pwc-archive/papers-with-abstracts huggingface dataset:
Fairness as a Program Property
Authors: Aws Albarghouthi, Loris D'Antoni, Samuel Drews, Aditya Nori
Date: 2016-10-19 00:00:00
Tasks: Decision Making, Fairness

We explore the following question: Is a decision-making program fair, for
some useful definition of fairness? First, we describe how several algorithmic
fairness questions can be phrased as program verification problems. Second, we
discuss an automated verification technique for proving or disproving fairness
of decision-making programs with respect to a probabilistic model of the
population.
Support Vector Machines and Generalisation in HEP
Authors: A. Bethani, A. J. Bevan, J. Hays, T. J. Stevenson
Date: 2016-10-19 00:00:00

We review the concept of support vector machines (SVMs) and discuss examples
of their use. One of the benefits of SVM algorithms, compared with neural
networks and decision trees is that they can be less susceptible to over
fitting than those other algorithms are to over training. This issue is related
to the generalisation of a multivariate algorithm (MVA); a problem that has
often been overlooked in particle physics. We discuss cross validation and how
this can be used to improve the generalisation of a MVA in the context of High
Energy Physics analyses. The examples presented use the Toolkit for
Multivariate Analysis (TMVA) based on ROOT and describe our improvements to the
SVM functionality and new tools introduced for cross validation within this
framework.
Chinese Restaurant Process for cognate clustering: A threshold free approach
Authors: Taraka Rama
Date: 2016-10-19 00:00:00
Tasks: Clustering

In this paper, we introduce a threshold free approach, motivated from Chinese
Restaurant Process, for the purpose of cognate clustering. We show that our
approach yields similar results to a linguistically motivated cognate
clustering system known as LexStat. Our Chinese Restaurant Process system is
fast and does not require any threshold and can be applied to any language
family of the world.
Fast and Accurate Surface Normal Integration on Non-Rectangular Domains
Authors: Martin Bähr, Michael Breuß, Yvain Quéau, Ali Sharifi Boroujerdi, Jean-Denis Durou
Date: 2016-10-19 00:00:00
Tasks: Computational Efficiency

The integration of surface normals for the purpose of computing the shape of
a surface in 3D space is a classic problem in computer vision. However, even
nowadays it is still a challenging task to devise a method that combines the
flexibility to work on non-trivial computational domains with high accuracy,
robustness and computational efficiency. By uniting a classic approach for
surface normal integration with modern computational techniques we construct a
solver that fulfils these requirements. Building upon the Poisson integration
model we propose to use an iterative Krylov subspace solver as a core step in
tackling the task. While such a method can be very efficient, it may only show
its full potential when combined with a suitable numerical preconditioning and
a problem-specific initialisation. We perform a thorough numerical study in
order to identify an appropriate preconditioner for our purpose. To address the
issue of a suitable initialisation we propose to compute this initial state via
a recently developed fast marching integrator. Detailed numerical experiments
illuminate the benefits of this novel combination. In addition, we show on
real-world photometric stereo datasets that the developed numerical framework
is flexible enough to tackle modern computer vision applications.
K-Nearest Neighbor Classification Using Anatomized Data
Authors: Koray Mancuhan, Chris Clifton
Date: 2016-10-19 00:00:00
Tasks: Anatomy, Classification, General Classification

This paper analyzes k nearest neighbor classification with training data
anonymized using anatomy. Anatomy preserves all data values, but introduces
uncertainty in the mapping between identifying and sensitive values. We first
study the theoretical effect of the anatomized training data on the k nearest
neighbor error rate bounds, nearest neighbor convergence rate, and Bayesian
error. We then validate the derived bounds empirically. We show that 1)
Learning from anatomized data approaches the limits of learning through the
unprotected data (although requiring larger training data), and 2) nearest
neighbor using anatomized data outperforms nearest neighbor on
generalization-based anonymization.
Vietnamese Named Entity Recognition using Token Regular Expressions and Bidirectional Inference
Authors: Phuong Le-Hong
Date: 2016-10-18 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper describes an efficient approach to improve the accuracy of a named
entity recognition system for Vietnamese. The approach combines regular
expressions over tokens and a bidirectional inference method in a sequence
labelling model. The proposed method achieves an overall $F_1$ score of 89.66%
on a test set of an evaluation campaign, organized in late 2016 by the
Vietnamese Language and Speech Processing (VLSP) community.
Going off the Grid: Iterative Model Selection for Biclustered Matrix Completion
Authors: Eric Chi, Liuiyi Hu, Arvind K. Saibaba, Arvind U. K. Rao
Date: 2016-10-18 00:00:00
Tasks: Matrix Completion, Model Selection

We consider the problem of performing matrix completion with side information
on row-by-row and column-by-column similarities. We build upon recent proposals
for matrix estimation with smoothness constraints with respect to row and
column graphs. We present a novel iterative procedure for directly minimizing
an information criterion in order to select an appropriate amount row and
column smoothing, namely perform model selection. We also discuss how to
exploit the special structure of the problem to scale up the estimation and
model selection procedure via the Hutchinson estimator. We present simulation
results and an application to predicting associations in imaging-genomics
studies.
Recognizing Semantic Features in Faces using Deep Learning
Authors: Amogh Gudi
Date: 2015-12-02 00:00:00
Tasks: Deep Learning

The human face constantly conveys information, both consciously and
subconsciously. However, as basic as it is for humans to visually interpret
this information, it is quite a big challenge for machines. Conventional
semantic facial feature recognition and analysis techniques are already in use
and are based on physiological heuristics, but they suffer from lack of
robustness and high computation time. This thesis aims to explore ways for
machines to learn to interpret semantic information available in faces in an
automated manner without requiring manual design of feature detectors, using
the approach of Deep Learning. This thesis provides a study of the effects of
various factors and hyper-parameters of deep neural networks in the process of
determining an optimal network configuration for the task of semantic facial
feature recognition. This thesis explores the effectiveness of the system to
recognize the various semantic features (like emotions, age, gender, ethnicity
etc.) present in faces. Furthermore, the relation between the effect of
high-level concepts on low level features is explored through an analysis of
the similarities in low-level descriptors of different semantic features. This
thesis also demonstrates a novel idea of using a deep network to generate 3-D
Active Appearance Models of faces from real-world 2-D images.
  For a more detailed report on this work, please see [arXiv:1512.00743v1].
Human Pose Estimation in Space and Time using 3D CNN
Authors: Agne Grinciunaite, Amogh Gudi, Emrah Tasli, Marten den Uyl
Date: 2016-08-31 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation

This paper explores the capabilities of convolutional neural networks to deal
with a task that is easily manageable for humans: perceiving 3D pose of a human
body from varying angles. However, in our approach, we are restricted to using
a monocular vision system. For this purpose, we apply a convolutional neural
network approach on RGB videos and extend it to three dimensional convolutions.
This is done via encoding the time dimension in videos as the 3\ts{rd}
dimension in convolutional space, and directly regressing to human body joint
positions in 3D coordinate space. This research shows the ability of such a
network to achieve state-of-the-art performance on the selected Human3.6M
dataset, thus demonstrating the possibility of successfully representing
temporal data with an additional dimension in the convolutional operation.
IDNet: Smartphone-based Gait Recognition with Convolutional Neural Networks
Authors: Matteo Gadaleta, Michele Rossi
Date: 2016-06-10 00:00:00
Tasks: Decision Making, Gait Recognition

Here, we present IDNet, a user authentication framework from
smartphone-acquired motion signals. Its goal is to recognize a target user from
their way of walking, using the accelerometer and gyroscope (inertial) signals
provided by a commercial smartphone worn in the front pocket of the user's
trousers. IDNet features several innovations including: i) a robust and
smartphone-orientation-independent walking cycle extraction block, ii) a novel
feature extractor based on convolutional neural networks, iii) a one-class
support vector machine to classify walking cycles, and the coherent integration
of these into iv) a multi-stage authentication technique. IDNet is the first
system that exploits a deep learning approach as universal feature extractors
for gait recognition, and that combines classification results from subsequent
walking cycles into a multi-stage decision making framework. Experimental
results show the superiority of our approach against state-of-the-art
techniques, leading to misclassification rates (either false negatives or
positives) smaller than 0.15% with fewer than five walking cycles. Design
choices are discussed and motivated throughout, assessing their impact on the
user authentication performance.
Clustering by connection center evolution
Authors: Xiurui Geng, Hairong Tang
Date: 2016-10-19 00:00:00
Tasks: Clustering

The determination of cluster centers generally depends on the scale that we
use to analyze the data to be clustered. Inappropriate scale usually leads to
unreasonable cluster centers and thus unreasonable results. In this study, we
first consider the similarity of elements in the data as the connectivity of
nodes in an undirected graph, then present the concept of a connection center
and regard it as the cluster center of the data. Based on this definition, the
determination of cluster centers and the assignment of class are very simple,
natural and effective. One more crucial finding is that the cluster centers of
different scales can be obtained easily by the different powers of a similarity
matrix and the change of power from small to large leads to the dynamic
evolution of cluster centers from local (microscopic) to global (microscopic).
Further, in this process of evolution, the number of categories changes
discontinuously, which means that the presented method can automatically skip
the unreasonable number of clusters, suggest appropriate observation scales and
provide corresponding cluster results.
Consistent Kernel Mean Estimation for Functions of Random Variables
Authors: Carl-Johann Simon-Gabriel, Adam Ścibior, Ilya Tolstikhin, Bernhard Schölkopf
Date: 2016-10-19 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Probabilistic Programming

We provide a theoretical foundation for non-parametric estimation of
functions of random variables using kernel mean embeddings. We show that for
any continuous function $f$, consistent estimators of the mean embedding of a
random variable $X$ lead to consistent estimators of the mean embedding of
$f(X)$. For Mat\'ern kernels and sufficiently smooth functions we also provide
rates of convergence. Our results extend to functions of multiple random
variables. If the variables are dependent, we require an estimator of the mean
embedding of their joint distribution as a starting point; if they are
independent, it is sufficient to have separate estimators of the mean
embeddings of their marginal distributions. In either case, our results cover
both mean embeddings based on i.i.d. samples as well as "reduced set"
expansions in terms of dependent expansion points. The latter serves as a
justification for using such expansions to limit memory resources when applying
the approach as a basis for probabilistic programming.
A Bayesian Approach to Estimation of Speaker Normalization Parameters
Authors: Dhananjay Ram, Debasis Kundu, Rajesh M. Hegde
Date: 2016-10-19 00:00:00
Tasks: speech-recognition, Speech Recognition

In this work, a Bayesian approach to speaker normalization is proposed to
compensate for the degradation in performance of a speaker independent speech
recognition system. The speaker normalization method proposed herein uses the
technique of vocal tract length normalization (VTLN). The VTLN parameters are
estimated using a novel Bayesian approach which utilizes the Gibbs sampler, a
special type of Markov Chain Monte Carlo method. Additionally the
hyperparameters are estimated using maximum likelihood approach. This model is
used assuming that human vocal tract can be modeled as a tube of uniform cross
section. It captures the variation in length of the vocal tract of different
speakers more effectively, than the linear model used in literature. The work
has also investigated different methods like minimization of Mean Square Error
(MSE) and Mean Absolute Error (MAE) for the estimation of VTLN parameters. Both
single pass and two pass approaches are then used to build a VTLN based speech
recognizer. Experimental results on recognition of vowels and Hindi phrases
from a medium vocabulary indicate that the Bayesian method improves the
performance by a considerable margin.
A multi-task learning model for malware classification with useful file access pattern from API call sequence
Authors: Xin Wang, Siu Ming Yiu
Date: 2016-10-19 00:00:00
Tasks: Classification, Document Classification, Feature Engineering, General Classification, Malware Classification, Malware Detection, Multi-Task Learning, Sentence, Sentence Summarization

Based on API call sequences, semantic-aware and machine learning (ML) based
malware classifiers can be built for malware detection or classification.
Previous works concentrate on crafting and extracting various features from
malware binaries, disassembled binaries or API calls via static or dynamic
analysis and resorting to ML to build classifiers. However, they tend to
involve too much feature engineering and fail to provide interpretability. We
solve these two problems with the recent advances in deep learning: 1)
RNN-based autoencoders (RNN-AEs) can automatically learn low-dimensional
representation of a malware from its raw API call sequence. 2) Multiple
decoders can be trained under different supervisions to give more information,
other than the class or family label of a malware. Inspired by the works of
document classification and automatic sentence summarization, each API call
sequence can be regarded as a sentence. In this paper, we make the first
attempt to build a multi-task malware learning model based on API call
sequences. The model consists of two decoders, one for malware classification
and one for $\emph{file access pattern}$ (FAP) generation given the API call
sequence of a malware. We base our model on the general seq2seq framework.
Experiments show that our model can give competitive classification results as
well as insightful FAP information.
An automatic bad band preremoval algorithm for hyperspectral imagery
Authors: Luyan Ji, Xiurui Geng, Yongchao Zhao, Fuxiang Wang
Date: 2016-10-19 00:00:00

For most hyperspectral remote sensing applications, removing bad bands, such
as water absorption bands, is a required preprocessing step. Currently, the
commonly applied method is by visual inspection, which is very time-consuming
and it is easy to overlook some noisy bands. In this study, we find an inherent
connection between target detection algorithms and the corrupted band removal.
As an example, for the matched filter (MF), which is the most widely used
target detection method for hyperspectral data, we present an automatic
MF-based algorithm for bad band identification. The MF detector is a filter
vector, and the resulting filter output is the sum of all bands weighted by the
MF coefficients. Therefore, we can identify bad bands only by using the MF
filter vector itself, the absolute value of whose entry accounts for the
importance of each band for the target detection. For a specific target of
interest, the bands with small MF weights correspond to the noisy or bad ones.
Based on this fact, we develop an automatic bad band preremoval algorithm by
utilizing the average absolute value of MF weights for multiple targets within
a scene. Experiments with three well known hyperspectral datasets show that our
method can always identify the water absorption and other low signal-to-noise
(SNR) bands that are usually chosen as bad bands manually.
Learning Determinantal Point Processes in Sublinear Time
Authors: Christophe Dupuy, Francis Bach
Date: 2016-10-19 00:00:00
Tasks: Document Summarization, Point Processes

We propose a new class of determinantal point processes (DPPs) which can be
manipulated for inference and parameter learning in potentially sublinear time
in the number of items. This class, based on a specific low-rank factorization
of the marginal kernel, is particularly suited to a subclass of continuous DPPs
and DPPs defined on exponentially many items. We apply this new class to
modelling text documents as sampling a DPP of sentences, and propose a
conditional maximum likelihood formulation to model topic proportions, which is
made possible with no approximation for our class of DPPs. We present an
application to document summarization with a DPP on $2^{500}$ items.
A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation
Authors: Duc Thanh Nguyen, Binh-Son Hua, Lap-Fai Yu, Sai-Kit Yeung
Date: 2016-10-19 00:00:00
Tasks: object-detection, Object Detection, Scene Segmentation, Scene Understanding

Recent advances of 3D acquisition devices have enabled large-scale
acquisition of 3D scene data. Such data, if completely and well annotated, can
serve as useful ingredients for a wide spectrum of computer vision and graphics
works such as data-driven modeling and scene understanding, object detection
and recognition. However, annotating a vast amount of 3D scene data remains
challenging due to the lack of an effective tool and/or the complexity of 3D
scenes (e.g. clutter, varying illumination conditions). This paper aims to
build a robust annotation tool that effectively and conveniently enables the
segmentation and annotation of massive 3D data. Our tool works by coupling 2D
and 3D information via an interactive framework, through which users can
provide high-level semantic annotation for objects. We have experimented our
tool and found that a typical indoor scene could be well segmented and
annotated in less than 30 minutes by using the tool, as opposed to a few hours
if done manually. Along with the tool, we created a dataset of over a hundred
3D scenes associated with complete annotations using our tool. The tool and
dataset are available at www.scenenn.net.
Sparse Quadratic Discriminant Analysis and Community Bayes
Authors: Ya Le, Trevor Hastie
Date: 2014-07-17 00:00:00
Tasks: General Classification

We develop a class of rules spanning the range between quadratic discriminant
analysis and naive Bayes, through a path of sparse graphical models. A group
lasso penalty is used to introduce shrinkage and encourage a similar pattern of
sparsity across precision matrices. It gives sparse estimates of interactions
and produces interpretable models. Inspired by the connected-components
structure of the estimated precision matrices, we propose the community Bayes
model, which partitions features into several conditional independent
communities and splits the classification problem into separate smaller ones.
The community Bayes idea is quite general and can be applied to non-Gaussian
data and likelihood-based classifiers.
Making brain-machine interfaces robust to future neural variability
Authors: David Sussillo, Sergey D. Stavisky, Jonathan C. Kao, Stephen I. Ryu, Krishna V. Shenoy
Date: 2016-10-19 00:00:00
Tasks: Decoder, Translation

A major hurdle to clinical translation of brain-machine interfaces (BMIs) is
that current decoders, which are trained from a small quantity of recent data,
become ineffective when neural recording conditions subsequently change. We
tested whether a decoder could be made more robust to future neural variability
by training it to handle a variety of recording conditions sampled from months
of previously collected data as well as synthetic training data perturbations.
We developed a new multiplicative recurrent neural network BMI decoder that
successfully learned a large variety of neural-to- kinematic mappings and
became more robust with larger training datasets. When tested with a non-human
primate preclinical BMI model, this decoder was robust under conditions that
disabled a state-of-the-art Kalman filter based decoder. These results validate
a new BMI strategy in which accumulated data history is effectively harnessed,
and may facilitate reliable daily BMI use by reducing decoder retraining
downtime.
Hit-and-Run for Sampling and Planning in Non-Convex Spaces
Authors: Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek
Date: 2016-10-19 00:00:00

We propose the Hit-and-Run algorithm for planning and sampling problems in
non-convex spaces. For sampling, we show the first analysis of the Hit-and-Run
algorithm in non-convex spaces and show that it mixes fast as long as certain
smoothness conditions are satisfied. In particular, our analysis reveals an
intriguing connection between fast mixing and the existence of smooth
measure-preserving mappings from a convex space to the non-convex space. For
planning, we show advantages of Hit-and-Run compared to state-of-the-art
planning methods such as Rapidly-Exploring Random Trees.
Surprisal-Driven Feedback in Recurrent Networks
Authors: Kamil M Rocki
Date: 2016-08-22 00:00:00

Recurrent neural nets are widely used for predicting temporal data. Their
inherent deep feedforward structure allows learning complex sequential
patterns. It is believed that top-down feedback might be an important missing
ingredient which in theory could help disambiguate similar patterns depending
on broader context. In this paper we introduce surprisal-driven recurrent
networks, which take into account past error information when making new
predictions. This is achieved by continuously monitoring the discrepancy
between most recent predictions and the actual observations. Furthermore, we
show that it outperforms other stochastic and fully deterministic approaches on
enwik8 character level prediction task achieving 1.37 BPC on the test portion
of the text.