pwc-archive/papers-with-abstracts huggingface dataset:
Inductive Pairwise Ranking: Going Beyond the n log(n) Barrier
Authors: U. N. Niranjan, Arun Rajkumar
Date: 2017-02-09 00:00:00
Tasks: Matrix Completion

We study the problem of ranking a set of items from nonactively chosen
pairwise preferences where each item has feature information with it. We
propose and characterize a very broad class of preference matrices giving rise
to the Feature Low Rank (FLR) model, which subsumes several models ranging from
the classic Bradley-Terry-Luce (BTL) (Bradley and Terry 1952) and Thurstone
(Thurstone 1927) models to the recently proposed blade-chest (Chen and Joachims
2016) and generic low-rank preference (Rajkumar and Agarwal 2016) models. We
use the technique of matrix completion in the presence of side information to
develop the Inductive Pairwise Ranking (IPR) algorithm that provably learns a
good ranking under the FLR model, in a sample-efficient manner. In practice,
through systematic synthetic simulations, we confirm our theoretical findings
regarding improvements in the sample complexity due to the use of feature
information. Moreover, on popular real-world preference learning datasets, with
as less as 10% sampling of the pairwise comparisons, our method recovers a good
ranking.
EEG Representation Using Multi-instance Framework on The Manifold of Symmetric Positive Definite Matrices for EEG-based Computer Aided Diagnosis
Authors: Khadijeh Sadatnejad, Saeed S. Ghidary, Reza Rostami, Reza Kazemi
Date: 2017-02-08 00:00:00
Tasks: Diagnostic, EEG, Electroencephalogram (EEG)

The generalization and robustness of an electroencephalogram (EEG)-based
computer aided diagnostic system are crucial requirements in actual clinical
practice. To reach these goals, we propose a new EEG representation that
provides a more realistic view of brain functionality by applying
multi-instance (MI) framework to consider the non-stationarity of the EEG
signal. The non-stationary characteristic of EEG is considered by describing
the signal as a bag of relevant and irrelevant concepts. The concepts are
provided by a robust representation of homogenous segments of EEG signal using
spatial covariance matrices. Due to the nonlinear geometry of the space of
covariance matrices, we determine the boundaries of the homogeneous segments
based on adaptive segmentation of the signal in a Riemannian framework. Each
subject is described as a bag of covariance matrices of homogenous segments and
the bag-level discriminative information is used for classification. To
evaluate the performance of the proposed approach, we examine it in attention
deficit hyperactivity/bipolar mood disorder detection and depression/normal
diagnosis applications. Experimental results confirm the superiority of the
proposed approach, which is gained due to the robustness of covariance
descriptor, the effectiveness of Riemannian geometry, and the benefits of
considering the inherent non-stationary nature of the brain.
Character-level Deep Conflation for Business Data Analytics
Authors: Zhe Gan, P. D. Singh, Ameet Joshi, Xiaodong He, Jianshu Chen, Jianfeng Gao, Li Deng
Date: 2017-02-08 00:00:00

Connecting different text attributes associated with the same entity
(conflation) is important in business data analytics since it could help merge
two different tables in a database to provide a more comprehensive profile of
an entity. However, the conflation task is challenging because two text strings
that describe the same entity could be quite different from each other for
reasons such as misspelling. It is therefore critical to develop a conflation
model that is able to truly understand the semantic meaning of the strings and
match them at the semantic level. To this end, we develop a character-level
deep conflation model that encodes the input text strings from character level
into finite dimension feature vectors, which are then used to compute the
cosine similarity between the text strings. The model is trained in an
end-to-end manner using back propagation and stochastic gradient descent to
maximize the likelihood of the correct association. Specifically, we propose
two variants of the deep conflation model, based on long-short-term memory
(LSTM) recurrent neural network (RNN) and convolutional neural network (CNN),
respectively. Both models perform well on a real-world business analytics
dataset and significantly outperform the baseline bag-of-character (BoC) model.
Backpropagation Training for Fisher Vectors within Neural Networks
Authors: Patrick Wieschollek, Fabian Groh, Hendrik P. A. Lensch
Date: 2017-02-08 00:00:00
Tasks: GPU

Fisher-Vectors (FV) encode higher-order statistics of a set of multiple local
descriptors like SIFT features. They already show good performance in
combination with shallow learning architectures on visual recognitions tasks.
Current methods using FV as a feature descriptor in deep architectures assume
that all original input features are static. We propose a framework to jointly
learn the representation of original features, FV parameters and parameters of
the classifier in the style of traditional neural networks. Our proof of
concept implementation improves the performance of FV on the Pascal Voc 2007
challenge in a multi-GPU setting in comparison to a default SVM setting. We
demonstrate that FV can be embedded into neural networks at arbitrary
positions, allowing end-to-end training with back-propagation.
Soft Biometrics: Gender Recognition from Unconstrained Face Images using Local Feature Descriptor
Authors: Olasimbo Ayodeji Arigbabu, Sharifah Mumtazah Syed Ahmad, Wan Azizun Wan Adnan, Salman Yussof, Saif Mahmood
Date: 2017-02-08 00:00:00

Gender recognition from unconstrained face images is a challenging task due
to the high degree of misalignment, pose, expression, and illumination
variation. In previous works, the recognition of gender from unconstrained face
images is approached by utilizing image alignment, exploiting multiple samples
per individual to improve the learning ability of the classifier, or learning
gender based on prior knowledge about pose and demographic distributions of the
dataset. However, image alignment increases the complexity and time of
computation, while the use of multiple samples or having prior knowledge about
data distribution is unrealistic in practical applications. This paper presents
an approach for gender recognition from unconstrained face images. Our
technique exploits the robustness of local feature descriptor to photometric
variations to extract the shape description of the 2D face image using a single
sample image per individual. The results obtained from experiments on Labeled
Faces in the Wild (LFW) dataset describe the effectiveness of the proposed
method. The essence of this study is to investigate the most suitable functions
and parameter settings for recognizing gender from unconstrained face images.
Cooperative Training of Descriptor and Generator Networks
Authors: Jianwen Xie, Yang Lu, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu
Date: 2016-09-29 00:00:00

This paper studies the cooperative training of two generative models for
image modeling and synthesis. Both models are parametrized by convolutional
neural networks (ConvNets). The first model is a deep energy-based model, whose
energy function is defined by a bottom-up ConvNet, which maps the observed
image to the energy. We call it the descriptor network. The second model is a
generator network, which is a non-linear version of factor analysis. It is
defined by a top-down ConvNet, which maps the latent factors to the observed
image. The maximum likelihood learning algorithms of both models involve MCMC
sampling such as Langevin dynamics. We observe that the two learning algorithms
can be seamlessly interwoven into a cooperative learning algorithm that can
train both models simultaneously. Specifically, within each iteration of the
cooperative learning algorithm, the generator model generates initial
synthesized examples to initialize a finite-step MCMC that samples and trains
the energy-based descriptor model. After that, the generator model learns from
how the MCMC changes its synthesized examples. That is, the descriptor model
teaches the generator model by MCMC, so that the generator model accumulates
the MCMC transitions and reproduces them by direct ancestral sampling. We call
this scheme MCMC teaching. We show that the cooperative algorithm can learn
highly realistic generative models.
Learning detectors of malicious web requests for intrusion detection in network traffic
Authors: Lukas Machlica, Karel Bartos, Michal Sofka
Date: 2017-02-08 00:00:00
Tasks: Intrusion Detection

This paper proposes a generic classification system designed to detect
security threats based on the behavior of malware samples. The system relies on
statistical features computed from proxy log fields to train detectors using a
database of malware samples. The behavior detectors serve as basic reusable
building blocks of the multi-level detection architecture. The detectors
identify malicious communication exploiting encrypted URL strings and domains
generated by a Domain Generation Algorithm (DGA) which are frequently used in
Command and Control (C&C), phishing, and click fraud. Surprisingly, very
precise detectors can be built given only a limited amount of information
extracted from a single proxy log. This way, the computational requirements of
the detectors are kept low which allows for deployment on a wide range of
security devices and without depending on traffic context such as DNS logs,
Whois records, webpage content, etc. Results on several weeks of live traffic
from 100+ companies having 350k+ hosts show correct detection with a precision
exceeding 95% of malicious flows, 95% of malicious URLs and 90% of infected
hosts. In addition, a comparison with a signature and rule-based solution shows
that our system is able to detect significant amount of new threats.
Deep Kernelized Autoencoders
Authors: Michael Kampffmeyer, Sigurd Løkse, Filippo Maria Bianchi, Robert Jenssen, Lorenzo Livi
Date: 2017-02-08 00:00:00
Tasks: Denoising

In this paper we introduce the deep kernelized autoencoder, a neural network
model that allows an explicit approximation of (i) the mapping from an input
space to an arbitrary, user-specified kernel space and (ii) the back-projection
from such a kernel space to input space. The proposed method is based on
traditional autoencoders and is trained through a new unsupervised loss
function. During training, we optimize both the reconstruction accuracy of
input samples and the alignment between a kernel matrix given as prior and the
inner products of the hidden representations computed by the autoencoder.
Kernel alignment provides control over the hidden representation learned by the
autoencoder. Experiments have been performed to evaluate both reconstruction
and kernel alignment performance. Additionally, we applied our method to
emulate kPCA on a denoising task obtaining promising results.
Monocular LSD-SLAM Integration within AR System
Authors: Markus Höll, Vincent Lepetit
Date: 2017-02-08 00:00:00
Tasks: Simultaneous Localization and Mapping

In this paper, we cover the process of integrating Large-Scale Direct
Simultaneous Localization and Mapping (LSD-SLAM) algorithm into our existing AR
stereo engine, developed for our modified "Augmented Reality Oculus Rift". With
that, we are able to track one of our realworld cameras which are mounted on
the rift, within a complete unknown environment. This makes it possible to
achieve a constant and full augmentation, synchronizing our 3D movement (x, y,
z) in both worlds, the real world and the virtual world. The development for
the basic AR setup using the Oculus Rift DK1 and two fisheye cameras is fully
documented in our previous paper. After an introduction to image-based
registration, we detail the LSD-SLAM algorithm and document our code
implementing our integration. The AR stereo engine with Oculus Rift support can
be accessed via the GIT repository https://github.com/MaXvanHeLL/ARift.git and
the modified LSD-SLAM project used for the integration is available here
https://github.com/MaXvanHeLL/LSD-SLAM.git.
Signal Processing on Graphs: Causal Modeling of Unstructured Data
Authors: Jonathan Mei, José M. F. Moura
Date: 2015-02-28 00:00:00
Tasks: Time Series, Time Series Analysis

Many applications collect a large number of time series, for example, the
financial data of companies quoted in a stock exchange, the health care data of
all patients that visit the emergency room of a hospital, or the temperature
sequences continuously measured by weather stations across the US. These data
are often referred to as unstructured. A first task in its analytics is to
derive a low dimensional representation, a graph or discrete manifold, that
describes well the interrelations among the time series and their
intrarelations across time. This paper presents a computationally tractable
algorithm for estimating this graph that structures the data. The resulting
graph is directed and weighted, possibly capturing causal relations, not just
reciprocal correlations as in many existing approaches in the literature. A
convergence analysis is carried out. The algorithm is demonstrated on random
graph datasets and real network time series datasets, and its performance is
compared to that of related methods. The adjacency matrices estimated with the
new method are close to the true graph in the simulated data and consistent
with prior physical knowledge in the real dataset tested.
Trainable Greedy Decoding for Neural Machine Translation
Authors: Jiatao Gu, Kyunghyun Cho, Victor O. K. Li
Date: 2017-02-08 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder, Machine Translation, Translation

Recent research in neural machine translation has largely focused on two
aspects; neural network architectures and end-to-end learning algorithms. The
problem of decoding, however, has received relatively little attention from the
research community. In this paper, we solely focus on the problem of decoding
given a trained neural machine translation model. Instead of trying to build a
new decoding algorithm for any specific decoding objective, we propose the idea
of trainable decoding algorithm in which we train a decoding algorithm to find
a translation that maximizes an arbitrary decoding objective. More
specifically, we design an actor that observes and manipulates the hidden state
of the neural machine translation decoder and propose to train it using a
variant of deterministic policy gradient. We extensively evaluate the proposed
algorithm using four language pairs and two decoding objectives and show that
we can indeed train a trainable greedy decoder that generates a better
translation (in terms of a target decoding objective) with minimal
computational overhead.
Data Selection Strategies for Multi-Domain Sentiment Analysis
Authors: Sebastian Ruder, Parsa Ghaffari, John G. Breslin
Date: 2017-02-08 00:00:00
Tasks: Domain Adaptation, Sentiment Analysis

Domain adaptation is important in sentiment analysis as sentiment-indicating
words vary between domains. Recently, multi-domain adaptation has become more
pervasive, but existing approaches train on all available source domains
including dissimilar ones. However, the selection of appropriate training data
is as important as the choice of algorithm. We undertake -- to our knowledge
for the first time -- an extensive study of domain similarity metrics in the
context of sentiment analysis and propose novel representations, metrics, and a
new scope for data selection. We evaluate the proposed methods on two
large-scale multi-domain adaptation settings on tweets and reviews and
demonstrate that they consistently outperform strong random and balanced
baselines, while our proposed selection strategy outperforms instance-level
selection and yields the best score on a large reviews corpus.
Unsupervised Dialogue Act Induction using Gaussian Mixtures
Authors: Tomáš Brychcín, Pavel Král
Date: 2016-12-20 00:00:00

This paper introduces a new unsupervised approach for dialogue act induction.
Given the sequence of dialogue utterances, the task is to assign them the
labels representing their function in the dialogue.
  Utterances are represented as real-valued vectors encoding their meaning. We
model the dialogue as Hidden Markov model with emission probabilities estimated
by Gaussian mixtures. We use Gibbs sampling for posterior inference.
  We present the results on the standard Switchboard-DAMSL corpus. Our
algorithm achieves promising results compared with strong supervised baselines
and outperforms other unsupervised algorithms.
Towards Reading Hidden Emotions: A comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods
Authors: Xiaobai Li, Xiaopeng Hong, Antti Moilanen, Xiaohua Huang, Tomas Pfister, Guoying Zhao, Matti Pietikäinen
Date: 2015-11-02 00:00:00
Tasks: Micro-Expression Spotting

Micro-expressions (MEs) are rapid, involuntary facial expressions which
reveal emotions that people do not intend to show. Studying MEs is valuable as
recognizing them has many important applications, particularly in forensic
science and psychotherapy. However, analyzing spontaneous MEs is very
challenging due to their short duration and low intensity. Automatic ME
analysis includes two tasks: ME spotting and ME recognition. For ME spotting,
previous studies have focused on posed rather than spontaneous videos. For ME
recognition, the performance of previous studies is low. To address these
challenges, we make the following contributions: (i)We propose the first method
for spotting spontaneous MEs in long videos (by exploiting feature difference
contrast). This method is training free and works on arbitrary unseen videos.
(ii)We present an advanced ME recognition framework, which outperforms previous
work by a large margin on two challenging spontaneous ME databases (SMIC and
CASMEII). (iii)We propose the first automatic ME analysis system (MESR), which
can spot and recognize MEs from spontaneous video data. Finally, we show our
method outperforms humans in the ME recognition task by a large margin, and
achieves comparable performance to humans at the very challenging task of
spotting and then recognizing spontaneous MEs.
A Hybrid Convolutional Variational Autoencoder for Text Generation
Authors: Stanislau Semeniuta, Aliaksei Severyn, Erhardt Barth
Date: 2017-02-08 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder, Language Modeling, Language Modelling, Text Generation

In this paper we explore the effect of architectural choices on learning a
Variational Autoencoder (VAE) for text generation. In contrast to the
previously introduced VAE model for text where both the encoder and decoder are
RNNs, we propose a novel hybrid architecture that blends fully feed-forward
convolutional and deconvolutional components with a recurrent language model.
Our architecture exhibits several attractive properties such as faster run time
and convergence, ability to better handle long sequences and, more importantly,
it helps to avoid some of the major difficulties posed by training VAE models
on textual data.
An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks
Authors: Mateusz Koziński, Loïc Simon, Frédéric Jurie
Date: 2017-02-08 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

We propose a method for semi-supervised training of structured-output neural
networks. Inspired by the framework of Generative Adversarial Networks (GAN),
we train a discriminator network to capture the notion of a quality of network
output. To this end, we leverage the qualitative difference between outputs
obtained on the labelled training data and unannotated data. We then use the
discriminator as a source of error signal for unlabelled data. This effectively
boosts the performance of a network on a held out test set. Initial experiments
in image segmentation demonstrate that the proposed framework enables achieving
the same network performance as in a fully supervised scenario, while using two
times less annotations.
Preference-based Teaching
Authors: Zi-Yuan Gao, Christoph Ries, Hans Ulrich Simon, Sandra Zilles
Date: 2017-02-06 00:00:00

We introduce a new model of teaching named "preference-based teaching" and a
corresponding complexity parameter---the preference-based teaching dimension
(PBTD)---representing the worst-case number of examples needed to teach any
concept in a given concept class. Although the PBTD coincides with the
well-known recursive teaching dimension (RTD) on finite classes, it is
radically different on infinite ones: the RTD becomes infinite already for
trivial infinite classes (such as half-intervals) whereas the PBTD evaluates to
reasonably small values for a wide collection of infinite classes including
classes consisting of so-called closed sets w.r.t. a given closure operator,
including various classes related to linear sets over $\mathbb{N}_0$ (whose RTD
had been studied quite recently) and including the class of Euclidean
half-spaces. On top of presenting these concrete results, we provide the reader
with a theoretical framework (of a combinatorial flavor) which helps to derive
bounds on the PBTD.
Iterative Multi-document Neural Attention for Multiple Answer Prediction
Authors: Claudio Greco, Alessandro Suglia, Pierpaolo Basile, Gaetano Rossiello, Giovanni Semeraro
Date: 2017-02-08 00:00:00
Tasks: Prediction, Question Answering, Recommendation Systems

People have information needs of varying complexity, which can be solved by
an intelligent agent able to answer questions formulated in a proper way,
eventually considering user context and preferences. In a scenario in which the
user profile can be considered as a question, intelligent agents able to answer
questions can be used to find the most relevant answers for a given user. In
this work we propose a novel model based on Artificial Neural Networks to
answer questions with multiple answers by exploiting multiple facts retrieved
from a knowledge base. The model is evaluated on the factoid Question Answering
and top-n recommendation tasks of the bAbI Movie Dialog dataset. After
assessing the performance of the model on both tasks, we try to define the
long-term goal of a conversational recommender system able to interact using
natural language and to support users in their information seeking processes in
a personalized way.
Multi-scale Convolutional Neural Networks for Crowd Counting
Authors: Lingke Zeng, Xiangmin Xu, Bolun Cai, Suo Qiu, Tong Zhang
Date: 2017-02-08 00:00:00
Tasks: Crowd Counting

Crowd counting on static images is a challenging problem due to scale
variations. Recently deep neural networks have been shown to be effective in
this task. However, existing neural-networks-based methods often use the
multi-column or multi-network model to extract the scale-relevant features,
which is more complicated for optimization and computation wasting. To this
end, we propose a novel multi-scale convolutional neural network (MSCNN) for
single image crowd counting. Based on the multi-scale blobs, the network is
able to generate scale-relevant features for higher crowd counting performances
in a single-column architecture, which is both accuracy and cost effective for
practical applications. Complemental results show that our method outperforms
the state-of-the-art methods on both accuracy and robustness with far less
number of parameters.