pwc-archive/papers-with-abstracts huggingface dataset:
Discriminative Block-Diagonal Representation Learning for Image Recognition
Authors: Zheng Zhang, Yong Xu, Ling Shao, Jian Yang
Date: 2017-07-12 00:00:00
Tasks: Representation Learning

Existing block-diagonal representation researches mainly focuses on casting
block-diagonal regularization on training data, while only little attention is
dedicated to concurrently learning both block-diagonal representations of
training and test data. In this paper, we propose a discriminative
block-diagonal low-rank representation (BDLRR) method for recognition. In
particular, the elaborate BDLRR is formulated as a joint optimization problem
of shrinking the unfavorable representation from off-block-diagonal elements
and strengthening the compact block-diagonal representation under the
semi-supervised framework of low-rank representation. To this end, we first
impose penalty constraints on the negative representation to eliminate the
correlation between different classes such that the incoherence criterion of
the extra-class representation is boosted. Moreover, a constructed subspace
model is developed to enhance the self-expressive power of training samples and
further build the representation bridge between the training and test samples,
such that the coherence of the learned intra-class representation is
consistently heightened. Finally, the resulting optimization problem is solved
elegantly by employing an alternative optimization strategy, and a simple
recognition algorithm on the learned representation is utilized for final
prediction. Extensive experimental results demonstrate that the proposed method
achieves superb recognition results on four face image datasets, three
character datasets, and the fifteen scene multi-categories dataset. It not only
shows superior potential on image recognition but also outperforms
state-of-the-art methods.
Encoding Video and Label Priors for Multi-label Video Classification on YouTube-8M dataset
Authors: Seil Na, Youngjae Yu, Sang-ho Lee, Ji-Sung Kim, Gunhee Kim
Date: 2017-06-24 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Video Classification

YouTube-8M is the largest video dataset for multi-label video classification.
In order to tackle the multi-label classification on this challenging dataset,
it is necessary to solve several issues such as temporal modeling of videos,
label imbalances, and correlations between labels. We develop a deep neural
network model, which consists of four components: the frame encoder, the
classification layer, the label processing layer, and the loss function. We
introduce our newly proposed methods and discusses how existing models operate
in the YouTube-8M Classification Task, what insights they have, and why they
succeed (or fail) to achieve good performance. Most of the models we proposed
are very high compared to the baseline models, and the ensemble of the models
we used is 8th in the Kaggle Competition.
An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling
Authors: Hien D. Nguyen, Faicel Chamroukhi
Date: 2017-07-12 00:00:00
Tasks: Clustering, Mixture-of-Experts

Mixture-of-experts (MoE) models are a powerful paradigm for modeling of data
arising from complex data generating processes (DGPs). In this article, we
demonstrate how different MoE models can be constructed to approximate the
underlying DGPs of arbitrary types of data. Due to the probabilistic nature of
MoE models, we propose the maximum quasi-likelihood (MQL) estimator as a method
for estimating MoE model parameters from data, and we provide conditions under
which MQL estimators are consistent and asymptotically normal. The blockwise
minorization-maximizatoin (blockwise-MM) algorithm framework is proposed as an
all-purpose method for constructing algorithms for obtaining MQL estimators. An
example derivation of a blockwise-MM algorithm is provided. We then present a
method for constructing information criteria for estimating the number of
components in MoE models and provide justification for the classic Bayesian
information criterion (BIC). We explain how MoE models can be used to conduct
classification, clustering, and regression and we illustrate these applications
via a pair of worked examples.
Online Handwritten Mathematical Expressions Recognition System Using Fuzzy Neural Network
Authors: E. Naderan
Date: 2017-07-11 00:00:00

The article describes developed information technology for online recognition
of handwritten mathematical expressions that based on proposed approaches to
handwritten symbols recognition and structural analysis.
A breakthrough in Speech emotion recognition using Deep Retinal Convolution Neural Networks
Authors: Yafeng Niu, Dongsheng Zou, Yadong Niu, Zhongshi He, Hua Tan
Date: 2017-07-12 00:00:00
Tasks: Data Augmentation, Emotion Recognition, Speech Emotion Recognition

Speech emotion recognition (SER) is to study the formation and change of
speaker's emotional state from the speech signal perspective, so as to make the
interaction between human and computer more intelligent. SER is a challenging
task that has encountered the problem of less training data and low prediction
accuracy. Here we propose a data augmentation algorithm based on the imaging
principle of the retina and convex lens, to acquire the different sizes of
spectrogram and increase the amount of training data by changing the distance
between the spectrogram and the convex lens. Meanwhile, with the help of deep
learning to get the high-level features, we propose the Deep Retinal
Convolution Neural Networks (DRCNNs) for SER and achieve the average accuracy
over 99%. The experimental results indicate that DRCNNs outperforms the
previous studies in terms of both the number of emotions and the accuracy of
recognition. Predictably, our results will dramatically improve human-computer
interaction.
NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles
Authors: Jiajun Lu, Hussein Sibai, Evan Fabry, David Forsyth
Date: 2017-07-12 00:00:00
Tasks: Autonomous Vehicles, object-detection, Object Detection

It has been shown that most machine learning algorithms are susceptible to
adversarial perturbations. Slightly perturbing an image in a carefully chosen
direction in the image space may cause a trained neural network model to
misclassify it. Recently, it was shown that physical adversarial examples
exist: printing perturbed images then taking pictures of them would still
result in misclassification. This raises security and safety concerns.
  However, these experiments ignore a crucial property of physical objects: the
camera can view objects from different distances and at different angles. In
this paper, we show experiments that suggest that current constructions of
physical adversarial examples do not disrupt object detection from a moving
platform. Instead, a trained neural network classifies most of the pictures
taken from different distances and angles of a perturbed image correctly. We
believe this is because the adversarial property of the perturbation is
sensitive to the scale at which the perturbed picture is viewed, so (for
example) an autonomous car will misclassify a stop sign only from a small range
of distances.
  Our work raises an important question: can one construct examples that are
adversarial for many or most viewing conditions? If so, the construction should
offer very significant insights into the internal representation of patterns by
deep networks. If not, there is a good prospect that adversarial examples can
be reduced to a curiosity with little practical impact.
Hot-Rodding the Browser Engine: Automatic Configuration of JavaScript Compilers
Authors: Chris Fawcett, Lars Kotthoff, Holger H. Hoos
Date: 2017-07-11 00:00:00

Modern software systems in many application areas offer to the user a
multitude of parameters, switches and other customisation hooks. Humans tend to
have difficulties determining the best configurations for particular
applications. Modern optimising compilers are an example of such software
systems; their many parameters need to be tuned for optimal performance, but
are often left at the default values for convenience. In this work, we
automatically determine compiler parameter settings that result in optimised
performance for particular applications. Specifically, we apply a
state-of-the-art automated parameter configuration procedure based on
cutting-edge machine learning and optimisation techniques to two prominent
JavaScript compilers and demonstrate that significant performance improvements,
more than 35% in some cases, can be achieved over the default parameter
settings on a diverse set of benchmarks.
Unsupervised robust nonparametric learning of hidden community properties
Authors: Mikhail A. Langovoy, Akhilesh Gotmare, Martin Jaggi
Date: 2017-07-11 00:00:00

We consider learning of fundamental properties of communities in large noisy
networks, in the prototypical situation where the nodes or users are split into
two classes according to a binary property, e.g., according to their opinions
or preferences on a topic. For learning these properties, we propose a
nonparametric, unsupervised, and scalable graph scan procedure that is, in
addition, robust against a class of powerful adversaries. In our setup, one of
the communities can fall under the influence of a knowledgeable adversarial
leader, who knows the full network structure, has unlimited computational
resources and can completely foresee our planned actions on the network. We
prove strong consistency of our results in this setup with minimal assumptions.
In particular, the learning procedure estimates the baseline activity of normal
users asymptotically correctly with probability 1; the only assumption being
the existence of a single implicit community of asymptotically negligible
logarithmic size. We provide experiments on real and synthetic data to
illustrate the performance of our method, including examples with adversaries.
Creatism: A deep-learning photographer capable of creating professional work
Authors: Hui Fang, Meng Zhang
Date: 2017-07-11 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

Machine-learning excels in many areas with well-defined goals. However, a
clear goal is usually not available in art forms, such as photography. The
success of a photograph is measured by its aesthetic value, a very subjective
concept. This adds to the challenge for a machine learning approach.
  We introduce Creatism, a deep-learning system for artistic content creation.
In our system, we break down aesthetics into multiple aspects, each can be
learned individually from a shared dataset of professional examples. Each
aspect corresponds to an image operation that can be optimized efficiently. A
novel editing tool, dramatic mask, is introduced as one operation that improves
dramatic lighting for a photo. Our training does not require a dataset with
before/after image pairs, or any additional labels to indicate different
aspects in aesthetics.
  Using our system, we mimic the workflow of a landscape photographer, from
framing for the best composition to carrying out various post-processing
operations. The environment for our virtual photographer is simulated by a
collection of panorama images from Google Street View. We design a
"Turing-test"-like experiment to objectively measure quality of its creations,
where professional photographers rate a mixture of photographs from different
sources blindly. Experiments show that a portion of our robot's creation can be
confused with professional work.
Detecting Policy Preferences and Dynamics in the UN General Debate with Neural Word Embeddings
Authors: Stefano Gurciullo, Slava Mikhaylov
Date: 2017-07-11 00:00:00
Tasks: Word Embeddings

Foreign policy analysis has been struggling to find ways to measure policy
preferences and paradigm shifts in international political systems. This paper
presents a novel, potential solution to this challenge, through the application
of a neural word embedding (Word2vec) model on a dataset featuring speeches by
heads of state or government in the United Nations General Debate. The paper
provides three key contributions based on the output of the Word2vec model.
First, it presents a set of policy attention indices, synthesizing the semantic
proximity of political speeches to specific policy themes. Second, it
introduces country-specific semantic centrality indices, based on topological
analyses of countries' semantic positions with respect to each other. Third, it
tests the hypothesis that there exists a statistical relation between the
semantic content of political speeches and UN voting behavior, falsifying it
and suggesting that political speeches contain information of different nature
then the one behind voting outcomes. The paper concludes with a discussion of
the practical use of its results and consequences for foreign policy analysis,
public accountability, and transparency.
Why do similarity matching objectives lead to Hebbian/anti-Hebbian networks?
Authors: Cengiz Pehlevan, Anirvan Sengupta, Dmitri B. Chklovskii
Date: 2017-03-23 00:00:00
Tasks: Dimensionality Reduction

Modeling self-organization of neural networks for unsupervised learning using
Hebbian and anti-Hebbian plasticity has a long history in neuroscience. Yet,
derivations of single-layer networks with such local learning rules from
principled optimization objectives became possible only recently, with the
introduction of similarity matching objectives. What explains the success of
similarity matching objectives in deriving neural networks with local learning
rules? Here, using dimensionality reduction as an example, we introduce several
variable substitutions that illuminate the success of similarity matching. We
show that the full network objective may be optimized separately for each
synapse using local learning rules both in the offline and online settings. We
formalize the long-standing intuition of the rivalry between Hebbian and
anti-Hebbian rules by formulating a min-max optimization problem. We introduce
a novel dimensionality reduction objective using fractional matrix exponents.
To illustrate the generality of our approach, we apply it to a novel
formulation of dimensionality reduction combined with whitening. We confirm
numerically that the networks with learning rules derived from principled
objectives perform better than those with heuristic learning rules.
An Introduction to Deep Learning for the Physical Layer
Authors: Timothy J. O'Shea, Jakob Hoydis
Date: 2017-02-02 00:00:00
Tasks: Deep Learning, General Classification

We present and discuss several novel applications of deep learning for the
physical layer. By interpreting a communications system as an autoencoder, we
develop a fundamental new way to think about communications system design as an
end-to-end reconstruction task that seeks to jointly optimize transmitter and
receiver components in a single process. We show how this idea can be extended
to networks of multiple transmitters and receivers and present the concept of
radio transformer networks as a means to incorporate expert domain knowledge in
the machine learning model. Lastly, we demonstrate the application of
convolutional neural networks on raw IQ samples for modulation classification
which achieves competitive accuracy with respect to traditional schemes relying
on expert features. The paper is concluded with a discussion of open challenges
and areas for future investigation.
Proceedings of the 2017 AdKDD & TargetAd Workshop
Authors: Abraham Bagherjeiran, Nemanja Djuric, Mihajlo Grbovic, Kuang-Chih Lee, Kun Liu, Vladan Radosavljevic, Suju Rajan
Date: 2017-07-11 00:00:00

Proceedings of the 2017 AdKDD and TargetAd Workshop held in conjunction with
the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Halifax,
Nova Scotia, Canada.
Multiple Context-Free Tree Grammars: Lexicalization and Characterization
Authors: Joost Engelfriet, Andreas Maletti, Sebastian Maneth
Date: 2017-07-11 00:00:00

Multiple (simple) context-free tree grammars are investigated, where "simple"
means "linear and nondeleting". Every multiple context-free tree grammar that
is finitely ambiguous can be lexicalized; i.e., it can be transformed into an
equivalent one (generating the same tree language) in which each rule of the
grammar contains a lexical symbol. Due to this transformation, the rank of the
nonterminals increases at most by 1, and the multiplicity (or fan-out) of the
grammar increases at most by the maximal rank of the lexical symbols; in
particular, the multiplicity does not increase when all lexical symbols have
rank 0. Multiple context-free tree grammars have the same tree generating power
as multi-component tree adjoining grammars (provided the latter can use a
root-marker). Moreover, every multi-component tree adjoining grammar that is
finitely ambiguous can be lexicalized. Multiple context-free tree grammars have
the same string generating power as multiple context-free (string) grammars and
polynomial time parsing algorithms. A tree language can be generated by a
multiple context-free tree grammar if and only if it is the image of a regular
tree language under a deterministic finite-copying macro tree transducer.
Multiple context-free tree grammars can be used as a synchronous translation
device.
Initialising Kernel Adaptive Filters via Probabilistic Inference
Authors: Iván Castro, Cristóbal Silva, Felipe Tobar
Date: 2017-07-11 00:00:00
Tasks: Time Series, Time Series Analysis

We present a probabilistic framework for both (i) determining the initial
settings of kernel adaptive filters (KAFs) and (ii) constructing fully-adaptive
KAFs whereby in addition to weights and dictionaries, kernel parameters are
learnt sequentially. This is achieved by formulating the estimator as a
probabilistic model and defining dedicated prior distributions over the kernel
parameters, weights and dictionary, enforcing desired properties such as
sparsity. The model can then be trained using a subset of data to initialise
standard KAFs or updated sequentially each time a new observation becomes
available. Due to the nonlinear/non-Gaussian properties of the model, learning
and inference is achieved using gradient-based maximum-a-posteriori
optimisation and Markov chain Monte Carlo methods, and can be confidently used
to compute predictions. The proposed framework was validated on nonlinear time
series of both synthetic and real-world nature, where it outperformed standard
KAFs in terms of mean square error and the sparsity of the learnt dictionaries.
On the letter frequencies and entropy of written Marathi
Authors: Jaydeep Chipalkatti, Mihir Kulkarni
Date: 2017-07-11 00:00:00

We carry out a comprehensive analysis of letter frequencies in contemporary
written Marathi. We determine sets of letters which statistically predominate
any large generic Marathi text, and use these sets to estimate the entropy of
Marathi.
Multi-Task Learning Using Neighborhood Kernels
Authors: Niloofar Yousefi, Cong Li, Mansooreh Mollaghasemi, Georgios Anagnostopoulos, Michael Georgiopoulos
Date: 2017-07-11 00:00:00
Tasks: Multi-Task Learning

This paper introduces a new and effective algorithm for learning kernels in a
Multi-Task Learning (MTL) setting. Although, we consider a MTL scenario here,
our approach can be easily applied to standard single task learning, as well.
As shown by our empirical results, our algorithm consistently outperforms the
traditional kernel learning algorithms such as uniform combination solution,
convex combinations of base kernels as well as some kernel alignment-based
models, which have been proven to give promising results in the past. We
present a Rademacher complexity bound based on which a new Multi-Task Multiple
Kernel Learning (MT-MKL) model is derived. In particular, we propose a Support
Vector Machine-regularized model in which, for each task, an optimal kernel is
learned based on a neighborhood-defining kernel that is not restricted to be
positive semi-definite. Comparative experimental results are showcased that
underline the merits of our neighborhood-defining framework in both
classification and regression problems.
Reliability Assessment of Distribution System Using Fuzzy Logic for Modelling of Transformer and Line Uncertainties
Authors: Ahmad Shokrollahi, Hossein Sangrody, Mahdi Motalleb, Mandana Rezaeiahari, Elham Foruzan, Fattah Hassanzadeh
Date: 2017-07-11 00:00:00

Reliability assessment of distribution system, based on historical data and
probabilistic methods, leads to an unreliable estimation of reliability indices
since the data for the distribution components are usually inaccurate or
unavailable. Fuzzy logic is an efficient method to deal with the uncertainty in
reliability inputs. In this paper, the ENS index along with other commonly used
indices in reliability assessment are evaluated for the distribution system
using fuzzy logic. Accordingly, the influential variables on the failure rate
and outage duration time of the distribution components, which are natural or
human-made, are explained using proposed fuzzy membership functions. The
reliability indices are calculated and compared for different cases of the
system operations by simulation on the IEEE RBTS Bus 2. The results of
simulation show how utilities can significantly improve the reliability of
their distribution system by considering the risk of the influential variables.
Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text
Authors: Ayush Jaiswal, Ekraam Sabir, Wael Abd-Almageed, Premkumar Natarajan
Date: 2017-07-06 00:00:00
Tasks: Representation Learning

Real world multimedia data is often composed of multiple modalities such as
an image or a video with associated text (e.g. captions, user comments, etc.)
and metadata. Such multimodal data packages are prone to manipulations, where a
subset of these modalities can be altered to misrepresent or repurpose data
packages, with possible malicious intent. It is, therefore, important to
develop methods to assess or verify the integrity of these multimedia packages.
Using computer vision and natural language processing methods to directly
compare the image (or video) and the associated caption to verify the integrity
of a media package is only possible for a limited set of objects and scenes. In
this paper, we present a novel deep learning-based approach for assessing the
semantic integrity of multimedia packages containing images and captions, using
a reference set of multimedia packages. We construct a joint embedding of
images and captions with deep multimodal representation learning on the
reference dataset in a framework that also provides image-caption consistency
scores (ICCSs). The integrity of query media packages is assessed as the
inlierness of the query ICCSs with respect to the reference dataset. We present
the MultimodAl Information Manipulation dataset (MAIM), a new dataset of media
packages from Flickr, which we make available to the research community. We use
both the newly created dataset as well as Flickr30K and MS COCO datasets to
quantitatively evaluate our proposed approach. The reference dataset does not
contain unmanipulated versions of tampered query packages. Our method is able
to achieve F1 scores of 0.75, 0.89 and 0.94 on MAIM, Flickr30K and MS COCO,
respectively, for detecting semantically incoherent media packages.