:00:00

In the tensor completion problem, one seeks to estimate a low-rank tensor
based on a random sample of revealed entries. In terms of the required sample
size, earlier work revealed a large gap between estimation with unbounded
computational resources (using, for instance, tensor nuclear norm minimization)
and polynomial-time algorithms. Among the latter, the best statistical
guarantees have been proved, for third-order tensors, using the sixth level of
the sum-of-squares (SOS) semidefinite programming hierarchy (Barak and Moitra,
2014). However, the SOS approach does not scale well to large problem
instances. By contrast, spectral methods --- based on unfolding or matricizing
the tensor --- are attractive for their low complexity, but have been believed
to require a much larger sample size.
  This paper presents two main contributions. First, we propose a new
unfolding-based method, which outperforms naive ones for symmetric $k$-th order
tensors of rank $r$. For this result we make a study of singular space
estimation for partially revealed matrices of large aspect ratio, which may be
of independent interest. For third-order tensors, our algorithm matches the SOS
method in terms of sample size (requiring about $rd^{3/2}$ revealed entries),
subject to a worse rank condition ($r\ll d^{3/4}$ rather than $r\ll d^{3/2}$).
We complement this result with a different spectral algorithm for third-order
tensors in the overcomplete ($r\ge d$) regime. Under a random model, this
second approach succeeds in estimating tensors of rank $d\le r \ll d^{3/2}$
from about $rd^{3/2}$ revealed entries.
Human Action Attribute Learning From Video Data Using Low-Rank Representations
Authors: Tong Wu, Prudhvi Gurram, Raghuveer M. Rao, Waheed U. Bajwa
Date: 2016-12-23 00:00:00
Tasks: Action Recognition, Activity Recognition, Attribute, Clustering, Human Activity Recognition, Temporal Action Localization

Representation of human actions as a sequence of human body movements or action attributes enables the development of models for human activity recognition and summarization. We present an extension of the low-rank representation (LRR) model, termed the clustering-aware structure-constrained low-rank representation (CS-LRR) model, for unsupervised learning of human action attributes from video data. Our model is based on the union-of-subspaces (UoS) framework, and integrates spectral clustering into the LRR optimization problem for better subspace clustering results. We lay out an efficient linear alternating direction method to solve the CS-LRR optimization problem. We also introduce a hierarchical subspace clustering approach, termed hierarchical CS-LRR, to learn the attributes without the need for a priori specification of their number. By visualizing and labeling these action attributes, the hierarchical model can be used to semantically summarize long video sequences of human actions at multiple resolutions. A human action or activity can also be uniquely represented as a sequence of transitions from one action attribute to another, which can then be used for human action recognition. We demonstrate the effectiveness of the proposed model for semantic summarization and action recognition through comprehensive experiments on five real-world human action datasets.
Automatic Labelling of Topics with Neural Embeddings
Authors: Shraey Bhatia, Jey Han Lau, Timothy Baldwin
Date: 2016-12-16 00:00:00
Proceeding: COLING 2016 12
Tasks: Topic Models

Topics generated by topic models are typically represented as list of terms.
To reduce the cognitive overhead of interpreting these topics for end-users, we
propose labelling a topic with a succinct phrase that summarises its theme or
idea. Using Wikipedia document titles as label candidates, we compute neural
embeddings for documents and words to select the most relevant labels for
topics. Compared to a state-of-the-art topic labelling system, our methodology
is simpler, more efficient, and finds better topic labels.
A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements
Authors: Daniel Durstewitz
Date: 2016-12-23 00:00:00
Tasks: State Space Models, Time Series, Time Series Analysis

The computational properties of neural systems are often thought to be
implemented in terms of their network dynamics. Hence, recovering the system
dynamics from experimentally observed neuronal time series, like multiple
single-unit (MSU) recordings or neuroimaging data, is an important step toward
understanding its computations. Ideally, one would not only seek a state space
representation of the dynamics, but would wish to have access to its governing
equations for in-depth analysis. Recurrent neural networks (RNNs) are a
computationally powerful and dynamically universal formal framework which has
been extensively studied from both the computational and the dynamical systems
perspective. Here we develop a semi-analytical maximum-likelihood estimation
scheme for piecewise-linear RNNs (PLRNNs) within the statistical framework of
state space models, which accounts for noise in both the underlying latent
dynamics and the observation process. The Expectation-Maximization algorithm is
used to infer the latent state distribution, through a global Laplace
approximation, and the PLRNN parameters iteratively. After validating the
procedure on toy examples, the approach is applied to MSU recordings from the
rodent anterior cingulate cortex obtained during performance of a classical
working memory task, delayed alternation. A model with 5 states turned out to
be sufficient to capture the essential computational dynamics underlying task
performance, including stimulus-selective delay activity. The estimated models
were rarely multi-stable, but rather were tuned to exhibit slow dynamics in the
vicinity of a bifurcation point. In summary, the present work advances a
semi-analytical (thus reasonably fast) maximum-likelihood estimation framework
for PLRNNs that may enable to recover the relevant dynamics underlying observed
neuronal time series, and directly link them to computational properties.
"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach
Authors: Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek
Date: 2016-12-23 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Interpretable Machine Learning

Text documents can be described by a number of abstract concepts such as
semantic category, writing style, or sentiment. Machine learning (ML) models
have been trained to automatically map documents to these abstract concepts,
allowing to annotate very large text collections, more than could be processed
by a human in a lifetime. Besides predicting the text's category very
accurately, it is also highly desirable to understand how and why the
categorization process takes place. In this paper, we demonstrate that such
understanding can be achieved by tracing the classification decision back to
individual words using layer-wise relevance propagation (LRP), a recently
developed technique for explaining predictions of complex non-linear
classifiers. We train two word-based ML models, a convolutional neural network
(CNN) and a bag-of-words SVM classifier, on a topic categorization task and
adapt the LRP method to decompose the predictions of these models onto words.
Resulting scores indicate how much individual words contribute to the overall
classification decision. This enables one to distill relevant information from
text documents without an explicit semantic information extraction step. We
further use the word-wise relevance scores for generating novel vector-based
document representations which capture semantic information. Based on these
document vectors, we introduce a measure of model explanatory power and show
that, although the SVM and CNN models perform similarly in terms of
classification accuracy, the latter exhibits a higher level of explainability
which makes it more comprehensible for humans and potentially more useful for
other applications.
Known Unknowns: Uncertainty Quality in Bayesian Neural Networks
Authors: Ramon Oliveira, Pedro Tabacof, Eduardo Valle
Date: 2016-12-05 00:00:00
Tasks: Anomaly Detection, Known Unknowns

We evaluate the uncertainty quality in neural networks using anomaly
detection. We extract uncertainty measures (e.g. entropy) from the predictions
of candidate models, use those measures as features for an anomaly detector,
and gauge how well the detector differentiates known from unknown classes. We
assign higher uncertainty quality to candidate models that lead to better
detectors. We also propose a novel method for sampling a variational
approximation of a Bayesian neural network, called One-Sample Bayesian
Approximation (OSBA). We experiment on two datasets, MNIST and CIFAR10. We
compare the following candidate neural network models: Maximum Likelihood,
Bayesian Dropout, OSBA, and --- for MNIST --- the standard variational
approximation. We show that Bayesian Dropout and OSBA provide better
uncertainty information than Maximum Likelihood, and are essentially equivalent
to the standard variational approximation, but much faster.