pwc-archive/papers-with-abstracts huggingface dataset:
Determining Health Utilities through Data Mining of Social Media
Authors: Christopher Thompson, Josh Introne, Clint Young
Date: 2016-08-13 00:00:00

'Health utilities' measure patient preferences for perfect health compared to
specific unhealthy states, such as asthma, a fractured hip, or colon cancer.
When integrated over time, these estimations are called quality adjusted life
years (QALYs). Until now, characterizing health utilities (HUs) required
detailed patient interviews or written surveys. While reliable and specific,
this data remained costly due to efforts to locate, enlist and coordinate
participants. Thus the scope, context and temporality of diseases examined has
remained limited.
  Now that more than a billion people use social media, we propose a novel
strategy: use natural language processing to analyze public online
conversations for signals of the severity of medical conditions and correlate
these to known HUs using machine learning. In this work, we filter a dataset
that originally contained 2 billion tweets for relevant content on 60 diseases.
Using this data, our algorithm successfully distinguished mild from severe
diseases, which had previously been categorized only by traditional techniques.
This represents progress towards two related applications: first, predicting
HUs where such information is nonexistent; and second, (where rich HU data
already exists) estimating temporal or geographic patterns of disease severity
through data mining.
Human Pose Estimation from Depth Images via Inference Embedded Multi-task Learning
Authors: Keze Wang, Shengfu Zhai, Hui Cheng, Xiaodan Liang, Liang Lin
Date: 2016-08-13 00:00:00
Tasks: Multi-Task Learning, Pose Estimation, Pose Prediction

Human pose estimation (i.e., locating the body parts / joints of a person) is
a fundamental problem in human-computer interaction and multimedia
applications. Significant progress has been made based on the development of
depth sensors, i.e., accessible human pose prediction from still depth images
[32]. However, most of the existing approaches to this problem involve several
components/models that are independently designed and optimized, leading to
suboptimal performances. In this paper, we propose a novel inference-embedded
multi-task learning framework for predicting human pose from still depth
images, which is implemented with a deep architecture of neural networks.
Specifically, we handle two cascaded tasks: i) generating the heat (confidence)
maps of body parts via a fully convolutional network (FCN); ii) seeking the
optimal configuration of body parts based on the detected body part proposals
via an inference built-in MatchNet [10], which measures the appearance and
geometric kinematic compatibility of body parts and embodies the dynamic
programming inference as an extra network layer. These two tasks are jointly
optimized. Our extensive experiments show that the proposed deep model
significantly improves the accuracy of human pose estimation over other several
state-of-the-art methods or SDKs. We also release a large-scale dataset for
comparison, which includes 100K depth images under challenging scenarios.
When was that made?
Authors: Sirion Vittayakorn, Alexander C. Berg, Tamara L. Berg
Date: 2016-08-12 00:00:00
Tasks: Retrieval

In this paper, we explore deep learning methods for estimating when objects
were made. Automatic methods for this task could potentially be useful for
historians, collectors, or any individual interested in estimating when their
artifact was created. Direct applications include large-scale data organization
or retrieval. Toward this goal, we utilize features from existing deep networks
and also fine-tune new networks for temporal estimation. In addition, we create
two new datasets of 67,771 dated clothing items from Flickr and museum
collections. Our method outperforms both a color-based baseline and previous
state of the art methods for temporal estimation. We also provide several
analyses of what our networks have learned, and demonstrate applications to
identifying temporal inspiration in fashion collections.
Temporal Registration in In-Utero Volumetric MRI Time Series
Authors: Ruizhi Liao, Esra Turk, Miaomiao Zhang, Jie Luo, Ellen Grant, Elfar Adalsteinsson, Polina Golland
Date: 2016-08-12 00:00:00
Tasks: Time Series, Time Series Analysis

We present a robust method to correct for motion and deformations for
in-utero volumetric MRI time series. Spatio-temporal analysis of dynamic MRI
requires robust alignment across time in the presence of substantial and
unpredictable motion. We make a Markov assumption on the nature of deformations
to take advantage of the temporal structure in the image data. Forward message
passing in the corresponding hidden Markov model (HMM) yields an estimation
algorithm that only has to account for relatively small motion between
consecutive frames. We demonstrate the utility of the temporal model by showing
that its use improves the accuracy of the segmentation propagation through
temporal registration. Our results suggest that the proposed model captures
accurately the temporal dynamics of deformations in in-utero MRI time series.
Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks
Authors: Dat Tien Nguyen, Kamela Ali Al Mannai, Shafiq Joty, Hassan Sajjad, Muhammad Imran, Prasenjit Mitra
Date: 2016-08-12 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Feature Engineering, General Classification

The role of social media, in particular microblogging platforms such as
Twitter, as a conduit for actionable and tactical information during disasters
is increasingly acknowledged. However, time-critical analysis of big crisis
data on social media streams brings challenges to machine learning techniques,
especially the ones that use supervised learning. The Scarcity of labeled data,
particularly in the early hours of a crisis, delays the machine learning
process. The current state-of-the-art classification methods require a
significant amount of labeled data specific to a particular event for training
plus a lot of feature engineering to achieve best results. In this work, we
introduce neural network based classification methods for binary and
multi-class tweet classification task. We show that neural network based models
do not require any feature engineering and perform better than state-of-the-art
methods. In the early hours of a disaster when no labeled data is available,
our proposed method makes the best use of the out-of-event data and achieves
good results.
A Convolutional Neural Network Neutrino Event Classifier
Authors: A. Aurisano, A. Radovic, D. Rocco, A. Himmel, M. D. Messier, E. Niner, G. Pawloski, F. Psihas, A. Sousa, P. Vahle
Date: 2016-04-05 00:00:00

Convolutional neural networks (CNNs) have been widely applied in the computer
vision community to solve complex problems in image recognition and analysis.
We describe an application of the CNN technology to the problem of identifying
particle interactions in sampling calorimeters used commonly in high energy
physics and high energy neutrino physics in particular. Following a discussion
of the core concepts of CNNs and recent innovations in CNN architectures
related to the field of deep learning, we outline a specific application to the
NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network)
identifies neutrino interactions based on their topology without the need for
detailed reconstruction and outperforms algorithms currently in use by the NOvA
collaboration.
Sparse Estimation of Multivariate Poisson Log-Normal Models from Count Data
Authors: Hao Wu, Xinwei Deng, Naren Ramakrishnan
Date: 2016-02-22 00:00:00
Tasks: regression

Modeling data with multivariate count responses is a challenging problem due
to the discrete nature of the responses. Existing methods for univariate count
responses cannot be easily extended to the multivariate case since the
dependency among multiple responses needs to be properly accommodated. In this
paper, we propose a multivariate Poisson log-normal regression model for
multivariate data with count responses. By simultaneously estimating the
regression coefficients and inverse covariance matrix over the latent variables
with an efficient Monte Carlo EM algorithm, the proposed regression model takes
advantages of association among multiple count responses to improve the model
prediction performance. Simulation studies and applications to real world data
are conducted to systematically evaluate the performance of the proposed method
in comparison with conventional methods.
gvnn: Neural Network Library for Geometric Computer Vision
Authors: Ankur Handa, Michael Bloesch, Viorica Patraucean, Simon Stent, John McCormac, Andrew Davison
Date: 2016-07-25 00:00:00
Tasks: Image Reconstruction, Visual Odometry

We introduce gvnn, a neural network library in Torch aimed towards bridging
the gap between classic geometric computer vision and deep learning. Inspired
by the recent success of Spatial Transformer Networks, we propose several new
layers which are often used as parametric transformations on the data in
geometric computer vision. These layers can be inserted within a neural network
much in the spirit of the original spatial transformers and allow
backpropagation to enable end-to-end learning of a network involving any domain
knowledge in geometric computer vision. This opens up applications in learning
invariance to 3D geometric transformation for place recognition, end-to-end
visual odometry, depth estimation and unsupervised learning through warping
with a parametric transformation for image reconstruction error.
COREALMLIB: An ALM Library Translated from the Component Library
Authors: Daniela Inclezan
Date: 2016-08-06 00:00:00
Tasks: Natural Language Understanding, Translation

This paper presents COREALMLIB, an ALM library of commonsense knowledge about
dynamic domains. The library was obtained by translating part of the COMPONENT
LIBRARY (CLIB) into the modular action language ALM. CLIB consists of general
reusable and composable commonsense concepts, selected based on a thorough
study of ontological and lexical resources. Our translation targets CLIB states
(i.e., fluents) and actions. The resulting ALM library contains the
descriptions of 123 action classes grouped into 43 reusable modules that are
organized into a hierarchy. It is made available online and of interest to
researchers in the action language, answer-set programming, and natural
language understanding communities. We believe that our translation has two
main advantages over its CLIB counterpart: (i) it specifies axioms about
actions in a more elaboration tolerant and readable way, and (ii) it can be
seamlessly integrated with ASP reasoning algorithms (e.g., for planning and
postdiction). In contrast, axioms are described in CLIB using STRIPS-like
operators, and CLIB's inference engine cannot handle planning nor postdiction.
Under consideration for publication in TPLP.
Traversing Environments Using Possibility Graphs for Humanoid Robots
Authors: Michael X. Grey, Aaron D. Ames, C. Karen Liu
Date: 2016-08-12 00:00:00
Tasks: Motion Planning

Locomotion for legged robots poses considerable challenges when confronted by
obstacles and adverse environments. Footstep planners are typically only
designed for one mode of locomotion, but traversing unfavorable environments
may require several forms of locomotion to be sequenced together, such as
walking, crawling, and jumping. Multi-modal motion planners can be used to
address some of these problems, but existing implementations tend to be
time-consuming and are limited to quasi-static actions. This paper presents a
motion planning method to traverse complex environments using multiple
categories of actions. We introduce the concept of the "Possibility Graph",
which uses high-level approximations of constraint manifolds to rapidly explore
the "possibility" of actions, thereby allowing lower-level single-action motion
planners to be utilized more efficiently. We show that the Possibility Graph
can quickly find paths through several different challenging environments which
require various combinations of actions in order to traverse.
Does quantification without adjustments work?
Authors: Dirk Tasche
Date: 2016-02-28 00:00:00
Tasks: Binary Quantification, General Classification

Classification is the task of predicting the class labels of objects based on
the observation of their features. In contrast, quantification has been defined
as the task of determining the prevalences of the different sorts of class
labels in a target dataset. The simplest approach to quantification is Classify
& Count where a classifier is optimised for classification on a training set
and applied to the target dataset for the prediction of class labels. In the
case of binary quantification, the number of predicted positive labels is then
used as an estimate of the prevalence of the positive class in the target
dataset. Since the performance of Classify & Count for quantification is known
to be inferior its results typically are subject to adjustments. However, some
researchers recently have suggested that Classify & Count might actually work
without adjustments if it is based on a classifer that was specifically trained
for quantification. We discuss the theoretical foundation for this claim and
explore its potential and limitations with a numerical example based on the
binormal model with equal variances. In order to identify an optimal quantifier
in the binormal setting, we introduce the concept of local Bayes optimality. As
a side remark, we present a complete proof of a theorem by Ye et al. (2012).
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
Authors: S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, Geoffrey E. Hinton
Date: 2016-03-28 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Scene Understanding

We present a framework for efficient inference in structured image models
that explicitly reason about objects. We achieve this by performing
probabilistic inference using a recurrent neural network that attends to scene
elements and processes them one at a time. Crucially, the model itself learns
to choose the appropriate number of inference steps. We use this scheme to
learn to perform inference in partially specified 2D models (variable-sized
variational auto-encoders) and fully specified 3D models (probabilistic
renderers). We show that such models learn to identify multiple objects -
counting, locating and classifying the elements of a scene - without any
supervision, e.g., decomposing 3D images with various numbers of objects in a
single forward pass of a neural network. We further show that the networks
produce accurate inferences when compared to supervised counterparts, and that
their structure leads to improved generalization.
On Minimal Accuracy Algorithm Selection in Computer Vision and Intelligent Systems
Authors: Martin Lukac, Kamila Abdiyeva, Michitaka Kameyama
Date: 2016-08-12 00:00:00

In this paper we discuss certain theoretical properties of algorithm
selection approach to image processing and to intelligent system in general. We
analyze the theoretical limits of algorithm selection with respect to the
algorithm selection accuracy. We show the theoretical formulation of a crisp
bound on the algorithm selector precision guaranteeing to always obtain better
than the best available algorithm result.
One-class classifiers based on entropic spanning graphs
Authors: Lorenzo Livi, Cesare Alippi
Date: 2016-04-08 00:00:00
Tasks: One-class classifier

One-class classifiers offer valuable tools to assess the presence of outliers
in data. In this paper, we propose a design methodology for one-class
classifiers based on entropic spanning graphs. Our approach takes into account
the possibility to process also non-numeric data by means of an embedding
procedure. The spanning graph is learned on the embedded input data and the
outcoming partition of vertices defines the classifier. The final partition is
derived by exploiting a criterion based on mutual information minimization.
Here, we compute the mutual information by using a convenient formulation
provided in terms of the $\alpha$-Jensen difference. Once training is
completed, in order to associate a confidence level with the classifier
decision, a graph-based fuzzy model is constructed. The fuzzification process
is based only on topological information of the vertices of the entropic
spanning graph. As such, the proposed one-class classifier is suitable also for
data characterized by complex geometric structures. We provide experiments on
well-known benchmarks containing both feature vectors and labeled graphs. In
addition, we apply the method to the protein solubility recognition problem by
considering several representations for the input samples. Experimental results
demonstrate the effectiveness and versatility of the proposed method with
respect to other state-of-the-art approaches.
Perceptual Reward Functions
Authors: Ashley Edwards, Charles Isbell, Atsuo Takanishi
Date: 2016-08-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning problems are often described through rewards that
indicate if an agent has completed some task. This specification can yield
desirable behavior, however many problems are difficult to specify in this
manner, as one often needs to know the proper configuration for the agent. When
humans are learning to solve tasks, we often learn from visual instructions
composed of images or videos. Such representations motivate our development of
Perceptual Reward Functions, which provide a mechanism for creating visual task
descriptions. We show that this approach allows an agent to learn from rewards
that are based on raw pixels rather than internal parameters.
DeepDiary: Automatic Caption Generation for Lifelogging Image Streams
Authors: Chenyou Fan, David J. Crandall
Date: 2016-08-12 00:00:00
Tasks: Caption Generation, Image Captioning, Image Retrieval, Retrieval

Lifelogging cameras capture everyday life from a first-person perspective,
but generate so much data that it is hard for users to browse and organize
their image collections effectively. In this paper, we propose to use automatic
image captioning algorithms to generate textual representations of these
collections. We develop and explore novel techniques based on deep learning to
generate captions for both individual images and image streams, using temporal
consistency constraints to create summaries that are both more compact and less
noisy. We evaluate our techniques with quantitative and qualitative results,
and apply captioning to an image retrieval application for finding potentially
private images. Our results suggest that our automatic captioning algorithms,
while imperfect, may work well enough to help users manage lifelogging photo
collections.
Content-based image retrieval tutorial
Authors: Joani Mitro
Date: 2016-08-12 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Information Retrieval, Retrieval

This paper functions as a tutorial for individuals interested to enter the
field of information retrieval but wouldn't know where to begin from. It
describes two fundamental yet efficient image retrieval techniques, the first
being k - nearest neighbors (knn) and the second support vector machines(svm).
The goal is to provide the reader with both the theoretical and practical
aspects in order to acquire a better understanding. Along with this tutorial we
have also developed the equivalent software1 using the MATLAB environment in
order to illustrate the techniques, so that the reader can have a hands-on
experience.
Redefining part-of-speech classes with distributional semantic models
Authors: Andrey Kutuzov, Erik Velldal, Lilja Øvrelid
Date: 2016-08-12 00:00:00
Proceeding: CONLL 2016 8
Tasks: POS, TAG, Word Embeddings

This paper studies how word embeddings trained on the British National Corpus
interact with part of speech boundaries. Our work targets the Universal PoS tag
set, which is currently actively being used for annotation of a range of
languages. We experiment with training classifiers for predicting PoS tags for
words based on their embeddings. The results show that the information about
PoS affiliation contained in the distributional vectors allows us to discover
groups of words with distributional patterns that differ from other words of
the same part of speech.
  This data often reveals hidden inconsistencies of the annotation process or
guidelines. At the same time, it supports the notion of `soft' or `graded' part
of speech affiliations. Finally, we show that information about PoS is
distributed among dozens of vector components, not limited to only one or two
features.
Compositional Distributional Cognition
Authors: Yaared Al-Mehairi, Bob Coecke, Martha Lewis
Date: 2016-08-12 00:00:00
Tasks: Logical Reasoning

We accommodate the Integrated Connectionist/Symbolic Architecture (ICS) of
[32] within the categorical compositional semantics (CatCo) of [13], forming a
model of categorical compositional cognition (CatCog). This resolves intrinsic
problems with ICS such as the fact that representations inhabit an unbounded
space and that sentences with differing tree structures cannot be directly
compared. We do so in a way that makes the most of the grammatical structure
available, in contrast to strategies like circular convolution. Using the CatCo
model also allows us to make use of tools developed for CatCo such as the
representation of ambiguity and logical reasoning via density matrices,
structural meanings for words such as relative pronouns, and addressing over-
and under-extension, all of which are present in cognitive processes. Moreover
the CatCog framework is sufficiently flexible to allow for entirely different
representations of meaning, such as conceptual spaces. Interestingly, since the
CatCo model was largely inspired by categorical quantum mechanics, so is
CatCog.
Mini-Batch Spectral Clustering
Authors: Yufei Han, Maurizio Filippone
Date: 2016-07-07 00:00:00
Tasks: Clustering

The cost of computing the spectrum of Laplacian matrices hinders the
application of spectral clustering to large data sets. While approximations
recover computational tractability, they can potentially affect clustering
performance. This paper proposes a practical approach to learn spectral
clustering based on adaptive stochastic gradient optimization. Crucially, the
proposed approach recovers the exact spectrum of Laplacian matrices in the
limit of the iterations, and the cost of each iteration is linear in the number
of samples. Extensive experimental validation on data sets with up to half a
million samples demonstrate its scalability and its ability to outperform
state-of-the-art approximate methods to learn spectral clustering for a given
computational budget.
Measuring the State of the Art of Automated Pathway Curation Using Graph Algorithms - A Case Study of the mTOR Pathway
Authors: Michael Spranger, Sucheendra K. Palaniappan, Samik Ghosh
Date: 2016-08-12 00:00:00
Proceeding: WS 2016 8

This paper evaluates the difference between human pathway curation and
current NLP systems. We propose graph analysis methods for quantifying the gap
between human curated pathway maps and the output of state-of-the-art automatic
NLP systems. Evaluation is performed on the popular mTOR pathway. Based on
analyzing where current systems perform well and where they fail, we identify
possible avenues for progress.
Extracting Biological Pathway Models From NLP Event Representations
Authors: Michael Spranger, Sucheendra K. Palaniappan, Samik Ghosh
Date: 2016-08-12 00:00:00
Proceeding: WS 2015 7

This paper describes an an open-source software system for the automatic
conversion of NLP event representations to system biology structured data
interchange formats such as SBML and BioPAX. It is part of a larger effort to
make results of the NLP community available for system biology pathway
modelers.