pwc-archive/papers-with-abstracts huggingface dataset:
Fault Diagnosis of Helical Gear Box using Large Margin K-Nearest Neighbors Classifier using Sound Signals
Authors: M. Amarnath, S. Arunav, Hemantha Kumar, V. Sugumaran, G. S Raghvendra
Date: 2015-08-19 00:00:00
Tasks: Descriptive, Fault Diagnosis, General Classification

Gear drives are one of the most widely used transmission system in many
machinery. Sound signals of a rotating machine contain the dynamic information
about its health conditions. Not much information available in the literature
reporting suitability of sound signals for fault diagnosis applications.
Maximum numbers of literature are based on FFT (Fast Fourier Transform)
analysis and have its own limitations with non-stationary signals like the ones
from gears. In this paper, attempt has been made in using sound signals
acquired from gears in good and simulated faulty conditions for the purpose of
fault diagnosis through a machine learning approach. The descriptive
statistical features were extracted from the acquired sound signals and the
predominant features were selected using J48 decision tree technique. The
selected features were then used for classification using Large Margin
K-nearest neighbor approach. The paper also discusses the effect of various
parameters on classification accuracy.
Drawing and Analyzing Causal DAGs with DAGitty
Authors: Johannes Textor
Date: 2015-08-19 00:00:00
Tasks: Epidemiology, Sociology

DAGitty is a software for drawing and analyzing causal diagrams, also known
as directed acyclic graphs (DAGs). Functions include identification of minimal
sufficient adjustment sets for estimating causal effects, diagnosis of
insufficient or invalid adjustment via the identification of biasing paths,
identification of instrumental variables, and derivation of testable
implications. DAGitty is provided in the hope that it is useful for researchers
and students in Epidemiology, Sociology, Psychology, and other empirical
disciplines. The software should run in any web browser that supports modern
JavaScript, HTML, and SVG. This is the user manual for DAGitty version 2.3. The
manual is updated with every release of a new stable version. DAGitty is
available at dagitty.net.
Saliency maps on image hierarchies
Authors: Verónica Vilaplana
Date: 2015-08-19 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

In this paper we propose two saliency models for salient object segmentation
based on a hierarchical image segmentation, a tree-like structure that
represents regions at different scales from the details to the whole image
(e.g. gPb-UCM, BPT). The first model is based on a hierarchy of image
partitions. The saliency at each level is computed on a region basis, taking
into account the contrast between regions. The maps obtained for the different
partitions are then integrated into a final saliency map. The second model
directly works on the structure created by the segmentation algorithm,
computing saliency at each node and integrating these cues in a straightforward
manner into a single saliency map. We show that the proposed models produce
high quality saliency maps. Objective evaluation demonstrates that the two
methods achieve state-of-the-art performance in several benchmark datasets.
Learning to Predict Independent of Span
Authors: Hado van Hasselt, Richard S. Sutton
Date: 2015-08-19 00:00:00
Tasks: Reinforcement Learning

We consider how to learn multi-step predictions efficiently. Conventional
algorithms wait until observing actual outcomes before performing the
computations to update their predictions. If predictions are made at a high
rate or span over a large amount of time, substantial computation can be
required to store all relevant observations and to update all predictions when
the outcome is finally observed. We show that the exact same predictions can be
learned in a much more computationally congenial way, with uniform per-step
computation that does not depend on the span of the predictions. We apply this
idea to various settings of increasing generality, repeatedly adding desired
properties and each time deriving an equivalent span-independent algorithm for
the conventional algorithm that satisfies these desiderata. Interestingly,
along the way several known algorithmic constructs emerge spontaneously from
our derivations, including dutch eligibility traces, temporal difference
errors, and averaging. This allows us to link these constructs one-to-one to
the corresponding desiderata, unambiguously connecting the `how' to the `why'.
Each step, we make sure that the derived algorithm subsumes the previous
algorithms, thereby retaining their properties. Ultimately we arrive at a
single general temporal-difference algorithm that is applicable to the full
setting of reinforcement learning.
Proposal for the creation of a research facility for the development of the SP machine
Authors: J. Gerard Wolff, Vasile Palade
Date: 2015-08-19 00:00:00
Tasks: Information Retrieval, Medical Diagnosis, Retrieval

This is a proposal to create a research facility for the development of a
high-parallel version of the "SP machine", based on the "SP theory of
intelligence". We envisage that the new version of the SP machine will be an
open-source software virtual machine, derived from the existing "SP computer
model", and hosted on an existing high-performance computer. It will be a means
for researchers everywhere to explore what can be done with the system and to
create new versions of it. The SP system is a unique attempt to simplify and
integrate observations and concepts across artificial intelligence, mainstream
computing, mathematics, and human perception and cognition, with information
compression as a unifying theme. Potential benefits and applications include
helping to solve problems associated with big data; facilitating the
development of autonomous robots; unsupervised learning, natural language
processing, several kinds of reasoning, fuzzy pattern recognition at multiple
levels of abstraction, computer vision, best-match and semantic forms of
information retrieval, software engineering, medical diagnosis, simplification
of computing systems, and the seamless integration of diverse kinds of
knowledge and diverse aspects of intelligence. Additional motivations include
the potential of the SP system to help solve problems in defence, security, and
the detection and prevention of crime; potential in terms of economic, social,
environmental, and academic criteria, and in terms of publicity; and the
potential for international influence in research. The main elements of the
proposed facility are described, including support for the development of
"SP-neural", a neural version of the SP machine. The facility should be
permanent in the sense that it should be available for the foreseeable future,
and it should be designed to facilitate its use by researchers anywhere in the
world.
Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections
Authors: Jingwei Zhang, Aaron Gerow, Jaan Altosaar, James Evans, Richard Jean So
Date: 2015-08-19 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Topic Models

Weak topic correlation across document collections with different numbers of
topics in individual collections presents challenges for existing
cross-collection topic models. This paper introduces two probabilistic topic
models, Correlated LDA (C-LDA) and Correlated HDP (C-HDP). These address
problems that can arise when analyzing large, asymmetric, and potentially
weakly-related collections. Topic correlations in weakly-related collections
typically lie in the tail of the topic distribution, where they would be
overlooked by models unable to fit large numbers of topics. To efficiently
model this long tail for large-scale analysis, our models implement a parallel
sampling algorithm based on the Metropolis-Hastings and alias methods (Yuan et
al., 2015). The models are first evaluated on synthetic data, generated to
simulate various collection-level asymmetries. We then present a case study of
modeling over 300k documents in collections of sciences and humanities research
from JSTOR.
Introduction to Cross-Entropy Clustering The R Package CEC
Authors: Jacek Tabor, Przemysław Spurek, Konrad Kamieniecki, Marek Śmieja, Krzysztof Misztal
Date: 2015-08-19 00:00:00
Tasks: Clustering

The R Package CEC performs clustering based on the cross-entropy clustering
(CEC) method, which was recently developed with the use of information theory.
The main advantage of CEC is that it combines the speed and simplicity of
$k$-means with the ability to use various Gaussian mixture models and reduce
unnecessary clusters. In this work we present a practical tutorial to CEC based
on the R Package CEC. Functions are provided to encompass the whole process of
clustering.
Spatio-temporal Spike and Slab Priors for Multiple Measurement Vector Problems
Authors: Michael Riis Andersen, Ole Winther, Lars Kai Hansen
Date: 2015-08-19 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

We are interested in solving the multiple measurement vector (MMV) problem
for instances, where the underlying sparsity pattern exhibit spatio-temporal
structure motivated by the electroencephalogram (EEG) source localization
problem. We propose a probabilistic model that takes this structure into
account by generalizing the structured spike and slab prior and the associated
Expectation Propagation inference scheme. Based on numerical experiments, we
demonstrate the viability of the model and the approximate inference scheme.
Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification
Authors: Bokai Cao, Xiangnan Kong, Jingyuan Zhang, Philip S. Yu, Ann B. Ragin
Date: 2015-08-19 00:00:00
Tasks: Diagnostic, feature selection, General Classification, Graph Classification

Mining discriminative subgraph patterns from graph data has attracted great
interest in recent years. It has a wide variety of applications in disease
diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the
graph representation alone. However, in many real-world applications, the side
information is available along with the graph data. For example, for
neurological disorder identification, in addition to the brain networks derived
from neuroimaging data, hundreds of clinical, immunologic, serologic and
cognitive measures may also be documented for each subject. These measures
compose multiple side views encoding a tremendous amount of supplemental
information for diagnostic purposes, yet are often ignored. In this paper, we
study the problem of discriminative subgraph selection using multiple side
views and propose a novel solution to find an optimal set of subgraph features
for graph classification by exploring a plurality of side views. We derive a
feature evaluation criterion, named gSide, to estimate the usefulness of
subgraph patterns based upon side views. Then we develop a branch-and-bound
algorithm, called gMSV, to efficiently search for optimal subgraph features by
integrating the subgraph mining process and the procedure of discriminative
feature selection. Empirical studies on graph classification tasks for
neurological disorders using brain networks demonstrate that subgraph patterns
selected by the multi-side-view guided subgraph selection approach can
effectively boost graph classification performances and are relevant to disease
diagnosis.
Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images
Authors: Alexander Krull, Eric Brachmann, Frank Michel, Michael Ying Yang, Stefan Gumhold, Carsten Rother
Date: 2015-08-19 00:00:00
Proceeding: ICCV 2015 12
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, Object, Pose Estimation

Analysis-by-synthesis has been a successful approach for many tasks in
computer vision, such as 6D pose estimation of an object in an RGB-D image
which is the topic of this work. The idea is to compare the observation with
the output of a forward process, such as a rendered image of the object of
interest in a particular pose. Due to occlusion or complicated sensor noise, it
can be difficult to perform this comparison in a meaningful way. We propose an
approach that "learns to compare", while taking these difficulties into
account. This is done by describing the posterior density of a particular
object pose with a convolutional neural network (CNN) that compares an observed
and rendered image. The network is trained with the maximum likelihood
paradigm. We observe empirically that the CNN does not specialize to the
geometry or appearance of specific objects, and it can be used with objects of
vastly different shapes and appearances, and in different backgrounds. Compared
to state-of-the-art, we demonstrate a significant improvement on two different
datasets which include a total of eleven objects, cluttered background, and
heavy occlusion.
Traversing Knowledge Graphs in Vector Space
Authors: Kelvin Guu, John Miller, Percy Liang
Date: 2015-06-03 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Knowledge Base Completion, Knowledge Graphs

Path queries on a knowledge graph can be used to answer compositional
questions such as "What languages are spoken by people living in Lisbon?".
However, knowledge graphs often have missing facts (edges) which disrupts path
queries. Recent models for knowledge base completion impute missing facts by
embedding knowledge graphs in vector spaces. We show that these models can be
recursively applied to answer path queries, but that they suffer from cascading
errors. This motivates a new "compositional" training objective, which
dramatically improves all models' ability to answer path queries, in some cases
more than doubling accuracy. On a standard knowledge base completion task, we
also demonstrate that compositional training acts as a novel form of structural
regularization, reliably improving performance across all base models (reducing
errors by up to 43%) and achieving new state-of-the-art results.
A Survey of Current Datasets for Vision and Language Research
Authors: Francis Ferraro, Nasrin Mostafazadeh, Ting-Hao, Huang, Lucy Vanderwende, Jacob Devlin, Michel Galley, Margaret Mitchell
Date: 2015-06-23 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Survey

Integrating vision and language has long been a dream in work on artificial intelligence (AI). In the past two years, we have witnessed an explosion of work that brings together vision and language from images to videos and beyond. The available corpora have played a crucial role in advancing this area of research. In this paper, we propose a set of quality metrics for evaluating and analyzing the vision & language datasets and categorize them accordingly. Our analyses show that the most recent datasets have been using more complex language and more abstract concepts, however, there are different strengths and weaknesses in each.
Recognizing Extended Spatiotemporal Expressions by Actively Trained Average Perceptron Ensembles
Authors: Wei Zhang, Yang Yu, Osho Gupta, Judith Gelernter
Date: 2015-08-19 00:00:00
Tasks: Active Learning, Ensemble Learning, Sentence

Precise geocoding and time normalization for text requires that location and
time phrases be identified. Many state-of-the-art geoparsers and temporal
parsers suffer from low recall. Categories commonly missed by parsers are:
nouns used in a non- spatiotemporal sense, adjectival and adverbial phrases,
prepositional phrases, and numerical phrases. We collected and annotated data
set by querying commercial web searches API with such spatiotemporal
expressions as were missed by state-of-the- art parsers. Due to the high cost
of sentence annotation, active learning was used to label training data, and a
new strategy was designed to better select training examples to reduce labeling
cost. For the learning algorithm, we applied an average perceptron trained
Featurized Hidden Markov Model (FHMM). Five FHMM instances were used to create
an ensemble, with the output phrase selected by voting. Our ensemble model was
tested on a range of sequential labeling tasks, and has shown competitive
performance. Our contributions include (1) an new dataset annotated with named
entities and expanded spatiotemporal expressions; (2) a comparison of inference
algorithms for ensemble models showing the superior accuracy of Belief
Propagation over Viterbi Decoding; (3) a new example re-weighting method for
active ensemble learning that 'memorizes' the latest examples trained; (4) a
spatiotemporal parser that jointly recognizes expanded spatiotemporal
expressions as well as named entities.
Exploring Metaphorical Senses and Word Representations for Identifying Metonyms
Authors: Wei Zhang, Judith Gelernter
Date: 2015-08-19 00:00:00

A metonym is a word with a figurative meaning, similar to a metaphor. Because
metonyms are closely related to metaphors, we apply features that are used
successfully for metaphor recognition to the task of detecting metonyms. On the
ACL SemEval 2007 Task 8 data with gold standard metonym annotations, our system
achieved 86.45% accuracy on the location metonyms. Our code can be found on
GitHub.
A Dictionary Learning Approach for Factorial Gaussian Models
Authors: Y. Cem Subakan, Johannes Traa, Paris Smaragdis, Noah Stein
Date: 2015-08-18 00:00:00
Tasks: Dictionary Learning, parameter estimation

In this paper, we develop a parameter estimation method for factorially
parametrized models such as Factorial Gaussian Mixture Model and Factorial
Hidden Markov Model. Our contributions are two-fold. First, we show that the
emission matrix of the standard Factorial Model is unidentifiable even if the
true assignment matrix is known. Secondly, we address the issue of
identifiability by making a one component sharing assumption and derive a
parameter learning algorithm for this case. Our approach is based on a
dictionary learning problem of the form $X = O R$, where the goal is to learn
the dictionary $O$ given the data matrix $X$. We argue that due to the specific
structure of the activation matrix $R$ in the shared component factorial
mixture model, and an incoherence assumption on the shared component, it is
possible to extract the columns of the $O$ matrix without the need for
alternating between the estimation of $O$ and $R$.
Robust Subspace Clustering via Smoothed Rank Approximation
Authors: Zhao Kang, Chong Peng, Qiang Cheng
Date: 2015-08-18 00:00:00
Tasks: Clustering, Face Clustering, Motion Segmentation

Matrix rank minimizing subject to affine constraints arises in many
application areas, ranging from signal processing to machine learning. Nuclear
norm is a convex relaxation for this problem which can recover the rank exactly
under some restricted and theoretically interesting conditions. However, for
many real-world applications, nuclear norm approximation to the rank function
can only produce a result far from the optimum. To seek a solution of higher
accuracy than the nuclear norm, in this paper, we propose a rank approximation
based on Logarithm-Determinant. We consider using this rank approximation for
subspace clustering application. Our framework can model different kinds of
errors and noise. Effective optimization strategy is developed with theoretical
guarantee to converge to a stationary point. The proposed method gives
promising results on face clustering and motion segmentation tasks compared to
the state-of-the-art subspace clustering algorithms.
Hash Function Learning via Codewords
Authors: Yinjie Huang, Michael Georgiopoulos, Georgios C. Anagnostopoulos
Date: 2015-08-13 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

In this paper we introduce a novel hash learning framework that has two main
distinguishing features, when compared to past approaches. First, it utilizes
codewords in the Hamming space as ancillary means to accomplish its hash
learning task. These codewords, which are inferred from the data, attempt to
capture similarity aspects of the data's hash codes. Secondly and more
importantly, the same framework is capable of addressing supervised,
unsupervised and, even, semi-supervised hash learning tasks in a natural
manner. A series of comparative experiments focused on content-based image
retrieval highlights its performance advantages.
A Deep Pyramid Deformable Part Model for Face Detection
Authors: Rajeev Ranjan, Vishal M. Patel, Rama Chellappa
Date: 2015-08-18 00:00:00
Tasks: Face Detection, Robust Face Recognition

We present a face detection algorithm based on Deformable Part Models and
deep pyramidal features. The proposed method called DP2MFD is able to detect
faces of various sizes and poses in unconstrained conditions. It reduces the
gap in training and testing of DPM on deep features by adding a normalization
layer to the deep convolutional neural network (CNN). Extensive experiments on
four publicly available unconstrained face detection datasets show that our
method is able to capture the meaningful structure of faces and performs
significantly better than many competitive face detection algorithms.
Sparse Auto-Regressive: Robust Estimation of AR Parameters
Authors: Mohsen Joneidi
Date: 2013-06-14 00:00:00
Tasks: regression, Time Series, Time Series Analysis

In this paper I present a new approach for regression of time series using
their own samples. This is a celebrated problem known as Auto-Regression.
Dealing with outlier or missed samples in a time series makes the problem of
estimation difficult, so it should be robust against them. Moreover for coding
purposes I will show that it is desired the residual of auto-regression be
sparse. To these aims, I first assume a multivariate Gaussian prior on the
residual and then obtain the estimation. Two simple simulations have been done
on spectrum estimation and speech coding.
ESDF: Ensemble Selection using Diversity and Frequency
Authors: Shouvick Mondal, Arko Banerjee
Date: 2015-08-18 00:00:00
Tasks: Clustering, Diversity

Recently ensemble selection for consensus clustering has emerged as a
research problem in Machine Intelligence. Normally consensus clustering
algorithms take into account the entire ensemble of clustering, where there is
a tendency of generating a very large size ensemble before computing its
consensus. One can avoid considering the entire ensemble and can judiciously
select few partitions in the ensemble without compromising on the quality of
the consensus. This may result in an efficient consensus computation technique
and may save unnecessary computational overheads. The ensemble selection
problem addresses this issue of consensus clustering. In this paper, we propose
an efficient method of ensemble selection for a large ensemble. We prioritize
the partitions in the ensemble based on diversity and frequency. Our method
selects top K of the partitions in order of priority, where K is decided by the
user. We observe that considering jointly the diversity and frequency helps in
identifying few representative partitions whose consensus is qualitatively
better than the consensus of the entire ensemble. Experimental analysis on a
large number of datasets shows our method gives better results than earlier
ensemble selection methods.