pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks
Authors: Tolga Ergen, Ali Hassan Mirza, Suleyman Serdar Kozat
Date: 2017-10-25 00:00:00
Tasks: Anomaly Detection, Semi-supervised Anomaly Detection, Supervised Anomaly Detection, Time Series Analysis

We investigate anomaly detection in an unsupervised framework and introduce
Long Short Term Memory (LSTM) neural network based algorithms. In particular,
given variable length data sequences, we first pass these sequences through our
LSTM based structure and obtain fixed length sequences. We then find a decision
function for our anomaly detectors based on the One Class Support Vector
Machines (OC-SVM) and Support Vector Data Description (SVDD) algorithms. As the
first time in the literature, we jointly train and optimize the parameters of
the LSTM architecture and the OC-SVM (or SVDD) algorithm using highly effective
gradient and quadratic programming based training methods. To apply the
gradient based training method, we modify the original objective criteria of
the OC-SVM and SVDD algorithms, where we prove the convergence of the modified
objective criteria to the original criteria. We also provide extensions of our
unsupervised formulation to the semi-supervised and fully supervised
frameworks. Thus, we obtain anomaly detection algorithms that can process
variable length data sequences while providing high performance, especially for
time series data. Our approach is generic so that we also apply this approach
to the Gated Recurrent Unit (GRU) architecture by directly replacing our LSTM
based structure with the GRU based structure. In our experiments, we illustrate
significant performance gains achieved by our algorithms with respect to the
conventional methods.
Putting Self-Supervised Token Embedding on the Tables
Authors: Marc Szafraniec, Gautier Marti, Philippe Donnat
Date: 2017-07-28 00:00:00

Information distribution by electronic messages is a privileged means of
transmission for many businesses and individuals, often under the form of
plain-text tables. As their number grows, it becomes necessary to use an
algorithm to extract text and numbers instead of a human. Usual methods are
focused on regular expressions or on a strict structure in the data, but are
not efficient when we have many variations, fuzzy structure or implicit labels.
In this paper we introduce SC2T, a totally self-supervised model for
constructing vector representations of tokens in semi-structured messages by
using characters and context levels that address these issues. It can then be
used for an unsupervised labeling of tokens, or be the basis for a
semi-supervised information extraction system.
Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network
Authors: Eric Laloy, Romain Hérault, John Lee, Diederik Jacques, Niklas Linde
Date: 2017-10-25 00:00:00
Tasks: Dimensionality Reduction

Efficient and high-fidelity prior sampling and inversion for complex
geological media is still a largely unsolved challenge. Here, we use a deep
neural network of the variational autoencoder type to construct a parametric
low-dimensional base model parameterization of complex binary geological media.
For inversion purposes, it has the attractive feature that random draws from an
uncorrelated standard normal distribution yield model realizations with spatial
characteristics that are in agreement with the training set. In comparison with
the most commonly used parametric representations in probabilistic inversion,
we find that our dimensionality reduction (DR) approach outperforms principle
component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform
(DCT) DR techniques for unconditional geostatistical simulation of a
channelized prior model. For the considered examples, important compression
ratios (200 - 500) are achieved. Given that the construction of our
parameterization requires a training set of several tens of thousands of prior
model realizations, our DR approach is more suited for probabilistic (or
deterministic) inversion than for unconditional (or point-conditioned)
geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D
transient hydraulic tomography data are used to demonstrate the DR-based
inversion. For the 2D case study, the performance is superior compared to
current state-of-the-art multiple-point statistics inversion by sequential
geostatistical resampling (SGR). Inversion results for the 3D application are
also encouraging.
Generalizing the Convolution Operator to extend CNNs to Irregular Domains
Authors: Jean-Charles Vialatte, Vincent Gripon, Grégoire Mercier
Date: 2016-06-03 00:00:00

Convolutional Neural Networks (CNNs) have become the state-of-the-art in
supervised learning vision tasks. Their convolutional filters are of paramount
importance for they allow to learn patterns while disregarding their locations
in input images. When facing highly irregular domains, generalized
convolutional operators based on an underlying graph structure have been
proposed. However, these operators do not exactly match standard ones on grid
graphs, and introduce unwanted additional invariance (e.g. with regards to
rotations). We propose a novel approach to generalize CNNs to irregular domains
using weight sharing and graph-based operators. Using experiments, we show that
these models resemble CNNs on regular domains and offer better performance than
multilayer perceptrons on distorded ones.
Exploiting generalization in the subspaces for faster model-based learning
Authors: Maryam Hashemzadeh, Reshad Hosseini, Majid Nili Ahmadabadi
Date: 2017-10-22 00:00:00
Tasks: Decision Making, Reinforcement Learning, Reinforcement Learning (RL)

Due to the lack of enough generalization in the state-space, common methods
in Reinforcement Learning (RL) suffer from slow learning speed especially in
the early learning trials. This paper introduces a model-based method in
discrete state-spaces for increasing learning speed in terms of required
experience (but not required computational time) by exploiting generalization
in the experiences of the subspaces. A subspace is formed by choosing a subset
of features in the original state representation (full-space). Generalization
and faster learning in a subspace are due to many-to-one mapping of experiences
from the full-space to each state in the subspace. Nevertheless, due to
inherent perceptual aliasing in the subspaces, the policy suggested by each
subspace does not generally converge to the optimal policy. Our approach,
called Model Based Learning with Subspaces (MoBLeS), calculates confidence
intervals of the estimated Q-values in the full-space and in the subspaces.
These confidence intervals are used in the decision making, such that the agent
benefits the most from the possible generalization while avoiding from
detriment of the perceptual aliasing in the subspaces. Convergence of MoBLeS to
the optimal policy is theoretically investigated. Additionally, we show through
several experiments that MoBLeS improves the learning speed in the early
trials.
Biometrics-as-a-Service: A Framework to Promote Innovative Biometric Recognition in the Cloud
Authors: Veeru Talreja, Terry Ferrett, Matthew C. Valenti, Arun Ross
Date: 2017-10-25 00:00:00

Biometric recognition, or simply biometrics, is the use of biological
attributes such as face, fingerprints or iris in order to recognize an
individual in an automated manner. A key application of biometrics is
authentication; i.e., using said biological attributes to provide access by
verifying the claimed identity of an individual. This paper presents a
framework for Biometrics-as-a-Service (BaaS) that performs biometric matching
operations in the cloud, while relying on simple and ubiquitous consumer
devices such as smartphones. Further, the framework promotes innovation by
providing interfaces for a plurality of software developers to upload their
matching algorithms to the cloud. When a biometric authentication request is
submitted, the system uses a criteria to automatically select an appropriate
matching algorithm. Every time a particular algorithm is selected, the
corresponding developer is rendered a micropayment. This creates an innovative
and competitive ecosystem that benefits both software developers and the
consumers. As a case study, we have implemented the following: (a) an ocular
recognition system using a mobile web interface providing user access to a
biometric authentication service, and (b) a Linux-based virtual machine
environment used by software developers for algorithm development and
submission.
Neural Stain-Style Transfer Learning using GAN for Histopathological Images
Authors: Hyungjoo Cho, Sungbin Lim, Gunho Choi, Hyun-seok Min
Date: 2017-10-23 00:00:00
Tasks: General Classification, Style Transfer, Transfer Learning

Performance of data-driven network for tumor classification varies with
stain-style of histopathological images. This article proposes the stain-style
transfer (SST) model based on conditional generative adversarial networks
(GANs) which is to learn not only the certain color distribution but also the
corresponding histopathological pattern. Our model considers feature-preserving
loss in addition to well-known GAN loss. Consequently our model does not only
transfers initial stain-styles to the desired one but also prevent the
degradation of tumor classifier on transferred images. The model is examined
using the CAMELYON16 dataset.
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions
Authors: Aryeh Kontorovich, Sivan Sabato, Roi Weiss
Date: 2017-05-23 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Generalization Bounds

We examine the Bayes-consistency of a recently proposed 1-nearest-neighbor-based multiclass learning algorithm. This algorithm is derived from sample compression bounds and enjoys the statistical advantages of tight, fully empirical generalization bounds, as well as the algorithmic advantages of a faster runtime and memory savings. We prove that this algorithm is strongly Bayes-consistent in metric spaces with finite doubling dimension --- the first consistency result for an efficient nearest-neighbor sample compression scheme. Rather surprisingly, we discover that this algorithm continues to be Bayes-consistent even in a certain infinite-dimensional setting, in which the basic measure-theoretic conditions on which classic consistency proofs hinge are violated. This is all the more surprising, since it is known that $k$-NN is not Bayes-consistent in this setting. We pose several challenging open problems for future research.
Compressive Online Robust Principal Component Analysis with Optical Flow for Video Foreground-Background Separation
Authors: Srivatsa Prativadibhayankaram, Huynh Van Luong, Thanh-Ha Le, Andre Kaup
Date: 2017-10-25 00:00:00
Tasks: Optical Flow Estimation

In the context of online Robust Principle Component Analysis (RPCA) for the
video foreground-background separation, we propose a compressive online RPCA
with optical flow that separates recursively a sequence of frames into sparse
(foreground) and low-rank (background) components. Our method considers a small
set of measurements taken per data vector (frame), which is different from
conventional batch RPCA, processing all the data directly. The proposed method
also incorporates multiple prior information, namely previous foreground and
background frames, to improve the separation and then updates the prior
information for the next frame. Moreover, the foreground prior frames are
improved by estimating motions between the previous foreground frames using
optical flow and compensating the motions to achieve higher quality foreground
prior. The proposed method is applied to online video foreground and background
separation from compressive measurements. The visual and quantitative results
show that our method outperforms the existing methods.
Linking Tweets with Monolingual and Cross-Lingual News using Transformed Word Embeddings
Authors: Aditya Mogadala, Dominik Jung, Achim Rettinger
Date: 2017-10-25 00:00:00
Tasks: Articles, Word Embeddings

Social media platforms have grown into an important medium to spread
information about an event published by the traditional media, such as news
articles. Grouping such diverse sources of information that discuss the same
topic in varied perspectives provide new insights. But the gap in word usage
between informal social media content such as tweets and diligently written
content (e.g. news articles) make such assembling difficult. In this paper, we
propose a transformation framework to bridge the word usage gap between tweets
and online news articles across languages by leveraging their word embeddings.
Using our framework, word embeddings extracted from tweets and news articles
are aligned closer to each other across languages, thus facilitating the
identification of similarity between news articles and tweets. Experimental
results show a notable improvement over baselines for monolingual tweets and
news articles comparison, while new findings are reported for cross-lingual
comparison.
TANKER: Distributed Architecture for Named Entity Recognition and Disambiguation
Authors: Sandro A. Coelho, Diego Moussallem, Gustavo C. Publio, Diego Esteves
Date: 2017-08-30 00:00:00
Tasks: Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Translation

Named Entity Recognition and Disambiguation (NERD) systems have recently been
widely researched to deal with the significant growth of the Web. NERD systems
are crucial for several Natural Language Processing (NLP) tasks such as
summarization, understanding, and machine translation. However, there is no
standard interface specification, i.e. these systems may vary significantly
either for exporting their outputs or for processing the inputs. Thus, when a
given company desires to implement more than one NERD system, the process is
quite exhaustive and prone to failure. In addition, industrial solutions demand
critical requirements, e.g., large-scale processing, completeness, versatility,
and licenses. Commonly, these requirements impose a limitation, making good
NERD models to be ignored by companies. This paper presents TANKER, a
distributed architecture which aims to overcome scalability, reliability and
failure tolerance limitations related to industrial needs by combining NERD
systems. To this end, TANKER relies on a micro-services oriented architecture,
which enables agile development and delivery of complex enterprise
applications. In addition, TANKER provides a standardized API which makes
possible to combine several NERD systems at once.
Switched latent force models for reverse-engineering transcriptional regulation in gene expression data
Authors: Andrés F. López-Lopera, Mauricio A. Álvarez
Date: 2015-11-23 00:00:00

To survive environmental conditions, cells transcribe their response
activities into encoded mRNA sequences in order to produce certain amounts of
protein concentrations. The external conditions are mapped into the cell
through the activation of special proteins called transcription factors (TFs).
Due to the difficult task to measure experimentally TF behaviours, and the
challenges to capture their quick-time dynamics, different types of models
based on differential equations have been proposed. However, those approaches
usually incur in costly procedures, and they present problems to describe
sudden changes in TF regulators. In this paper, we present a switched dynamical
latent force model for reverse-engineering transcriptional regulation in gene
expression data which allows the exact inference over latent TF activities
driving some observed gene expressions through a linear differential equation.
To deal with discontinuities in the dynamics, we introduce an approach that
switches between different TF activities and different dynamical systems. This
creates a versatile representation of transcription networks that can capture
discrete changes and non-linearities We evaluate our model on both simulated
data and real-data (e.g. microaerobic shift in E. coli, yeast respiration),
concluding that our framework allows for the fitting of the expression data
while being able to infer continuous-time TF profiles.
Toward Multidiversified Ensemble Clustering of High-Dimensional Data: From Subspaces to Metrics and Beyond
Authors: Dong Huang, Chang-Dong Wang, Jian-Huang Lai, Chee-Keong Kwoh
Date: 2017-10-09 00:00:00
Tasks: Clustering, Diversity

The rapid emergence of high-dimensional data in various areas has brought new challenges to current ensemble clustering research. To deal with the curse of dimensionality, recently considerable efforts in ensemble clustering have been made by means of different subspace-based techniques. However, besides the emphasis on subspaces, rather limited attention has been paid to the potential diversity in similarity/dissimilarity metrics. It remains a surprisingly open problem in ensemble clustering how to create and aggregate a large population of diversified metrics, and furthermore, how to jointly investigate the multi-level diversity in the large populations of metrics, subspaces, and clusters in a unified framework. To tackle this problem, this paper proposes a novel multidiversified ensemble clustering approach. In particular, we create a large number of diversified metrics by randomizing a scaled exponential similarity kernel, which are then coupled with random subspaces to form a large set of metric-subspace pairs. Based on the similarity matrices derived from these metric-subspace pairs, an ensemble of diversified base clusterings can thereby be constructed. Further, an entropy-based criterion is utilized to explore the cluster-wise diversity in ensembles, based on which three specific ensemble clustering algorithms are presented by incorporating three types of consensus functions. Extensive experiments are conducted on 30 high-dimensional datasets, including 18 cancer gene expression datasets and 12 image/speech datasets, which demonstrate the superiority of our algorithms over the state-of-the-art. The source code is available at https://github.com/huangdonghere/MDEC.
Sufficient and necessary causation are dual
Authors: Robert Künnemann
Date: 2017-10-25 00:00:00
Tasks: Relation

Causation has been the issue of philosophic debate since Hippocrates. Recent
work defines actual causation in terms of Pearl/Halpern's causality framework,
formalizing necessary causes (IJCAI'15). This has inspired causality notions in
the security domain (CSF'15), which, perhaps surprisingly, formalize sufficient
causes instead. We provide an explicit relation between necessary and
sufficient causes.
Kalman Filters and Homography: Utilizing the Matrix $A$
Authors: Burak Bayramli
Date: 2010-06-25 00:00:00
Tasks: Visual Tracking

Many problems in Computer Vision can be reduced to either working around a known transform, or given a model for the transform computing the inverse problem of the transform itself. We will look at two ways of working with the matrix $A$ and see how transforms are at the root of image processing and vision problems.
Re-evaluating the need for Modelling Term-Dependence in Text Classification Problems
Authors: Sounak Banerjee, Prasenjit Majumder, Mandar Mitra
Date: 2017-10-25 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

A substantial amount of research has been carried out in developing machine
learning algorithms that account for term dependence in text classification.
These algorithms offer acceptable performance in most cases but they are
associated with a substantial cost. They require significantly greater
resources to operate. This paper argues against the justification of the higher
costs of these algorithms, based on their performance in text classification
problems. In order to prove the conjecture, the performance of one of the best
dependence models is compared to several well established algorithms in text
classification. A very specific collection of datasets have been designed,
which would best reflect the disparity in the nature of text data, that are
present in real world applications. The results show that even one of the best
term dependence models, performs decent at best when compared to other
independence models. Coupled with their substantially greater requirement for
hardware resources for operation, this makes them an impractical choice for
being used in real world scenarios.
Crop Planning using Stochastic Visual Optimization
Authors: Gunjan Sehgal, Bindu Gupta, Kaushal Paneri, Karamjit Singh, Geetika Sharma, Gautam Shroff
Date: 2017-10-25 00:00:00
Tasks: Decision Making

As the world population increases and arable land decreases, it becomes vital
to improve the productivity of the agricultural land available. Given the
weather and soil properties, farmers need to take critical decisions such as
which seed variety to plant and in what proportion, in order to maximize
productivity. These decisions are irreversible and any unusual behavior of
external factors, such as weather, can have catastrophic impact on the
productivity of crop. A variety which is highly desirable to a farmer might be
unavailable or in short supply, therefore, it is very critical to evaluate
which variety or varieties are more likely to be chosen by farmers from a
growing region in order to meet demand. In this paper, we present our visual
analytics tool, ViSeed, showcased on the data given in Syngenta 2016 crop data
challenge 1 . This tool helps to predict optimal soybean seed variety or mix of
varieties in appropriate proportions which is more likely to be chosen by
farmers from a growing region. It also allows to analyse solutions generated
from our approach and helps in the decision making process by providing
insightful visualizations
A Predictive Account of Cafe Wall Illusions Using a Quantitative Model
Authors: Nasim Nematzadeh, David M. W. Powers
Date: 2017-05-19 00:00:00

This paper explores the tilt illusion effect in the Cafe Wall pattern using a
classical Gaussian Receptive Field model. In this illusion, the mortar lines
are misperceived as diverging or converging rather than horizontal. We examine
the capability of a simple bioplausible filtering model to recognize different
degrees of tilt effect in the Cafe Wall illusion based on different
characteristics of the pattern. Our study employed a Difference of Gaussians
model of retinal to cortical ON center and/or OFF center receptive fields. A
wide range of parameters of the stimulus, for example mortar thickness,
luminance, tiles contrast, phase of the tile displacement, have been studied.
Our model constructs an edge map representation at multiple scales that reveals
tilt cues and clues involved in the illusory perception of the Cafe Wall
pattern. We present here that our model can not only detect the tilt in this
pattern, but also can predict the strength of the illusion and quantify the
degree of tilt. For the first time quantitative predictions of a model are
reported for this stimulus. The results of our simulations are consistent with
previous psychophysical findings across the full range of Cafe Wall variations
tested. Our results also suggest that the Difference of Gaussians mechanism is
the heart of the effects explained by, and the mechanisms proposed for, the
Irradiation, Brightness Induction, and Bandpass Filtering models.
End-to-End Optimized Speech Coding with Deep Neural Networks
Authors: Srihari Kankanahalli
Date: 2017-10-25 00:00:00
Tasks: CPU, Feature Engineering, Quantization

Modern compression algorithms are often the result of laborious domain-specific research; industry standards such as MP3, JPEG, and AMR-WB took years to develop and were largely hand-designed. We present a deep neural network model which optimizes all the steps of a wideband speech coding pipeline (compression, quantization, entropy coding, and decompression) end-to-end directly from raw speech data -- no manual feature engineering necessary, and it trains in hours. In testing, our DNN-based coder performs on par with the AMR-WB standard at a variety of bitrates (~9kbps up to ~24kbps). It also runs in realtime on a 3.8GhZ Intel CPU.