pwc-archive/papers-with-abstracts huggingface dataset:
A data augmentation methodology for training machine/deep learning gait recognition algorithms
Authors: Christoforos C. Charalambous, Anil A. Bharath
Date: 2016-10-24 00:00:00
Tasks: Data Augmentation, Gait Recognition

There are several confounding factors that can reduce the accuracy of gait
recognition systems. These factors can reduce the distinctiveness, or alter the
features used to characterise gait, they include variations in clothing,
lighting, pose and environment, such as the walking surface. Full invariance to
all confounding factors is challenging in the absence of high-quality labelled
training data. We introduce a simulation-based methodology and a
subject-specific dataset which can be used for generating synthetic video
frames and sequences for data augmentation. With this methodology, we generated
a multi-modal dataset. In addition, we supply simulation files that provide the
ability to simultaneously sample from several confounding variables. The basis
of the data is real motion capture data of subjects walking and running on a
treadmill at different speeds. Results from gait recognition experiments
suggest that information about the identity of subjects is retained within
synthetically generated examples. The dataset and methodology allow studies
into fully-invariant identity recognition spanning a far greater number of
observation conditions than would otherwise be possible.
Geometry of Polysemy
Authors: Jiaqi Mu, Suma Bhat, Pramod Viswanath
Date: 2016-10-24 00:00:00
Tasks: Clustering, Sentence

Vector representations of words have heralded a transformational approach to
classical problems in NLP; the most popular example is word2vec. However, a
single vector does not suffice to model the polysemous nature of many
(frequent) words, i.e., words with multiple meanings. In this paper, we propose
a three-fold approach for unsupervised polysemy modeling: (a) context
representations, (b) sense induction and disambiguation and (c) lexeme (as a
word and sense pair) representations. A key feature of our work is the finding
that a sentence containing a target word is well represented by a low rank
subspace, instead of a point in a vector space. We then show that the subspaces
associated with a particular sense of the target word tend to intersect over a
line (one-dimensional subspace), which we use to disambiguate senses using a
clustering algorithm that harnesses the Grassmannian geometry of the
representations. The disambiguation algorithm, which we call $K$-Grassmeans,
leads to a procedure to label the different senses of the target word in the
corpus -- yielding lexeme vector representations, all in an unsupervised manner
starting from a large (Wikipedia) corpus in English. Apart from several
prototypical target (word,sense) examples and a host of empirical studies to
intuit and justify the various geometric representations, we validate our
algorithms on standard sense induction and disambiguation datasets and present
new state-of-the-art results.
On Multiplicative Multitask Feature Learning
Authors: Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun
Date: 2016-10-24 00:00:00
Proceeding: NeurIPS 2014 12

We investigate a general framework of multiplicative multitask feature
learning which decomposes each task's model parameters into a multiplication of
two components. One of the components is used across all tasks and the other
component is task-specific. Several previous methods have been proposed as
special cases of our framework. We study the theoretical properties of this
framework when different regularization conditions are applied to the two
decomposed components. We prove that this framework is mathematically
equivalent to the widely used multitask feature learning methods that are based
on a joint regularization of all model parameters, but with a more general form
of regularizers. Further, an analytical formula is derived for the across-task
component as related to the task-specific component for all these regularizers,
leading to a better understanding of the shrinkage effect. Study of this
framework motivates new multitask learning algorithms. We propose two new
learning formulations by varying the parameters in the proposed framework.
Empirical studies have revealed the relative advantages of the two new
formulations by comparing with the state of the art, which provides instructive
insights into the feature learning problem with multiple tasks.
A Greedy Approach to Adapting the Trace Parameter for Temporal Difference Learning
Authors: Martha White, Adam White
Date: 2016-07-02 00:00:00
Tasks: Meta-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

One of the main obstacles to broad application of reinforcement learning
methods is the parameter sensitivity of our core learning algorithms. In many
large-scale applications, online computation and function approximation
represent key strategies in scaling up reinforcement learning algorithms. In
this setting, we have effective and reasonably well understood algorithms for
adapting the learning-rate parameter, online during learning. Such
meta-learning approaches can improve robustness of learning and enable
specialization to current task, improving learning speed. For
temporal-difference learning algorithms which we study here, there is yet
another parameter, $\lambda$, that similarly impacts learning speed and
stability in practice. Unfortunately, unlike the learning-rate parameter,
$\lambda$ parametrizes the objective function that temporal-difference methods
optimize. Different choices of $\lambda$ produce different fixed-point
solutions, and thus adapting $\lambda$ online and characterizing the
optimization is substantially more complex than adapting the learning-rate
parameter. There are no meta-learning method for $\lambda$ that can achieve (1)
incremental updating, (2) compatibility with function approximation, and (3)
maintain stability of learning under both on and off-policy sampling. In this
paper we contribute a novel objective function for optimizing $\lambda$ as a
function of state rather than time. We derive a new incremental, linear
complexity $\lambda$-adaption algorithm that does not require offline batch
updating or access to a model of the world, and present a suite of experiments
illustrating the practicality of our new algorithm in three different settings.
Taken together, our contributions represent a concrete step towards black-box
application of temporal-difference learning methods in real world problems.
Automated OCT Segmentation for Images with DME
Authors: Sohini Roychowdhury, Dara D. Koozekanani, Michael Reinsbach, Keshab K. Parhi
Date: 2016-10-24 00:00:00
Tasks: Denoising

This paper presents a novel automated system that segments six sub-retinal
layers from optical coherence tomography (OCT) image stacks of healthy patients
and patients with diabetic macular edema (DME). First, each image in the OCT
stack is denoised using a Wiener deconvolution algorithm that estimates the
additive speckle noise variance using a novel Fourier-domain based structural
error. This denoising method enhances the image SNR by an average of 12dB.
Next, the denoised images are subjected to an iterative multi-resolution
high-pass filtering algorithm that detects seven sub-retinal surfaces in six
iterative steps. The thicknesses of each sub-retinal layer for all scans from a
particular OCT stack are then compared to the manually marked groundtruth. The
proposed system uses adaptive thresholds for denoising and segmenting each
image and hence it is robust to disruptions in the retinal micro-structure due
to DME. The proposed denoising and segmentation system has an average error of
1.2-5.8 $\mu m$ and 3.5-26$\mu m$ for segmenting sub-retinal surfaces in normal
and abnormal images with DME, respectively. For estimating the sub-retinal
layer thicknesses, the proposed system has an average error of 0.2-2.5 $\mu m$
and 1.8-18 $\mu m$ in normal and abnormal images, respectively. Additionally,
the average inner sub-retinal layer thickness in abnormal images is estimated
as 275$\mu m (r=0.92)$ with an average error of 9.3 $\mu m$, while the average
thickness of the outer layers in abnormal images is estimated as 57.4$\mu m
(r=0.74)$ with an average error of 3.5 $\mu m$. The proposed system can be
useful for tracking the disease progression for DME over a period of time.
Markov Chain methods for the bipartite Boolean quadratic programming problem
Authors: Daniel Karapetyan, Abraham P. Punnen, Andrew J. Parkes
Date: 2016-05-06 00:00:00

We study the Bipartite Boolean Quadratic Programming Problem (BBQP) which is
an extension of the well known Boolean Quadratic Programming Problem (BQP).
Applications of the BBQP include mining discrete patterns from binary data,
approximating matrices by rank-one binary matrices, computing the cut-norm of a
matrix, and solving optimisation problems such as maximum weight biclique,
bipartite maximum weight cut, maximum weight induced sub-graph of a bipartite
graph, etc. For the BBQP, we first present several algorithmic components,
specifically, hill climbers and mutations, and then show how to combine them in
a high-performance metaheuristic. Instead of hand-tuning a standard
metaheuristic to test the efficiency of the hybrid of the components, we chose
to use an automated generation of a multi-component metaheuristic to save human
time, and also improve objectivity in the analysis and comparisons of
components. For this we designed a new metaheuristic schema which we call
Conditional Markov Chain Search (CMCS). We show that CMCS is flexible enough to
model several standard metaheuristics; this flexibility is controlled by
multiple numeric parameters, and so is convenient for automated generation. We
study the configurations revealed by our approach and show that the best of
them outperforms the previous state-of-the-art BBQP algorithm by several orders
of magnitude. In our experiments we use benchmark instances introduced in the
preliminary version of this paper and described here, which have already become
the de facto standard in the BBQP literature.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Authors: Alexandra Chouldechova
Date: 2016-10-24 00:00:00
Tasks: Fairness, Prediction

Recidivism prediction instruments provide decision makers with an assessment
of the likelihood that a criminal defendant will reoffend at a future point in
time. While such instruments are gaining increasing popularity across the
country, their use is attracting tremendous controversy. Much of the
controversy concerns potential discriminatory bias in the risk assessments that
are produced. This paper discusses a fairness criterion originating in the
field of educational and psychological testing that has recently been applied
to assess the fairness of recidivism prediction instruments. We demonstrate how
adherence to the criterion may lead to considerable disparate impact when
recidivism prevalence differs across groups.
Learning Structured Inference Neural Networks with Label Relations
Authors: Hexiang Hu, Guang-Tong Zhou, Zhiwei Deng, Zicheng Liao, Greg Mori
Date: 2015-11-17 00:00:00
Proceeding: CVPR 2016 6
Tasks: General Classification, image-classification, Image Classification

Images of scenes have various objects as well as abundant attributes, and
diverse levels of visual categorization are possible. A natural image could be
assigned with fine-grained labels that describe major components,
coarse-grained labels that depict high level abstraction or a set of labels
that reveal attributes. Such categorization at different concept layers can be
modeled with label graphs encoding label information. In this paper, we exploit
this rich information with a state-of-art deep learning framework, and propose
a generic structured model that leverages diverse label relations to improve
image classification performance. Our approach employs a novel stacked label
prediction neural network, capturing both inter-level and intra-level label
semantics. We evaluate our method on benchmark image datasets, and empirical
results illustrate the efficacy of our model.
Nonlinear Adaptive Algorithms on Rank-One Tensor Models
Authors: Felipe C. Pinheiro, Cassio G. Lopes
Date: 2016-10-24 00:00:00

This work proposes a low complexity nonlinearity model and develops adaptive
algorithms over it. The model is based on the decomposable---or rank-one, in
tensor language---Volterra kernels. It may also be described as a product of
FIR filters, which explains its low-complexity. The rank-one model is also
interesting because it comes from a well-posed problem in approximation theory.
The paper uses such model in an estimation theory context to develop an exact
gradient-type algorithm, from which adaptive algorithms such as the least mean
squares (LMS) filter and its data-reuse version---the TRUE-LMS---are derived.
Stability and convergence issues are addressed. The algorithms are then tested
in simulations, which show its good performance when compared to other
nonlinear processing algorithms in the literature.
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition
Authors: Ahmed M. Alaa, Mihaela van der Schaar
Date: 2016-10-24 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Decision Making, Time Series, Time Series Analysis

We develop a Bayesian model for decision-making under time pressure with
endogenous information acquisition. In our model, the decision maker decides
when to observe (costly) information by sampling an underlying continuous-time
stochastic process (time series) that conveys information about the potential
occurrence or non-occurrence of an adverse event which will terminate the
decision-making process. In her attempt to predict the occurrence of the
adverse event, the decision-maker follows a policy that determines when to
acquire information from the time series (continuation), and when to stop
acquiring information and make a final prediction (stopping). We show that the
optimal policy has a rendezvous structure, i.e. a structure in which whenever a
new information sample is gathered from the time series, the optimal "date" for
acquiring the next sample becomes computable. The optimal interval between two
information samples balances a trade-off between the decision maker's surprise,
i.e. the drift in her posterior belief after observing new information, and
suspense, i.e. the probability that the adverse event occurs in the time
interval between two information samples. Moreover, we characterize the
continuation and stopping regions in the decision-maker's state-space, and show
that they depend not only on the decision-maker's beliefs, but also on the
context, i.e. the current realization of the time series.
A Harmonic Mean Linear Discriminant Analysis for Robust Image Classification
Authors: Shuai Zheng, Feiping Nie, Chris Ding, Heng Huang
Date: 2016-10-14 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification, image-classification, Image Classification, Supervised dimensionality reduction

Linear Discriminant Analysis (LDA) is a widely-used supervised dimensionality
reduction method in computer vision and pattern recognition. In null space
based LDA (NLDA), a well-known LDA extension, between-class distance is
maximized in the null space of the within-class scatter matrix. However, there
are some limitations in NLDA. Firstly, for many data sets, null space of
within-class scatter matrix does not exist, thus NLDA is not applicable to
those datasets. Secondly, NLDA uses arithmetic mean of between-class distances
and gives equal consideration to all between-class distances, which makes
larger between-class distances can dominate the result and thus limits the
performance of NLDA. In this paper, we propose a harmonic mean based Linear
Discriminant Analysis, Multi-Class Discriminant Analysis (MCDA), for image
classification, which minimizes the reciprocal of weighted harmonic mean of
pairwise between-class distance. More importantly, MCDA gives higher priority
to maximize small between-class distances. MCDA can be extended to multi-label
dimension reduction. Results on 7 single-label data sets and 4 multi-label data
sets show that MCDA has consistently better performance than 10 other
single-label approaches and 4 other multi-label approaches in terms of
classification accuracy, macro and micro average F1 score.
Virtual Embodiment: A Scalable Long-Term Strategy for Artificial Intelligence Research
Authors: Douwe Kiela, Luana Bulat, Anita L. Vero, Stephen Clark
Date: 2016-10-24 00:00:00
Tasks: Philosophy

Meaning has been called the "holy grail" of a variety of scientific
disciplines, ranging from linguistics to philosophy, psychology and the
neurosciences. The field of Artifical Intelligence (AI) is very much a part of
that list: the development of sophisticated natural language semantics is a
sine qua non for achieving a level of intelligence comparable to humans.
Embodiment theories in cognitive science hold that human semantic
representation depends on sensori-motor experience; the abundant evidence that
human meaning representation is grounded in the perception of physical reality
leads to the conclusion that meaning must depend on a fusion of multiple
(perceptual) modalities. Despite this, AI research in general, and its
subdisciplines such as computational linguistics and computer vision in
particular, have focused primarily on tasks that involve a single modality.
Here, we propose virtual embodiment as an alternative, long-term strategy for
AI research that is multi-modal in nature and that allows for the kind of
scalability required to develop the field coherently and incrementally, in an
ethically responsible fashion.
Reordering rules for English-Hindi SMT
Authors: Raj Nath Patel, Rohit Gupta, Prakash B. Pimpale, Sasikumar M
Date: 2016-10-24 00:00:00
Proceeding: WS 2013 8
Tasks: Machine Translation, Position, Sentence, Translation

Reordering is a preprocessing stage for Statistical Machine Translation (SMT)
system where the words of the source sentence are reordered as per the syntax
of the target language. We are proposing a rich set of rules for better
reordering. The idea is to facilitate the training process by better alignments
and parallel phrase extraction for a phrase-based SMT system. Reordering also
helps the decoding process and hence improving the machine translation quality.
We have observed significant improvements in the translation quality by using
our approach over the baseline SMT. We have used BLEU, NIST, multi-reference
word error rate, multi-reference position independent error rate for judging
the improvements. We have exploited open source SMT toolkit MOSES to develop
the system.
Using Machine Learning to Detect Noisy Neighbors in 5G Networks
Authors: Udi Margolin, Alberto Mozo, Bruno Ordozgoiti, Danny Raz, Elisha Rosensweig, Itai Segall
Date: 2016-10-24 00:00:00
Tasks: BIG-bench Machine Learning, Management

5G networks are expected to be more dynamic and chaotic in their structure
than current networks. With the advent of Network Function Virtualization
(NFV), Network Functions (NF) will no longer be tightly coupled with the
hardware they are running on, which poses new challenges in network management.
Noisy neighbor is a term commonly used to describe situations in NFV
infrastructure where an application experiences degradation in performance due
to the fact that some of the resources it needs are occupied by other
applications in the same cloud node. These situations cannot be easily
identified using straightforward approaches, which calls for the use of
sophisticated methods for NFV infrastructure management. In this paper we
demonstrate how Machine Learning (ML) techniques can be used to identify such
events. Through experiments using data collected at real NFV infrastructure, we
show that standard models for automated classification can detect the noisy
neighbor phenomenon with an accuracy of more than 90% in a simple scenario.
Statistical Machine Translation for Indian Languages: Mission Hindi
Authors: Raj Nath Patel, Prakash B. Pimpale, Sasikumar M
Date: 2016-10-24 00:00:00
Tasks: Machine Translation, Translation

This paper discusses Centre for Development of Advanced Computing Mumbai's
(CDACM) submission to the NLP Tools Contest on Statistical Machine Translation
in Indian Languages (ILSMT) 2014 (collocated with ICON 2014). The objective of
the contest was to explore the effectiveness of Statistical Machine Translation
(SMT) for Indian language to Indian language and English-Hindi machine
translation. In this paper, we have proposed that suffix separation and word
splitting for SMT from agglutinative languages to Hindi significantly improves
over the baseline (BL). We have also shown that the factored model with
reordering outperforms the phrase-based SMT for English-Hindi (\enhi). We
report our work on all five pairs of languages, namely Bengali-Hindi (\bnhi),
Marathi-Hindi (\mrhi), Tamil-Hindi (\tahi), Telugu-Hindi (\tehi), and \enhi for
Health, Tourism, and General domains.
Semantic Measures for the Comparison of Units of Language, Concepts or Instances from Text and Knowledge Base Analysis
Authors: Sébastien Harispe, Sylvie Ranwez, Stefan Janaqi, Jacky Montmain
Date: 2013-10-04 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Semantic measures are widely used today to estimate the strength of the
semantic relationship between elements of various types: units of language
(e.g., words, sentences, documents), concepts or even instances semantically
characterized (e.g., diseases, genes, geographical locations). Semantic
measures play an important role to compare such elements according to semantic
proxies: texts and knowledge representations, which support their meaning or
describe their nature. Semantic measures are therefore essential for designing
intelligent agents which will for example take advantage of semantic analysis
to mimic human ability to compare abstract or concrete objects. This paper
proposes a comprehensive survey of the broad notion of semantic measure for the
comparison of units of language, concepts or instances based on semantic proxy
analyses. Semantic measures generalize the well-known notions of semantic
similarity, semantic relatedness and semantic distance, which have been
extensively studied by various communities over the last decades (e.g.,
Cognitive Sciences, Linguistics, and Artificial Intelligence to mention a few).
Savu: A Python-based, MPI Framework for Simultaneous Processing of Multiple, N-dimensional, Large Tomography Datasets
Authors: Nicola Wadeson, Mark Basham
Date: 2016-10-24 00:00:00

Diamond Light Source (DLS), the UK synchrotron facility, attracts scientists
from across the world to perform ground-breaking x-ray experiments. With over
3000 scientific users per year, vast amounts of data are collected across the
experimental beamlines, with the highest volume of data collected during
tomographic imaging experiments. A growing interest in tomography as an imaging
technique, has led to an expansion in the range of experiments performed, in
addition to a growth in the size of the data per experiment.
  Savu is a portable, flexible, scientific processing pipeline capable of
processing multiple, n-dimensional datasets in serial on a PC, or in parallel
across a cluster. Developed at DLS, and successfully deployed across the
beamlines, it uses a modular plugin format to enable experiment-specific
processing and utilises parallel HDF5 to remove RAM restrictions. The Savu
design, described throughout this paper, focuses on easy integration of
existing and new functionality, flexibility and ease of use for users and
developers alike.