pwc-archive/papers-with-abstracts huggingface dataset:
Gabor Barcodes for Medical Image Retrieval
Authors: Mina Nouredanesh, Hamid. R. Tizhoosh, Ershad Banijamali
Date: 2016-05-14 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Medical Image Retrieval, Retrieval

In recent years, advances in medical imaging have led to the emergence of
massive databases, containing images from a diverse range of modalities. This
has significantly heightened the need for automated annotation of the images on
one side, and fast and memory-efficient content-based image retrieval systems
on the other side. Binary descriptors have recently gained more attention as a
potential vehicle to achieve these goals. One of the recently introduced binary
descriptors for tagging of medical images are Radon barcodes (RBCs) that are
driven from Radon transform via local thresholding. Gabor transform is also a
powerful transform to extract texture-based information. Gabor features have
exhibited robustness against rotation, scale, and also photometric
disturbances, such as illumination changes and image noise in many
applications. This paper introduces Gabor Barcodes (GBCs), as a novel framework
for the image annotation. To find the most discriminative GBC for a given query
image, the effects of employing Gabor filters with different parameters, i.e.,
different sets of scales and orientations, are investigated, resulting in
different barcode lengths and retrieval performances. The proposed method has
been evaluated on the IRMA dataset with 193 classes comprising of 12,677 x-ray
images for indexing, and 1,733 x-rays images for testing. A total error score
as low as $351$ ($\approx 80\%$ accuracy for the first hit) was achieved.
Capturing divergence in dependency trees to improve syntactic projection
Authors: Ryan Georgi, Fei Xia, William D. Lewis
Date: 2016-05-14 00:00:00
Tasks: Word Alignment

Obtaining syntactic parses is a crucial part of many NLP pipelines. However,
most of the world's languages do not have large amounts of syntactically
annotated corpora available for building parsers. Syntactic projection
techniques attempt to address this issue by using parallel corpora consisting
of resource-poor and resource-rich language pairs, taking advantage of a parser
for the resource-rich language and word alignment between the languages to
project the parses onto the data for the resource-poor language. These
projection methods can suffer, however, when the two languages are divergent.
In this paper, we investigate the possibility of using small, parallel,
annotated corpora to automatically detect divergent structural patterns between
two languages. These patterns can then be used to improve structural projection
algorithms, allowing for better performing NLP tools for resource-poor
languages, in particular those that may not have large amounts of annotated
data necessary for traditional, fully-supervised methods. While this detection
process is not exhaustive, we demonstrate that common patterns of divergence
can be identified automatically without prior knowledge of a given language
pair, and the patterns can be used to improve performance of projection
algorithms.
Generalized Linear Models for Aggregated Data
Authors: Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo
Date: 2016-05-14 00:00:00
Tasks: Imputation

Databases in domains such as healthcare are routinely released to the public
in aggregated form. Unfortunately, naive modeling with aggregated data may
significantly diminish the accuracy of inferences at the individual level. This
paper addresses the scenario where features are provided at the individual
level, but the target variables are only available as histogram aggregates or
order statistics. We consider a limiting case of generalized linear modeling
when the target variables are only known up to permutation, and explore how
this relates to permutation testing; a standard technique for assessing
statistical dependency. Based on this relationship, we propose a simple
algorithm to estimate the model parameters and individual level inferences via
alternating imputation and standard generalized linear model fitting. Our
results suggest the effectiveness of the proposed approach when, in the
original data, permutation testing accurately ascertains the veracity of the
linear relationship. The framework is extended to general histogram data with
larger bins - with order statistics such as the median as a limiting case. Our
experimental results on simulated data and aggregated healthcare data suggest a
diminishing returns property with respect to the granularity of the histogram -
when a linear relationship holds in the original data, the targets can be
predicted accurately given relatively coarse histograms.
Monotone Retargeting for Unsupervised Rank Aggregation with Object Features
Authors: Avradeep Bhowmik, Joydeep Ghosh
Date: 2016-05-14 00:00:00
Tasks: Object

Learning the true ordering between objects by aggregating a set of expert
opinion rank order lists is an important and ubiquitous problem in many
applications ranging from social choice theory to natural language processing
and search aggregation. We study the problem of unsupervised rank aggregation
where no ground truth ordering information in available, neither about the true
preference ordering between any set of objects nor about the quality of
individual rank lists. Aggregating the often inconsistent and poor quality rank
lists in such an unsupervised manner is a highly challenging problem, and
standard consensus-based methods are often ill-defined, and difficult to solve.
In this manuscript we propose a novel framework to bypass these issues by using
object attributes to augment the standard rank aggregation framework. We design
algorithms that learn joint models on both rank lists and object features to
obtain an aggregated rank ordering that is more accurate and robust, and also
helps weed out rank lists of dubious validity. We validate our techniques on
synthetic datasets where our algorithm is able to estimate the true rank
ordering even when the rank lists are corrupted. Experiments on three real
datasets, MQ2008, MQ2008 and OHSUMED, show that using object features can
result in significant improvement in performance over existing rank aggregation
methods that do not use object information. Furthermore, when at least some of
the rank lists are of high quality, our methods are able to effectively exploit
their high expertise to output an aggregated rank ordering of great accuracy.
Proceedings of the 5th Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI) at NIPS 2015
Authors: I. Rish, L. Wehbe, G. Langs, M. Grosse-Wentrup, B. Murphy, G. Cecchi
Date: 2016-05-14 00:00:00
Tasks: BIG-bench Machine Learning, Prognosis

This volume is a collection of contributions from the 5th Workshop on Machine
Learning and Interpretation in Neuroimaging (MLINI) at the Neural Information
Processing Systems (NIPS 2015) conference. Modern multivariate statistical
methods developed in the rapidly growing field of machine learning are being
increasingly applied to various problems in neuroimaging, from cognitive state
detection to clinical diagnosis and prognosis. Multivariate pattern analysis
methods are designed to examine complex relationships between high-dimensional
signals, such as brain images, and outcomes of interest, such as the category
of a stimulus, a type of a mental state of a subject, or a specific mental
disorder. Such techniques are in contrast with the traditional mass-univariate
approaches that dominated neuroimaging in the past and treated each individual
imaging measurement in isolation.
  We believe that machine learning has a prominent role in shaping how
questions in neuroscience are framed, and that the machine-learning mind set is
now entering modern psychology and behavioral studies. It is also equally
important that practical applications in these fields motivate a rapidly
evolving line or research in the machine learning community. In parallel, there
is an intense interest in learning more about brain function in the context of
rich naturalistic environments and scenes. Efforts to go beyond highly specific
paradigms that pinpoint a single function, towards schemes for measuring the
interaction with natural and more varied scene are made. The goal of the
workshop is to pinpoint the most pressing issues and common challenges across
the neuroscience, neuroimaging, psychology and machine learning fields, and to
sketch future directions and open questions in the light of novel methodology.
Better safe than sorry: Risky function exploitation through safe optimization
Authors: Eric Schulz, Quentin J. M. Huys, Dominik R. Bach, Maarten Speekenbrink, Andreas Krause
Date: 2016-02-02 00:00:00

Exploration-exploitation of functions, that is learning and optimizing a
mapping between inputs and expected outputs, is ubiquitous to many real world
situations. These situations sometimes require us to avoid certain outcomes at
all cost, for example because they are poisonous, harmful, or otherwise
dangerous. We test participants' behavior in scenarios in which they have to
find the optimum of a function while at the same time avoid outputs below a
certain threshold. In two experiments, we find that Safe-Optimization, a
Gaussian Process-based exploration-exploitation algorithm, describes
participants' behavior well and that participants seem to care firstly whether
a point is safe and then try to pick the optimal point from all such safe
points. This means that their trade-off between exploration and exploitation
can be seen as an intelligent, approximate, and homeostasis-driven strategy.
Online Optimization for Large-Scale Max-Norm Regularization
Authors: Jie Shen, Huan Xu, Ping Li
Date: 2014-06-12 00:00:00
Tasks: Matrix Completion

Max-norm regularizer has been extensively studied in the last decade as it
promotes an effective low-rank estimation for the underlying data. However,
such max-norm regularized problems are typically formulated and solved in a
batch manner, which prevents it from processing big data due to possible memory
budget. In this paper, hence, we propose an online algorithm that is scalable
to large-scale setting. Particularly, we consider the matrix decomposition
problem as an example, although a simple variant of the algorithm and analysis
can be adapted to other important problems such as matrix completion. The
crucial technique in our implementation is to reformulating the max-norm to an
equivalent matrix factorization form, where the factors consist of a (possibly
overcomplete) basis component and a coefficients one. In this way, we may
maintain the basis component in the memory and optimize over it and the
coefficients for each sample alternatively. Since the memory footprint of the
basis component is independent of the sample size, our algorithm is appealing
when manipulating a large collection of samples. We prove that the sequence of
the solutions (i.e., the basis component) produced by our algorithm converges
to a stationary point of the expected loss function asymptotically. Numerical
study demonstrates encouraging results for the efficacy and robustness of our
algorithm compared to the widely used nuclear norm solvers.
Neural Dataset Generality
Authors: Ragav Venkatesan, Vijetha Gattupalli, Baoxin Li
Date: 2016-05-14 00:00:00
Tasks: Transfer Learning

Often the filters learned by Convolutional Neural Networks (CNNs) from
different datasets appear similar. This is prominent in the first few layers.
This similarity of filters is being exploited for the purposes of transfer
learning and some studies have been made to analyse such transferability of
features. This is also being used as an initialization technique for different
tasks in the same dataset or for the same task in similar datasets.
Off-the-shelf CNN features have capitalized on this idea to promote their
networks as best transferable and most general and are used in a cavalier
manner in day-to-day computer vision tasks.
  It is curious that while the filters learned by these CNNs are related to the
atomic structures of the images from which they are learnt, all datasets learn
similar looking low-level filters. With the understanding that a dataset that
contains many such atomic structures learn general filters and are therefore
useful to initialize other networks with, we propose a way to analyse and
quantify generality among datasets from their accuracies on transferred
filters. We applied this metric on several popular character recognition,
natural image and a medical image dataset, and arrived at some interesting
conclusions. On further experimentation we also discovered that particular
classes in a dataset themselves are more general than others.
Occurrence Statistics of Entities, Relations and Types on the Web
Authors: Aman Madaan, Sunita Sarawagi
Date: 2016-05-14 00:00:00
Tasks: Entity Disambiguation

The problem of collecting reliable estimates of occurrence of entities on the
open web forms the premise for this report. The models learned for tagging
entities cannot be expected to perform well when deployed on the web. This is
owing to the severe mismatch in the distributions of such entities on the web
and in the relatively diminutive training data. In this report, we build up the
case for maximum mean discrepancy for estimation of occurrence statistics of
entities on the web, taking a review of named entity disambiguation techniques
and related concepts along the way.
Support Vector Algorithms for Optimizing the Partial Area Under the ROC Curve
Authors: Harikrishna Narasimhan, Shivani Agarwal
Date: 2016-05-13 00:00:00
Tasks: Combinatorial Optimization

The area under the ROC curve (AUC) is a widely used performance measure in
machine learning. Increasingly, however, in several applications, ranging from
ranking to biometric screening to medicine, performance is measured not in
terms of the full area under the ROC curve, but in terms of the \emph{partial}
area under the ROC curve between two false positive rates. In this paper, we
develop support vector algorithms for directly optimizing the partial AUC
between any two false positive rates. Our methods are based on minimizing a
suitable proxy or surrogate objective for the partial AUC error. In the case of
the full AUC, one can readily construct and optimize convex surrogates by
expressing the performance measure as a summation of pairwise terms. The
partial AUC, on the other hand, does not admit such a simple decomposable
structure, making it more challenging to design and optimize (tight) convex
surrogates for this measure.
  Our approach builds on the structural SVM framework of Joachims (2005) to
design convex surrogates for partial AUC, and solves the resulting optimization
problem using a cutting plane solver. Unlike the full AUC, where the
combinatorial optimization needed in each iteration of the cutting plane solver
can be decomposed and solved efficiently, the corresponding problem for the
partial AUC is harder to decompose. One of our main contributions is a
polynomial time algorithm for solving the combinatorial optimization problem
associated with partial AUC. We also develop an approach for optimizing a
tighter non-convex hinge loss based surrogate for the partial AUC using
difference-of-convex programming. Our experiments on a variety of real-world
and benchmark tasks confirm the efficacy of the proposed methods.
Modeling and Estimation of Discrete-Time Reciprocal Processes via Probabilistic Graphical Models
Authors: Francesca Paola Carli
Date: 2016-03-14 00:00:00

Reciprocal processes are acausal generalizations of Markov processes
introduced by Bernstein in 1932. In the literature, a significant amount of
attention has been focused on developing dynamical models for reciprocal
processes. In this paper, we provide a probabilistic graphical model for
reciprocal processes. This leads to a principled solution of the smoothing
problem via message passing algorithms. For the finite state space case,
convergence analysis is revisited via the Hilbert metric.
ABtree: An Algorithm for Subgroup-Based Treatment Assignment
Authors: Derek Feng, Xiaofei Wang
Date: 2016-05-13 00:00:00
Tasks: Marketing

Given two possible treatments, there may exist subgroups who benefit greater
from one treatment than the other. This problem is relevant to the field of
marketing, where treatments may correspond to different ways of selling a
product. It is similarly relevant to the field of public policy, where
treatments may correspond to specific government programs. And finally,
personalized medicine is a field wholly devoted to understanding which
subgroups of individuals will benefit from particular medical treatments. We
present a computationally fast tree-based method, ABtree, for treatment effect
differentiation. Unlike other methods, ABtree specifically produces decision
rules for optimal treatment assignment on a per-individual basis. The treatment
choices are selected for maximizing the overall occurrence of a desired binary
outcome, conditional on a set of covariates. In this poster, we present the
methodology on tree growth and pruning, and show performance results when
applied to simulated data as well as real data.
Unbiased Bayesian Inference for Population Markov Jump Processes via Random Truncations
Authors: Anastasis Georgoulas, Jane Hillston, Guido Sanguinetti
Date: 2015-09-28 00:00:00
Tasks: Bayesian Inference

We consider continuous time Markovian processes where populations of
individual agents interact stochastically according to kinetic rules. Despite
the increasing prominence of such models in fields ranging from biology to
smart cities, Bayesian inference for such systems remains challenging, as these
are continuous time, discrete state systems with potentially infinite
state-space. Here we propose a novel efficient algorithm for joint state /
parameter posterior sampling in population Markov Jump processes. We introduce
a class of pseudo-marginal sampling algorithms based on a random truncation
method which enables a principled treatment of infinite state spaces. Extensive
evaluation on a number of benchmark models shows that this approach achieves
considerable savings compared to state of the art methods, retaining accuracy
and fast convergence. We also present results on a synthetic biology data set
showing the potential for practical usefulness of our work.
Simultaneous Surface Reflectance and Fluorescence Spectra Estimation
Authors: Henryk Blasinski, Joyce Farrell, Brian Wandell
Date: 2016-05-13 00:00:00

There is widespread interest in estimating the fluorescence properties of
natural materials in an image. However, the separation between reflected and
fluoresced components is difficult, because it is impossible to distinguish
reflected and fluoresced photons without controlling the illuminant spectrum.
We show how to jointly estimate the reflectance and fluorescence from a single
set of images acquired under multiple illuminants. We present a framework based
on a linear approximation to the physical equations describing image formation
in terms of surface spectral reflectance and fluorescence due to multiple
fluorophores. We relax the non-convex, inverse estimation problem in order to
jointly estimate the reflectance and fluorescence properties in a single
optimization step and we use the Alternating Direction Method of Multipliers
(ADMM) approach to efficiently find a solution. We provide a software
implementation of the solver for our method and prior methods. We evaluate the
accuracy and reliability of the method using both simulations and experimental
data. To acquire data to test the methods, we built a custom imaging system
using a monochrome camera, a filter wheel with bandpass transmissive filters
and a small number of light emitting diodes. We compared the system and
algorithm performance with the ground truth as well as with prior methods. Our
approach produces lower errors compared to earlier algorithms.
Semantic Spaces
Authors: Yuri Manin, Matilde Marcolli
Date: 2016-05-13 00:00:00

Any natural language can be considered as a tool for producing large
databases (consisting of texts, written, or discursive). This tool for its
description in turn requires other large databases (dictionaries, grammars
etc.). Nowadays, the notion of database is associated with computer processing
and computer memory. However, a natural language resides also in human brains
and functions in human communication, from interpersonal to intergenerational
one. We discuss in this survey/research paper mathematical, in particular
geometric, constructions, which help to bridge these two worlds. In particular,
in this paper we consider the Vector Space Model of semantics based on
frequency matrices, as used in Natural Language Processing. We investigate
underlying geometries, formulated in terms of Grassmannians, projective spaces,
and flag varieties. We formulate the relation between vector space models and
semantic spaces based on semic axes in terms of projectability of subvarieties
in Grassmannians and projective spaces. We interpret Latent Semantics as a
geometric flow on Grassmannians. We also discuss how to formulate G\"ardenfors'
notion of "meeting of minds" in our geometric setting.
Natural Language Inference by Tree-Based Convolution and Heuristic Matching
Authors: Lili Mou, Rui Men, Ge Li, Yan Xu, Lu Zhang, Rui Yan, Zhi Jin
Date: 2015-12-28 00:00:00
Proceeding: ACL 2016 8
Tasks: Natural Language Inference, Sentence

In this paper, we propose the TBCNN-pair model to recognize entailment and
contradiction between two sentences. In our model, a tree-based convolutional
neural network (TBCNN) captures sentence-level semantics; then heuristic
matching layers like concatenation, element-wise product/difference combine the
information in individual sentences. Experimental results show that our model
outperforms existing sentence encoding-based approaches by a large margin.
Anytime Inference in Valuation Algebras
Authors: Abhishek Dasgupta, Samson Abramsky
Date: 2016-05-13 00:00:00

Anytime inference is inference performed incrementally, with the accuracy of
the inference being controlled by a tunable parameter, usually time. Such
anytime inference algorithms are also usually interruptible, gradually
converging to the exact inference value until terminated. While anytime
inference algorithms for specific domains like probability potentials exist in
the literature, our objective in this article is to obtain an anytime inference
algorithm which is sufficiently generic to cover a wide range of domains. For
this we utilise the theory of generic inference as a basis for constructing an
anytime inference algorithm, and in particular, extending work done on ordered
valuation algebras. The novel contribution of this work is the construction of
anytime algorithms in a generic framework, which automatically gives us
instantiations in various useful domains. We also show how to apply this
generic framework for anytime inference in semiring induced valuation algebras,
an important subclass of valuation algebras, which includes instances like
probability potentials, disjunctive normal forms and distributive lattices.
  Keywords: Approximation; Anytime algorithms; Resource-bounded computation;
Generic inference; Valuation algebras; Local computation; Binary join trees.