pwc-archive/papers-with-abstracts huggingface dataset:
Self-Taught Convolutional Neural Networks for Short Text Clustering
Authors: Jiaming Xu, Peng Wang, Suncong Zheng, Guanhua Tian, Jun Zhao, Bo Xu
Date: 2017-01-01 00:00:00
Tasks: Clustering, Dimensionality Reduction, Short Text Clustering, Text Clustering, Word Embeddings

Short text clustering is a challenging problem due to its sparseness of text
representation. Here we propose a flexible Self-Taught Convolutional neural
network framework for Short Text Clustering (dubbed STC^2), which can flexibly
and successfully incorporate more useful semantic features and learn non-biased
deep text representation in an unsupervised manner. In our framework, the
original raw text features are firstly embedded into compact binary codes by
using one existing unsupervised dimensionality reduction methods. Then, word
embeddings are explored and fed into convolutional neural networks to learn
deep feature representations, meanwhile the output units are used to fit the
pre-trained binary codes in the training process. Finally, we get the optimal
clusters by employing K-means to cluster the learned representations. Extensive
experimental results demonstrate that the proposed framework is effective,
flexible and outperform several popular clustering methods when tested on three
public short text datasets.
Social Media Argumentation Mining: The Quest for Deliberateness in Raucousness
Authors: Jan Šnajder
Date: 2016-12-31 00:00:00
Tasks: Position

Argumentation mining from social media content has attracted increasing
attention. The task is both challenging and rewarding. The informal nature of
user-generated content makes the task dauntingly difficult. On the other hand,
the insights that could be gained by a large-scale analysis of social media
argumentation make it a very worthwhile task. In this position paper I discuss
the motivation for social media argumentation mining, as well as the tasks and
challenges involved.
Very Fast Kernel SVM under Budget Constraints
Authors: David Picard
Date: 2016-12-31 00:00:00

In this paper we propose a fast online Kernel SVM algorithm under tight
budget constraints. We propose to split the input space using LVQ and train a
Kernel SVM in each cluster. To allow for online training, we propose to limit
the size of the support vector set of each cluster using different strategies.
We show in the experiment that our algorithm is able to achieve high accuracy
while having a very high number of samples processed per second both in
training and in the evaluation.
Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learning
Authors: Amit Shaked, Lior Wolf
Date: 2016-12-31 00:00:00
Proceeding: CVPR 2017 7
Tasks: Stereo Matching, Stereo Matching Hand

We present an improved three-step pipeline for the stereo matching problem
and introduce multiple novelties at each stage. We propose a new highway
network architecture for computing the matching cost at each possible
disparity, based on multilevel weighted residual shortcuts, trained with a
hybrid loss that supports multilevel comparison of image patches. A novel
post-processing step is then introduced, which employs a second deep
convolutional neural network for pooling global information from multiple
disparities. This network outputs both the image disparity map, which replaces
the conventional "winner takes all" strategy, and a confidence in the
prediction. The confidence score is achieved by training the network with a new
technique that we call the reflective loss. Lastly, the learned confidence is
employed in order to better detect outliers in the refinement step. The
proposed pipeline achieves state of the art accuracy on the largest and most
competitive stereo benchmarks, and the learned confidence is shown to
outperform all existing alternatives.
EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras (Extended Abstract)
Authors: Helge Rhodin, Christian Richardt, Dan Casas, Eldar Insafutdinov, Mohammad Shafiei, Hans-Peter Seidel, Bernt Schiele, Christian Theobalt
Date: 2016-12-31 00:00:00
Tasks: Pose Estimation

Marker-based and marker-less optical skeletal motion-capture methods use an
outside-in arrangement of cameras placed around a scene, with viewpoints
converging on the center. They often create discomfort by possibly needed
marker suits, and their recording volume is severely restricted and often
constrained to indoor scenes with controlled backgrounds. We therefore propose
a new method for real-time, marker-less and egocentric motion capture which
estimates the full-body skeleton pose from a lightweight stereo pair of fisheye
cameras that are attached to a helmet or virtual-reality headset. It combines
the strength of a new generative pose estimation framework for fisheye views
with a ConvNet-based body-part detector trained on a new automatically
annotated and augmented dataset. Our inside-in method captures full-body motion
in general indoor and outdoor scenes, and also crowded scenes.
Simulated Tornado Optimization
Authors: S. Hossein Hosseini, Tohid Nouri, Afshin Ebrahimi, S. Ali Hosseini
Date: 2016-12-31 00:00:00

We propose a swarm-based optimization algorithm inspired by air currents of a
tornado. Two main air currents - spiral and updraft - are mimicked. Spiral
motion is designed for exploration of new search areas and updraft movements is
deployed for exploitation of a promising candidate solution. Assignment of just
one search direction to each particle at each iteration, leads to low
computational complexity of the proposed algorithm respect to the conventional
algorithms. Regardless of the step size parameters, the only parameter of the
proposed algorithm, called tornado diameter, can be efficiently adjusted by
randomization. Numerical results over six different benchmark cost functions
indicate comparable and, in some cases, better performance of the proposed
algorithm respect to some other metaheuristics.
Learning Weighted Association Rules in Human Phenotype Ontology
Authors: Pietro Hiram Guzzi, Giuseppe Agapito, Marianna Milano, Mario Cannataro
Date: 2016-12-31 00:00:00
Tasks: Specificity

The Human Phenotype Ontology (HPO) is a structured repository of concepts
(HPO Terms) that are associated to one or more diseases. The process of
association is referred to as annotation. The relevance and the specificity of
both HPO terms and annotations are evaluated by a measure defined as
Information Content (IC). The analysis of annotated data is thus an important
challenge for bioinformatics. There exist different approaches of analysis.
From those, the use of Association Rules (AR) may provide useful knowledge, and
it has been used in some applications, e.g. improving the quality of
annotations. Nevertheless classical association rules algorithms do not take
into account the source of annotation nor the importance yielding to the
generation of candidate rules with low IC. This paper presents HPO-Miner (Human
Phenotype Ontology-based Weighted Association Rules) a methodology for
extracting Weighted Association Rules. HPO-Miner can extract relevant rules
from a biological point of view. A case study on using of HPO-Miner on publicly
available HPO annotation datasets is used to demonstrate the effectiveness of
our methodology.
Unsupervised single-particle deep clustering via statistical manifold learning
Authors: Jiayi Wu, Yong-Bei Ma, Charles Congdon, Bevin Brett, Shuobing Chen, Qi Ouyang, Youdong Mao
Date: 2016-04-15 00:00:00
Tasks: 3D Reconstruction, Clustering, Deep Clustering, General Classification

Motivation: Structural heterogeneity in single-particle cryo-electron
microscopy (cryo-EM) data represents a major challenge for high-resolution
structure determination. Unsupervised classification may serve as the first
step in the assessment of structural heterogeneity. Traditional algorithms for
unsupervised classification, such as K-means clustering and maximum likelihood
optimization, may classify images into wrong classes with decreasing
signal-to-noise-ratio (SNR) in the image data, yet demand increased cost in
computation. Overcoming these limitations requires further development on
clustering algorithms for high-performance cryo-EM data analysis. Results: Here
we introduce a statistical manifold learning algorithm for unsupervised
single-particle deep clustering. We show that statistical manifold learning
improves classification accuracy by about 40% in the absence of input
references for lower SNR data. Applications to several experimental datasets
suggest that our deep clustering approach can detect subtle structural
difference among classes. Through code optimization over the Intel
high-performance computing (HPC) processors, our software implementation can
generate thousands of reference-free class averages within several hours from
hundreds of thousands of single-particle cryo-EM images, which allows
significant improvement in ab initio 3D reconstruction resolution and quality.
Our approach has been successfully applied in several structural determination
projects. We expect that it provides a powerful computational tool in analyzing
highly heterogeneous structural data and assisting in computational
purification of single-particle datasets for high-resolution reconstruction.
A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy
Authors: Yanan Zhu, Qi Ouyang, Youdong Mao
Date: 2016-05-18 00:00:00

Background: Single-particle cryo-electron microscopy (cryo-EM) has become a
popular tool for structural determination of biological macromolecular
complexes. High-resolution cryo-EM reconstruction often requires hundreds of
thousands of single-particle images. Particle extraction from experimental
micrographs thus can be laborious and presents a major practical bottleneck in
cryo-EM structural determination. Existing computational methods of particle
picking often use low-resolution templates as inputs for particle matching,
making it possible to cause reference-dependent bias. It is critical to develop
a highly efficient template-free method to automatically recognize particle
images from cryo-EM micrographs. Results: We developed a deep learning-based
algorithmic framework, DeepEM, for single-particle recognition from noisy
cryo-EM micrographs, enabling automated particle picking, selection and
verification in an integrated fashion. The kernel of DeepEM is built upon a
convolutional neural network (CNN) of eight layers, which can be recursively
trained to be highly "knowledgeable". Our approach exhibits improved
performance and high precision when tested on the standard KLH dataset.
Application of DeepEM to several challenging experimental cryo-EM datasets
demonstrates its capability in avoiding selection of un-wanted particles and
non-particles even when true particles contain fewer features. Conclusions: The
DeepEM method derived from a deep CNN allows automated particle extraction from
raw cryo-EM micrographs in the absence of templates, which demonstrated
improved performance, objectivity and accuracy. Application of this novel
approach is expected to free the labor involved in single-particle
verification, thus promoting the efficiency of cryo-EM data processing.
A POS Tagger for Code Mixed Indian Social Media Text - ICON-2016 NLP Tools Contest Entry from Surukam
Authors: Sree Harsha Ramesh, Raveena R Kumar
Date: 2016-12-31 00:00:00
Tasks: POS

Building Part-of-Speech (POS) taggers for code-mixed Indian languages is a
particularly challenging problem in computational linguistics due to a dearth
of accurately annotated training corpora. ICON, as part of its NLP tools
contest has organized this challenge as a shared task for the second
consecutive year to improve the state-of-the-art. This paper describes the POS
tagger built at Surukam to predict the coarse-grained and fine-grained POS tags
for three language pairs - Bengali-English, Telugu-English and Hindi-English,
with the text spanning three popular social media platforms - Facebook,
WhatsApp and Twitter. We employed Conditional Random Fields as the sequence
tagging algorithm and used a library called sklearn-crfsuite - a thin wrapper
around CRFsuite for training our model. Among the features we used include -
character n-grams, language information and patterns for emoji, number,
punctuation and web-address. Our submissions in the constrained
environment,i.e., without making any use of monolingual POS taggers or the
like, obtained an overall average F1-score of 76.45%, which is comparable to
the 2015 winning score of 76.79%.
Learning from Conditional Distributions via Dual Embeddings
Authors: Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song
Date: 2016-07-15 00:00:00
Tasks: Reinforcement Learning

Many machine learning tasks, such as learning with invariance and policy
evaluation in reinforcement learning, can be characterized as problems of
learning from conditional distributions. In such problems, each sample $x$
itself is associated with a conditional distribution $p(z|x)$ represented by
samples $\{z_i\}_{i=1}^M$, and the goal is to learn a function $f$ that links
these conditional distributions to target values $y$. These learning problems
become very challenging when we only have limited samples or in the extreme
case only one sample from each conditional distribution. Commonly used
approaches either assume that $z$ is independent of $x$, or require an
overwhelmingly large samples from each conditional distribution.
  To address these challenges, we propose a novel approach which employs a new
min-max reformulation of the learning from conditional distribution problem.
With such new reformulation, we only need to deal with the joint distribution
$p(z,x)$. We also design an efficient learning algorithm, Embedding-SGD, and
establish theoretical sample complexity for such problems. Finally, our
numerical experiments on both synthetic and real-world datasets show that the
proposed approach can significantly improve over the existing algorithms.
p-DLA: A Predictive System Model for Onshore Oil and Gas Pipeline Dataset Classification and Monitoring - Part 1
Authors: E. N. Osegi
Date: 2016-12-31 00:00:00
Tasks: Decision Making, General Classification

With the rise in militant activity and rogue behaviour in oil and gas regions
around the world, oil pipeline disturbances is on the increase leading to huge
losses to multinational operators and the countries where such facilities
exist. However, this situation can be averted if adequate predictive monitoring
schemes are put in place. We propose in the first part of this paper, an
artificial intelligence predictive monitoring system capable of predictive
classification and pattern recognition of pipeline datasets. The predictive
system is based on a highly sparse predictive Deviant Learning Algorithm
(p-DLA) designed to synthesize a sequence of memory predictive clusters for
eventual monitoring, control and decision making. The DLA (p-DLA) is compared
with a popular machine learning algorithm, the Long Short-Term Memory (LSTM)
which is based on a temporal version of the standard feed-forward
back-propagation trained artificial neural networks (ANNs). The results of
simulations study show impressive results and validates the sparse memory
predictive approach which favours the sub-synthesis of a highly compressed and
low dimensional knowledge discovery and information prediction scheme. It also
shows that the proposed new approach is competitive with a well-known and
proven AI approach such as the LSTM.
Non-Negative Matrix Factorization Test Cases
Authors: Connor Sell, Jeremy Kepner
Date: 2016-12-30 00:00:00
Tasks: Clustering

Non-negative matrix factorization (NMF) is a prob- lem with many
applications, ranging from facial recognition to document clustering. However,
due to the variety of algorithms that solve NMF, the randomness involved in
these algorithms, and the somewhat subjective nature of the problem, there is
no clear "correct answer" to any particular NMF problem, and as a result, it
can be hard to test new algorithms. This paper suggests some test cases for NMF
algorithms derived from matrices with enumerable exact non-negative
factorizations and perturbations of these matrices. Three algorithms using
widely divergent approaches to NMF all give similar solutions over these test
cases, suggesting that these test cases could be used as test cases for
implementations of these existing NMF algorithms as well as potentially new NMF
algorithms. This paper also describes how the proposed test cases could be used
in practice.
Digital Advertising Traffic Operation: Machine Learning for Process Discovery
Authors: Massimiliano Dal Mas
Date: 2016-12-30 00:00:00
Tasks: BIG-bench Machine Learning

In a Web Advertising Traffic Operation it's necessary to manage the
day-to-day trafficking, pacing and optimization of digital and paid social
campaigns. The data analyst on Traffic Operation can not only quickly provide
answers but also speaks the language of the Process Manager and visually
displays the discovered process problems. In order to solve a growing number of
complaints in the customer service process, the weaknesses in the process
itself must be identified and communicated to the department. With the help of
Process Mining for the CRM data it is possible to identify unwanted loops and
delays in the process. With this paper we propose a process discovery based on
Machine Learning technique to automatically discover variations and detect at
first glance what the problem is, and undertake corrective measures.
Counterfactual Prediction with Deep Instrumental Variables Networks
Authors: Jason Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy
Date: 2016-12-30 00:00:00
Tasks: counterfactual, Prediction

We are in the middle of a remarkable rise in the use and capability of
artificial intelligence. Much of this growth has been fueled by the success of
deep learning architectures: models that map from observables to outputs via
multiple layers of latent representations. These deep learning algorithms are
effective tools for unstructured prediction, and they can be combined in AI
systems to solve complex automated reasoning problems. This paper provides a
recipe for combining ML algorithms to solve for causal effects in the presence
of instrumental variables -- sources of treatment randomization that are
conditionally independent from the response. We show that a flexible IV
specification resolves into two prediction tasks that can be solved with deep
neural nets: a first-stage network for treatment prediction and a second-stage
network whose loss function involves integration over the conditional treatment
distribution. This Deep IV framework imposes some specific structure on the
stochastic gradient descent routine used for training, but it is general enough
that we can take advantage of off-the-shelf ML capabilities and avoid extensive
algorithm customization. We outline how to obtain out-of-sample causal
validation in order to avoid over-fit. We also introduce schemes for both
Bayesian and frequentist inference: the former via a novel adaptation of
dropout training, and the latter via a data splitting routine.
Fuzzy Constraints Linear Discriminant Analysis
Authors: Hamid Reza Hassanzadeh, Hadi Sadoghi Yazdi, Abedin Vahedian
Date: 2016-12-30 00:00:00
Tasks: General Classification

In this paper we introduce a fuzzy constraint linear discriminant analysis
(FC-LDA). The FC-LDA tries to minimize misclassification error based on
modified perceptron criterion that benefits handling the uncertainty near the
decision boundary by means of a fuzzy linear programming approach with fuzzy
resources. The method proposed has low computational complexity because of its
linear characteristics and the ability to deal with noisy data with different
degrees of tolerance. Obtained results verify the success of the algorithm when
dealing with different problems. Comparing FC-LDA and LDA shows superiority in
classification task.
When the map is better than the territory
Authors: Erik P Hoel
Date: 2016-12-30 00:00:00
Tasks: Informativeness

The causal structure of any system can be analyzed at a multitude of spatial
and temporal scales. It has long been thought that while higher scale (macro)
descriptions of causal structure may be useful to observers, they are at best a
compressed description and at worse leave out critical information. However,
recent research applying information theory to causal analysis has shown that
the causal structure of some systems can actually come into focus (be more
informative) at a macroscale (Hoel et al. 2013). That is, a macro model of a
system (a map) can be more informative than a fully detailed model of the
system (the territory). This has been called causal emergence. While causal
emergence may at first glance seem counterintuitive, this paper grounds the
phenomenon in a classic concept from information theory: Shannon's discovery of
the channel capacity. I argue that systems have a particular causal capacity,
and that different causal models of those systems take advantage of that
capacity to various degrees. For some systems, only macroscale causal models
use the full causal capacity. Such macroscale causal models can either be
coarse-grains, or may leave variables and states out of the model (exogenous)
in various ways, which can improve the model's efficacy and its informativeness
via the same mathematical principles of how error-correcting codes take
advantage of an information channel's capacity. As model choice increase, the
causal capacity of a system approaches the channel capacity. Ultimately, this
provides a general framework for understanding how the causal structure of some
systems cannot be fully captured by even the most detailed microscopic model.
PrASP Report
Authors: Matthias Nickles
Date: 2016-12-30 00:00:00
Tasks: Inductive logic programming

This technical report describes the usage, syntax, semantics and core
algorithms of the probabilistic inductive logic programming framework PrASP.
PrASP is a research software which integrates non-monotonic reasoning based on
Answer Set Programming (ASP), probabilistic inference and parameter learning.
In contrast to traditional approaches to Probabilistic (Inductive) Logic
Programming, our framework imposes only little restrictions on probabilistic
logic programs. In particular, PrASP allows for ASP as well as First-Order
Logic syntax, and for the annotation of formulas with point probabilities as
well as interval probabilities. A range of widely configurable inference
algorithms can be combined in a pipeline-like fashion, in order to cover a
variety of use cases.
Automatic Data Deformation Analysis on Evolving Folksonomy Driven Environment
Authors: Massimiliano Dal Mas
Date: 2016-12-30 00:00:00

The Folksodriven framework makes it possible for data scientists to define an
ontology environment where searching for buried patterns that have some kind of
predictive power to build predictive models more effectively. It accomplishes
this through an abstractions that isolate parameters of the predictive modeling
process searching for patterns and designing the feature set, too. To reflect
the evolving knowledge, this paper considers ontologies based on folksonomies
according to a new concept structure called "Folksodriven" to represent
folksonomies. So, the studies on the transformational regulation of the
Folksodriven tags are regarded to be important for adaptive folksonomies
classifications in an evolving environment used by Intelligent Systems to
represent the knowledge sharing. Folksodriven tags are used to categorize
salient data points so they can be fed to a machine-learning system and
"featurizing" the data.