pwc-archive/papers-with-abstracts huggingface dataset:
Heart Rate Variability and Respiration Signal as Diagnostic Tools for Late Onset Sepsis in Neonatal Intensive Care Units
Authors: Yu-An Wang, Guy Carrault, Alain Beuchee, Nathalie Costet, Huazhong Shu, Lotfi Senhadji
Date: 2016-05-12 00:00:00
Tasks: Diagnostic, Heart Rate Variability

Apnea-bradycardia is one of the major clinical early indicators of late-onset
sepsis occurring in approximately 7% to 10% of all neonates and in more than
25% of very low birth weight infants in NICU. The objective of this paper was
to determine if HRV, respiration and their relationships help to diagnose
infection in premature infants via non-invasive ways in NICU. Therefore, we
implement Mono-Channel (MC) and Bi-Channel (BC) Analysis in two groups: sepsis
(S) vs. non-sepsis (NS). Firstly, we studied RR series not only by linear
methods: time domain and frequency domain, but also by non-linear methods:
chaos theory and information theory. The results show that alpha Slow, alpha
Fast and Sample Entropy are significant parameters to distinguish S from NS.
Secondly, the question about the functional coupling of HRV and nasal
respiration is addressed. Local linear correlation coefficient r2t,f has been
explored, while non-linear regression coefficient h2 was calculated in two
directions. It is obvious that r2t,f within the third frequency band (0.2<f<0.4
Hz) and h2 in two directions were complementary approaches to diagnose sepsis.
Thirdly, feasibility study is carried out on the candidate parameters selected
from MC and BC respectively. We discovered that the proposed test based on
optimal fusion of 6 features shows good performance with the largest AUC and a
reduced probability of false alarm (PFA).
Gearbox Fault Detection through PSO Exact Wavelet Analysis and SVM Classifier
Authors: Amir Hosein Zamanian, Abdolreza Ohadi
Date: 2016-05-12 00:00:00
Tasks: Fault Detection

Time-frequency methods for vibration-based gearbox faults detection have been
considered the most efficient method. Among these methods, continuous wavelet
transform (CWT) as one of the best time-frequency method has been used for both
stationary and transitory signals. Some deficiencies of CWT are problem of
overlapping and distortion ofsignals. In this condition, a large amount of
redundant information exists so that it may cause false alarm or
misinterpretation of the operator. In this paper a modified method called Exact
Wavelet Analysis is used to minimize the effects of overlapping and distortion
in case of gearbox faults. To implement exact wavelet analysis, Particle Swarm
Optimization (PSO) algorithm has been used for this purpose. This method have
been implemented for the acceleration signals from 2D acceleration sensor
acquired by Advantech PCI-1710 card from a gearbox test setup in Amirkabir
University of Technology. Gearbox has been considered in both healthy and
chipped tooth gears conditions. Kernelized Support Vector Machine (SVM) with
radial basis functions has used the extracted features from exact wavelet
analysis for classification. The efficiency of this classifier is then
evaluated with the other signals acquired from the setup test. The results show
that in comparison of CWT, PSO Exact Wavelet Transform has better ability in
feature extraction in price of more computational effort. In addition, PSO
exact wavelet has better speed comparing to Genetic Algorithm (GA) exact
wavelet in condition of equal population because of factoring mutation and
crossover in PSO algorithm. SVM classifier with the extracted features in
gearbox shows very good results and its ability has been proved.
Which Learning Algorithms Can Generalize Identity-Based Rules to Novel Inputs?
Authors: Paul Tupper, Bobak Shahriari
Date: 2016-05-12 00:00:00

We propose a novel framework for the analysis of learning algorithms that
allows us to say when such algorithms can and cannot generalize certain
patterns from training data to test data. In particular we focus on situations
where the rule that must be learned concerns two components of a stimulus being
identical. We call such a basis for discrimination an identity-based rule.
Identity-based rules have proven to be difficult or impossible for certain
types of learning algorithms to acquire from limited datasets. This is in
contrast to human behaviour on similar tasks. Here we provide a framework for
rigorously establishing which learning algorithms will fail at generalizing
identity-based rules to novel stimuli. We use this framework to show that such
algorithms are unable to generalize identity-based rules to novel inputs unless
trained on virtually all possible inputs. We demonstrate these results
computationally with a multilayer feedforward neural network.
Generating Sentences from a Continuous Space
Authors: Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Jozefowicz, Samy Bengio
Date: 2015-11-19 00:00:00
Proceeding: CONLL 2016 8
Tasks: Language Modeling, Language Modelling, Sentence

The standard recurrent neural network language model (RNNLM) generates
sentences one word at a time and does not work from an explicit global sentence
representation. In this work, we introduce and study an RNN-based variational
autoencoder generative model that incorporates distributed latent
representations of entire sentences. This factorization allows it to explicitly
model holistic properties of sentences such as style, topic, and high-level
syntactic features. Samples from the prior over these sentence representations
remarkably produce diverse and well-formed sentences through simple
deterministic decoding. By examining paths through this latent space, we are
able to generate coherent novel sentences that interpolate between known
sentences. We present techniques for solving the difficult learning problem
presented by this model, demonstrate its effectiveness in imputing missing
words, explore many interesting properties of the model's latent sentence
space, and present negative results on the use of the model in language
modeling.
On the Convergent Properties of Word Embedding Methods
Authors: Yingtao Tian, Vivek Kulkarni, Bryan Perozzi, Steven Skiena
Date: 2016-05-12 00:00:00
Tasks: Word Embeddings

Do word embeddings converge to learn similar things over different
initializations? How repeatable are experiments with word embeddings? Are all
word embedding techniques equally reliable? In this paper we propose evaluating
methods for learning word representations by their consistency across
initializations. We propose a measure to quantify the similarity of the learned
word representations under this setting (where they are subject to different
random initializations). Our preliminary results illustrate that our metric not
only measures a intrinsic property of word embedding methods but also
correlates well with other evaluation metrics on downstream tasks. We believe
our methods are is useful in characterizing robustness -- an important property
to consider when developing new word embedding methods.
Competitive analysis of the top-K ranking problem
Authors: Xi Chen, Sivakanth Gopi, Jieming Mao, Jon Schneider
Date: 2016-05-12 00:00:00
Tasks: Recommendation Systems

Motivated by applications in recommender systems, web search, social choice
and crowdsourcing, we consider the problem of identifying the set of top $K$
items from noisy pairwise comparisons. In our setting, we are non-actively
given $r$ pairwise comparisons between each pair of $n$ items, where each
comparison has noise constrained by a very general noise model called the
strong stochastic transitivity (SST) model. We analyze the competitive ratio of
algorithms for the top-$K$ problem. In particular, we present a linear time
algorithm for the top-$K$ problem which has a competitive ratio of
$\tilde{O}(\sqrt{n})$; i.e. to solve any instance of top-$K$, our algorithm
needs at most $\tilde{O}(\sqrt{n})$ times as many samples needed as the best
possible algorithm for that instance (in contrast, all previous known
algorithms for the top-$K$ problem have competitive ratios of
$\tilde{\Omega}(n)$ or worse). We further show that this is tight: any
algorithm for the top-$K$ problem has competitive ratio at least
$\tilde{\Omega}(\sqrt{n})$.
Empirical Similarity for Absent Data Generation in Imbalanced Classification
Authors: Arash Pourhabib
Date: 2015-08-05 00:00:00
Tasks: Classification, General Classification, imbalanced classification, Synthetic Data Generation

When the training data in a two-class classification problem is overwhelmed
by one class, most classification techniques fail to correctly identify the
data points belonging to the underrepresented class. We propose
Similarity-based Imbalanced Classification (SBIC) that learns patterns in the
training data based on an empirical similarity function. To take the imbalanced
structure of the training data into account, SBIC utilizes the concept of
absent data, i.e. data from the minority class which can help better find the
boundary between the two classes. SBIC simultaneously optimizes the weights of
the empirical similarity function and finds the locations of absent data
points. As such, SBIC uses an embedded mechanism for synthetic data generation
which does not modify the training dataset, but alters the algorithm to suit
imbalanced datasets. Therefore, SBIC uses the ideas of both major schools of
thoughts in imbalanced classification: Like cost-sensitive approaches SBIC
operates on an algorithm level to handle imbalanced structures; and similar to
synthetic data generation approaches, it utilizes the properties of unobserved
data points from the minority class. The application of SBIC to imbalanced
datasets suggests it is comparable to, and in some cases outperforms, other
commonly used classification techniques for imbalanced datasets.
Why Artificial Intelligence Needs a Task Theory --- And What It Might Look Like
Authors: Kristinn R. Thórisson, Jordi Bieger, Thröstur Thorarensen, Jóna S. Sigurðardóttir, Bas R. Steunebrink
Date: 2016-04-15 00:00:00
Tasks: Board Games, Question Answering

The concept of "task" is at the core of artificial intelligence (AI): Tasks
are used for training and evaluating AI systems, which are built in order to
perform and automatize tasks we deem useful. In other fields of engineering
theoretical foundations allow thorough evaluation of designs by methodical
manipulation of well understood parameters with a known role and importance;
this allows an aeronautics engineer, for instance, to systematically assess the
effects of wind speed on an airplane's performance and stability. No framework
exists in AI that allows this kind of methodical manipulation: Performance
results on the few tasks in current use (cf. board games, question-answering)
cannot be easily compared, however similar or different. The issue is even more
acute with respect to artificial *general* intelligence systems, which must
handle unanticipated tasks whose specifics cannot be known beforehand. A *task
theory* would enable addressing tasks at the *class* level, bypassing their
specifics, providing the appropriate formalization and classification of tasks,
environments, and their parameters, resulting in more rigorous ways of
measuring, comparing, and evaluating intelligent behavior. Even modest
improvements in this direction would surpass the current ad-hoc nature of
machine learning and AI evaluation. Here we discuss the main elements of the
argument for a task theory and present an outline of what it might look like
for physical tasks.
Trainlets: Dictionary Learning in High Dimensions
Authors: Jeremias Sulam, Boaz Ophir, Michael Zibulevsky, Michael Elad
Date: 2016-01-31 00:00:00
Tasks: Dictionary Learning, Vocal Bursts Intensity Prediction

Sparse representations has shown to be a very powerful model for real world
signals, and has enabled the development of applications with notable
performance. Combined with the ability to learn a dictionary from signal
examples, sparsity-inspired algorithms are often achieving state-of-the-art
results in a wide variety of tasks. Yet, these methods have traditionally been
restricted to small dimensions mainly due to the computational constraints that
the dictionary learning problem entails. In the context of image processing,
this implies handling small image patches. In this work we show how to
efficiently handle bigger dimensions and go beyond the small patches in
sparsity-based signal and image processing methods. We build our approach based
on a new cropped wavelet decomposition, which enables a multi-scale analysis
with virtually no border effects. We then employ this as the base dictionary
within a double sparsity model to enable the training of adaptive dictionaries.
To cope with the increase of training data, while at the same time improving
the training performance, we present an Online Sparse Dictionary Learning
(OSDL) algorithm to train this model effectively, enabling it to handle
millions of examples. This work shows that dictionary learning can be up-scaled
to tackle a new level of signal dimensions, obtaining large adaptable atoms
that we call trainlets.
A New Manifold Distance Measure for Visual Object Categorization
Authors: Fengfu Li, Xiayuan Huang, Hong Qiao, Bo Zhang
Date: 2016-05-12 00:00:00
Tasks: Clustering, Object, Object Categorization, Object Recognition, SSIM

Manifold distances are very effective tools for visual object recognition.
However, most of the traditional manifold distances between images are based on
the pixel-level comparison and thus easily affected by image rotations and
translations. In this paper, we propose a new manifold distance to model the
dissimilarities between visual objects based on the Complex Wavelet Structural
Similarity (CW-SSIM) index. The proposed distance is more robust to rotations
and translations of images than the traditional manifold distance and the
CW-SSIM index based distance. In addition, the proposed distance is combined
with the $k$-medoids clustering method to derive a new clustering method for
visual object categorization. Experiments on Coil-20, Coil-100 and Olivetti
Face Databases show that the proposed distance measure is better for visual
object categorization than both the traditional manifold distances and the
CW-SSIM index based distances.
Context-dependent feature analysis with random forests
Authors: Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts
Date: 2016-05-12 00:00:00
Tasks: feature selection

In many cases, feature selection is often more complicated than identifying a
single subset of input variables that would together explain the output. There
may be interactions that depend on contextual information, i.e., variables that
reveal to be relevant only in some specific circumstances. In this setting, the
contribution of this paper is to extend the random forest variable importances
framework in order (i) to identify variables whose relevance is
context-dependent and (ii) to characterize as precisely as possible the effect
of contextual information on these variables. The usage and the relevance of
our framework for highlighting context-dependent variables is illustrated on
both artificial and real datasets.
Noisy Parallel Approximate Decoding for Conditional Recurrent Language Model
Authors: Kyunghyun Cho
Date: 2016-05-12 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, speech-recognition, Speech Recognition, Translation, Video Description

Recent advances in conditional recurrent language modelling have mainly
focused on network architectures (e.g., attention mechanism), learning
algorithms (e.g., scheduled sampling and sequence-level training) and novel
applications (e.g., image/video description generation, speech recognition,
etc.) On the other hand, we notice that decoding algorithms/strategies have not
been investigated as much, and it has become standard to use greedy or beam
search. In this paper, we propose a novel decoding strategy motivated by an
earlier observation that nonlinear hidden layers of a deep neural network
stretch the data manifold. The proposed strategy is embarrassingly
parallelizable without any communication overhead, while improving an existing
decoding algorithm. We extensively evaluate it with attention-based neural
machine translation on the task of En->Cz translation.
Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning
Authors: Yulia Tsvetkov, Sunayana Sitaram, Manaal Faruqui, Guillaume Lample, Patrick Littell, David Mortensen, Alan W. black, Lori Levin, Chris Dyer
Date: 2016-05-12 00:00:00
Proceeding: NAACL 2016 6
Tasks: Representation Learning

We introduce polyglot language models, recurrent neural network models
trained to predict symbol sequences in many different languages using shared
representations of symbols and conditioning on typological information about
the language to be predicted. We apply these to the problem of modeling phone
sequences---a domain in which universal symbol inventories and
cross-linguistically shared feature representations are a natural fit.
Intrinsic evaluation on held-out perplexity, qualitative analysis of the
learned representations, and extrinsic evaluation in two downstream
applications that make use of phonetic features show (i) that polyglot models
better generalize to held-out data than comparable monolingual models and (ii)
that polyglot phonetic feature representations are of higher quality than those
learned monolingually.
A Mid-level Video Representation based on Binary Descriptors: A Case Study for Pornography Detection
Authors: Carlos Caetano, Sandra Avila, William Robson Schwartz, Silvio Jamil F. Guimarães, Arnaldo de A. Araújo
Date: 2016-05-12 00:00:00
Tasks: Pornography Detection, Video Description

With the growing amount of inappropriate content on the Internet, such as
pornography, arises the need to detect and filter such material. The reason for
this is given by the fact that such content is often prohibited in certain
environments (e.g., schools and workplaces) or for certain publics (e.g.,
children). In recent years, many works have been mainly focused on detecting
pornographic images and videos based on visual content, particularly on the
detection of skin color. Although these approaches provide good results, they
generally have the disadvantage of a high false positive rate since not all
images with large areas of skin exposure are necessarily pornographic images,
such as people wearing swimsuits or images related to sports. Local feature
based approaches with Bag-of-Words models (BoW) have been successfully applied
to visual recognition tasks in the context of pornography detection. Even
though existing methods provide promising results, they use local feature
descriptors that require a high computational processing time yielding
high-dimensional vectors. In this work, we propose an approach for pornography
detection based on local binary feature extraction and BossaNova image
representation, a BoW model extension that preserves more richly the visual
information. Moreover, we propose two approaches for video description based on
the combination of mid-level representations namely BossaNova Video Descriptor
(BNVD) and BoW Video Descriptor (BoW-VD). The proposed techniques are
promising, achieving an accuracy of 92.40%, thus reducing the classification
error by 16% over the current state-of-the-art local features approach on the
Pornography dataset.
Direct Method for Training Feed-forward Neural Networks using Batch Extended Kalman Filter for Multi-Step-Ahead Predictions
Authors: Artem Chernodub
Date: 2016-05-12 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Prediction

This paper is dedicated to the long-term, or multi-step-ahead, time series
prediction problem. We propose a novel method for training feed-forward neural
networks, such as multilayer perceptrons, with tapped delay lines. Special
batch calculation of derivatives called Forecasted Propagation Through Time and
batch modification of the Extended Kalman Filter are introduced. Experiments
were carried out on well-known time series benchmarks, the Mackey-Glass chaotic
process and the Santa Fe Laser Data Series. Recurrent and feed-forward neural
networks were evaluated.
The SP theory of intelligence and the representation and processing of knowledge in the brain
Authors: J. Gerard Wolff
Date: 2016-04-19 00:00:00

The "SP theory of intelligence", with its realisation in the "SP computer
model", aims to simplify and integrate observations and concepts across
AI-related fields, with information compression as a unifying theme. This paper
describes how abstract structures and processes in the theory may be realised
in terms of neurons, their interconnections, and the transmission of signals
between neurons. This part of the SP theory -- "SP-neural" -- is a tentative
and partial model for the representation and processing of knowledge in the
brain. In the SP theory (apart from SP-neural), all kinds of knowledge are
represented with "patterns", where a pattern is an array of atomic symbols in
one or two dimensions. In SP-neural, the concept of a "pattern" is realised as
an array of neurons called a "pattern assembly", similar to Hebb's concept of a
"cell assembly" but with important differences. Central to the processing of
information in the SP system is the powerful concept of "multiple alignment",
borrowed and adapted from bioinformatics. Processes such as pattern
recognition, reasoning and problem solving are achieved via the building of
multiple alignments, while unsupervised learning -- significantly different
from the "Hebbian" kinds of learning -- is achieved by creating patterns from
sensory information and also by creating patterns from multiple alignments in
which there is a partial match between one pattern and another. Short-lived
neural structures equivalent to multiple alignments will be created via an
inter-play of excitatory and inhibitory neural signals. The paper discusses
several associated issues, with relevant empirical evidence.
Fast Graph-Based Object Segmentation for RGB-D Images
Authors: Giorgio Toscana, Stefano Rosa
Date: 2016-05-12 00:00:00
Tasks: Object, Robotic Grasping, Segmentation, Semantic Segmentation

Object segmentation is an important capability for robotic systems, in
particular for grasping. We present a graph- based approach for the
segmentation of simple objects from RGB-D images. We are interested in
segmenting objects with large variety in appearance, from lack of texture to
strong textures, for the task of robotic grasping. The algorithm does not rely
on image features or machine learning. We propose a modified Canny edge
detector for extracting robust edges by using depth information and two simple
cost functions for combining color and depth cues. The cost functions are used
to build an undirected graph, which is partitioned using the concept of
internal and external differences between graph regions. The partitioning is
fast with O(NlogN) complexity. We also discuss ways to deal with missing depth
information. We test the approach on different publicly available RGB-D object
datasets, such as the Rutgers APC RGB-D dataset and the RGB-D Object Dataset,
and compare the results with other existing methods.