pwc-archive/papers-with-abstracts huggingface dataset:
VHT: Vertical Hoeffding Tree
Authors: Nicolas Kourtellis, Gianmarco De Francisci Morales, Albert Bifet, Arinto Murdopo
Date: 2016-07-28 00:00:00
Tasks: BIG-bench Machine Learning

IoT Big Data requires new machine learning methods able to scale to large
size of data arriving at high speed. Decision trees are popular machine
learning models since they are very effective, yet easy to interpret and
visualize. In the literature, we can find distributed algorithms for learning
decision trees, and also streaming algorithms, but not algorithms that combine
both features. In this paper we present the Vertical Hoeffding Tree (VHT), the
first distributed streaming algorithm for learning decision trees. It features
a novel way of distributing decision trees via vertical parallelism. The
algorithm is implemented on top of Apache SAMOA, a platform for mining
distributed data streams, and thus able to run on real-world clusters. We run
several experiments to study the accuracy and throughput performance of our new
VHT algorithm, as well as its ability to scale while keeping its superior
performance with respect to non-distributed decision trees.
Multiple Testing for Neuroimaging via Hidden Markov Random Field
Authors: Hai Shu, Bin Nan, Robert Koeppe
Date: 2014-04-04 00:00:00

Traditional voxel-level multiple testing procedures in neuroimaging, mostly
$p$-value based, often ignore the spatial correlations among neighboring voxels
and thus suffer from substantial loss of power. We extend the
local-significance-index based procedure originally developed for the hidden
Markov chain models, which aims to minimize the false nondiscovery rate subject
to a constraint on the false discovery rate, to three-dimensional neuroimaging
data using a hidden Markov random field model. A generalized
expectation-maximization algorithm for maximizing the penalized likelihood is
proposed for estimating the model parameters. Extensive simulations show that
the proposed approach is more powerful than conventional false discovery rate
procedures. We apply the method to the comparison between mild cognitive
impairment, a disease status with increased risk of developing Alzheimer's or
another dementia, and normal controls in the FDG-PET imaging study of the
Alzheimer's Disease Neuroimaging Initiative.
Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery
Authors: Han Liu, Lie Wang, Tuo Zhao
Date: 2013-05-10 00:00:00
Tasks: Activity Prediction, parameter estimation, regression

We propose a calibrated multivariate regression method named CMR for fitting
high dimensional multivariate regression models. Compared with existing
methods, CMR calibrates regularization for each regression task with respect to
its noise level so that it simultaneously attains improved finite-sample
performance and tuning insensitiveness. Theoretically, we provide sufficient
conditions under which CMR achieves the optimal rate of convergence in
parameter estimation. Computationally, we propose an efficient smoothed
proximal gradient algorithm with a worst-case numerical rate of convergence
$\cO(1/\epsilon)$, where $\epsilon$ is a pre-specified accuracy of the
objective function value. We conduct thorough numerical simulations to
illustrate that CMR consistently outperforms other high dimensional
multivariate regression methods. We also apply CMR to solve a brain activity
prediction problem and find that it is as competitive as a handcrafted model
created by human experts. The R package \texttt{camel} implementing the
proposed method is available on the Comprehensive R Archive Network
\url{http://cran.r-project.org/web/packages/camel/}.
Generalized Majorization-Minimization
Authors: Sobhan Naderi Parizi, Kun He, Reza Aghajani, Stan Sclaroff, Pedro Felzenszwalb
Date: 2015-06-25 00:00:00

Non-convex optimization is ubiquitous in machine learning. Majorization-Minimization (MM) is a powerful iterative procedure for optimizing non-convex functions that works by optimizing a sequence of bounds on the function. In MM, the bound at each iteration is required to \emph{touch} the objective function at the optimizer of the previous bound. We show that this touching constraint is unnecessary and overly restrictive. We generalize MM by relaxing this constraint, and propose a new optimization framework, named Generalized Majorization-Minimization (G-MM), that is more flexible. For instance, G-MM can incorporate application-specific biases into the optimization procedure without changing the objective function. We derive G-MM algorithms for several latent variable models and show empirically that they consistently outperform their MM counterparts in optimizing non-convex objectives. In particular, G-MM algorithms appear to be less sensitive to initialization.
Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering
Authors: Arun Mallya, Svetlana Lazebnik
Date: 2016-04-16 00:00:00
Tasks: General Classification, Human-Object Interaction Detection, Multiple-choice, Multiple Instance Learning, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

This paper proposes deep convolutional network models that utilize local and
global context to make human activity label predictions in still images,
achieving state-of-the-art performance on two recent datasets with hundreds of
labels each. We use multiple instance learning to handle the lack of
supervision on the level of individual person instances, and weighted loss to
handle unbalanced training data. Further, we show how specialized features
trained on these datasets can be used to improve accuracy on the Visual
Question Answering (VQA) task, in the form of multiple choice fill-in-the-blank
questions (Visual Madlibs). Specifically, we tackle two types of questions on
person activity and person-object relationship and show improvements over
generic features trained on the ImageNet classification task.
Preterm Birth Prediction: Deriving Stable and Interpretable Rules from High Dimensional Data
Authors: Truyen Tran, Wei Luo, Dinh Phung, Jonathan Morris, Kristen Rickard, Svetha Venkatesh
Date: 2016-07-28 00:00:00
Tasks: Prediction, Sensitivity, Specificity

Preterm births occur at an alarming rate of 10-15%. Preemies have a higher
risk of infant mortality, developmental retardation and long-term disabilities.
Predicting preterm birth is difficult, even for the most experienced
clinicians. The most well-designed clinical study thus far reaches a modest
sensitivity of 18.2-24.2% at specificity of 28.6-33.3%. We take a different
approach by exploiting databases of normal hospital operations. We aims are
twofold: (i) to derive an easy-to-use, interpretable prediction rule with
quantified uncertainties, and (ii) to construct accurate classifiers for
preterm birth prediction. Our approach is to automatically generate and select
from hundreds (if not thousands) of possible predictors using stability-aware
techniques. Derived from a large database of 15,814 women, our simplified
prediction rule with only 10 items has sensitivity of 62.3% at specificity of
81.5%.
Scalable and Accurate Online Feature Selection for Big Data
Authors: Kui Yu, Xindong Wu, Wei Ding, Jian Pei
Date: 2015-11-30 00:00:00
Tasks: feature selection

Feature selection is important in many big data applications. Two critical
challenges closely associate with big data. Firstly, in many big data
applications, the dimensionality is extremely high, in millions, and keeps
growing. Secondly, big data applications call for highly scalable feature
selection algorithms in an online manner such that each feature can be
processed in a sequential scan. We present SAOLA, a Scalable and Accurate
OnLine Approach for feature selection in this paper. With a theoretical
analysis on bounds of the pairwise correlations between features, SAOLA employs
novel pairwise comparison techniques and maintain a parsimonious model over
time in an online manner. Furthermore, to deal with upcoming features that
arrive by groups, we extend the SAOLA algorithm, and then propose a new
group-SAOLA algorithm for online group feature selection. The group-SAOLA
algorithm can online maintain a set of feature groups that is sparse at the
levels of both groups and individual features simultaneously. An empirical
study using a series of benchmark real data sets shows that our two algorithms,
SAOLA and group-SAOLA, are scalable on data sets of extremely high
dimensionality, and have superior performance over the state-of-the-art feature
selection methods.
3DFS: Deformable Dense Depth Fusion and Segmentation for Object Reconstruction from a Handheld Camera
Authors: Tanmay Gupta, Daeyun Shin, Naren Sivagnanadasan, Derek Hoiem
Date: 2016-06-15 00:00:00
Tasks: 3D Reconstruction, Depth Estimation, Object, Object Reconstruction, Segmentation, Surface Reconstruction

We propose an approach for 3D reconstruction and segmentation of a single
object placed on a flat surface from an input video. Our approach is to perform
dense depth map estimation for multiple views using a proposed objective
function that preserves detail. The resulting depth maps are then fused using a
proposed implicit surface function that is robust to estimation error,
producing a smooth surface reconstruction of the entire scene. Finally, the
object is segmented from the remaining scene using a proposed 2D-3D
segmentation that incorporates image and depth cues with priors and
regularization over the 3D volume and 2D segmentations. We evaluate 3D
reconstructions qualitatively on our Object-Videos dataset, comparing to
fusion, multiview stereo, and segmentation baselines. We also quantitatively
evaluate the dense depth estimation using the RGBD Scenes V2 dataset [Henry et
al. 2013] and the segmentation using keyframe annotations of the Object-Videos
dataset.
A 58.6mW Real-Time Programmable Object Detector with Multi-Scale Multi-Object Support Using Deformable Parts Model on 1920x1080 Video at 30fps
Authors: Amr Suleiman, Zhengdong Zhang, Vivienne Sze
Date: 2016-07-27 00:00:00
Tasks: Classification, General Classification, object-detection, Object Detection, Quantization, Real-Time Object Detection

This paper presents a programmable, energy-efficient and real-time object
detection accelerator using deformable parts models (DPM), with 2x higher
accuracy than traditional rigid body models. With 8 deformable parts detection,
three methods are used to address the high computational complexity:
classification pruning for 33x fewer parts classification, vector quantization
for 15x memory size reduction, and feature basis projection for 2x reduction of
the cost of each classification. The chip is implemented in 65nm CMOS
technology, and can process HD (1920x1080) images at 30fps without any off-chip
storage while consuming only 58.6mW (0.94nJ/pixel, 1168 GOPS/W). The chip has
two classification engines to simultaneously detect two different classes of
objects. With a tested high throughput of 60fps, the classification engines can
be time multiplexed to detect even more than two object classes. It is energy
scalable by changing the pruning factor or disabling the parts classification.
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
Authors: Yandong Guo, Lei Zhang, Yuxiao Hu, Xiaodong He, Jianfeng Gao
Date: 2016-07-27 00:00:00
Tasks: Face Recognition, Image Captioning

In this paper, we design a benchmark task and provide the associated datasets
for recognizing face images and link them to corresponding entity keys in a
knowledge base. More specifically, we propose a benchmark task to recognize one
million celebrities from their face images, by using all the possibly collected
face images of this individual on the web as training data. The rich
information provided by the knowledge base helps to conduct disambiguation and
improve the recognition accuracy, and contributes to various real-world
applications, such as image captioning and news video analysis. Associated with
this task, we design and provide concrete measurement set, evaluation protocol,
as well as training data. We also present in details our experiment setup and
report promising baseline results. Our benchmark task could lead to one of the
largest classification problems in computer vision. To the best of our
knowledge, our training dataset, which contains 10M images in version 1, is the
largest publicly available one in the world.
Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016)
Authors: Been Kim, Dmitry M. Malioutov, Kush R. Varshney
Date: 2016-07-08 00:00:00
Tasks: BIG-bench Machine Learning

This is the Proceedings of the 2016 ICML Workshop on Human Interpretability
in Machine Learning (WHI 2016), which was held in New York, NY, June 23, 2016.
  Invited speakers were Susan Athey, Rich Caruana, Jacob Feldman, Percy Liang,
and Hanna Wallach.
Calorie Counter: RGB-Depth Visual Estimation of Energy Expenditure at Home
Authors: Lili Tao, Tilo Burghardt, Majid Mirmehdi, Dima Damen, Ashley Cooper, Sion Hannuna, Massimo Camplani, Adeline Paiement, Ian Craddock
Date: 2016-07-27 00:00:00

We present a new framework for vision-based estimation of calorific
expenditure from RGB-D data - the first that is validated on physical gas
exchange measurements and applied to daily living scenarios. Deriving a
person's energy expenditure from sensors is an important tool in tracking
physical activity levels for health and lifestyle monitoring. Most existing
methods use metabolic lookup tables (METs) for a manual estimate or systems
with inertial sensors which ultimately require users to wear devices. In
contrast, the proposed pose-invariant and individual-independent vision
framework allows for a remote estimation of calorific expenditure. We
introduce, and evaluate our approach on, a new dataset called SPHERE-calorie,
for which visual estimates can be compared against simultaneously obtained,
indirect calorimetry measures based on gas exchange. % based on per breath gas
exchange. We conclude from our experiments that the proposed vision pipeline is
suitable for home monitoring in a controlled environment, with calorific
expenditure estimates above accuracy levels of commonly used manual estimations
via METs. With the dataset released, our work establishes a baseline for future
research for this little-explored area of computer vision.
Android Malware Detection Using Parallel Machine Learning Classifiers
Authors: Suleiman Y. Yerima, Sakir Sezer, Igor Muttik
Date: 2016-07-27 00:00:00
Tasks: Android Malware Detection, BIG-bench Machine Learning, General Classification, Malware Detection

Mobile malware has continued to grow at an alarming rate despite on-going
efforts towards mitigating the problem. This has been particularly noticeable
on Android due to its being an open platform that has subsequently overtaken
other platforms in the share of the mobile smart devices market. Hence,
incentivizing a new wave of emerging Android malware sophisticated enough to
evade most common detection methods. This paper proposes and investigates a
parallel machine learning based classification approach for early detection of
Android malware. Using real malware samples and benign applications, a
composite classification model is developed from parallel combination of
heterogeneous classifiers. The empirical evaluation of the model under
different combination schemes demonstrates its efficacy and potential to
improve detection accuracy. More importantly, by utilizing several classifiers
with diverse characteristics, their strengths can be harnessed not only for
enhanced Android malware detection but also quicker white box analysis by means
of the more interpretable constituent classifiers.
Psychologically inspired planning method for smart relocation task
Authors: Aleksandr I. Panov, Konstantin Yakovlev
Date: 2016-07-27 00:00:00

Behavior planning is known to be one of the basic cognitive functions, which
is essential for any cognitive architecture of any control system used in
robotics. At the same time most of the widespread planning algorithms employed
in those systems are developed using only approaches and models of Artificial
Intelligence and don't take into account numerous results of cognitive
experiments. As a result, there is a strong need for novel methods of behavior
planning suitable for modern cognitive architectures aimed at robot control.
One such method is presented in this work and is studied within a special class
of navigation task called smart relocation task. The method is based on the
hierarchical two-level model of abstraction and knowledge representation, e.g.
symbolic and subsymbolic. On the symbolic level sign world model is used for
knowledge representation and hierarchical planning algorithm, PMA, is utilized
for planning. On the subsymbolic level the task of path planning is considered
and solved as a graph search problem. Interaction between both planners is
examined and inter-level interfaces and feedback loops are described.
Preliminary experimental results are presented.
N-opcode Analysis for Android Malware Classification and Categorization
Authors: BooJoong Kang, Suleiman Y. Yerima, Kieran McLaughlin, Sakir Sezer
Date: 2016-07-27 00:00:00
Tasks: Classification, General Classification, Malware Classification, Malware Detection

Malware detection is a growing problem particularly on the Android mobile
platform due to its increasing popularity and accessibility to numerous third
party app markets. This has also been made worse by the increasingly
sophisticated detection avoidance techniques employed by emerging malware
families. This calls for more effective techniques for detection and
classification of Android malware. Hence, in this paper we present an n-opcode
analysis based approach that utilizes machine learning to classify and
categorize Android malware. This approach enables automated feature discovery
that eliminates the need for applying expert or domain knowledge to define the
needed features. Our experiments on 2520 samples that were performed using up
to 10-gram opcode features showed that an f-measure of 98% is achievable using
this approach.
Adaptive foveated single-pixel imaging with dynamic super-sampling
Authors: David B. Phillips, Ming-Jie Sun, Jonathan M. Taylor, Matthew P. Edgar, Stephen M. Barnett, Graham G. Gibson, Miles J. Padgett
Date: 2016-07-27 00:00:00
Tasks: Compressive Sensing

As an alternative to conventional multi-pixel cameras, single-pixel cameras
enable images to be recorded using a single detector that measures the
correlations between the scene and a set of patterns. However, to fully sample
a scene in this way requires at least the same number of correlation
measurements as there are pixels in the reconstructed image. Therefore
single-pixel imaging systems typically exhibit low frame-rates. To mitigate
this, a range of compressive sensing techniques have been developed which rely
on a priori knowledge of the scene to reconstruct images from an under-sampled
set of measurements. In this work we take a different approach and adopt a
strategy inspired by the foveated vision systems found in the animal kingdom -
a framework that exploits the spatio-temporal redundancy present in many
dynamic scenes. In our single-pixel imaging system a high-resolution foveal
region follows motion within the scene, but unlike a simple zoom, every frame
delivers new spatial information from across the entire field-of-view. Using
this approach we demonstrate a four-fold reduction in the time taken to record
the detail of rapidly evolving features, whilst simultaneously accumulating
detail of more slowly evolving regions over several consecutive frames. This
tiered super-sampling technique enables the reconstruction of video streams in
which both the resolution and the effective exposure-time spatially vary and
adapt dynamically in response to the evolution of the scene. The methods
described here can complement existing compressive sensing approaches and may
be applied to enhance a variety of computational imagers that rely on
sequential correlation measurements.
Neuromorphic Robot Dream
Authors: Alexander Tchitchigin, Max Talanov, Larisa Safina, Manuel Mazzara
Date: 2016-07-27 00:00:00

In this paper we present the next step in our approach to neurobiologically
plausible implementation of emotional reactions and behaviors for real-time
autonomous robotic systems. The working metaphor we use is the "day" and the
"night" phases of mammalian life. During the "day phase" a robotic system
stores the inbound information and is controlled by a light-weight rule-based
system in real time. In contrast to that, during the "night phase" information
that has been stored is transferred to a supercomputing system to update the
realistic neural network: emotional and behavioral strategies.
Spatial probabilistic pulsatility model for enhancing photoplethysmographic imaging systems
Authors: Robert Amelard, David A. Clausi, Alexander Wong
Date: 2016-07-27 00:00:00
Tasks: Density Estimation, Heart rate estimation

Photolethysmographic imaging (PPGI) is a widefield non-contact biophotonic
technology able to remotely monitor cardiovascular function over anatomical
areas. Though spatial context can provide increased physiological insight,
existing PPGI systems rely on coarse spatial averaging with no anatomical
priors for assessing arterial pulsatility. Here, we developed a continuous
probabilistic pulsatility model for importance-weighted blood pulse waveform
extraction. Using a data-driven approach, the model was constructed using a 23
participant sample with large demographic variation (11/12 female/male, age
11-60 years, BMI 16.4-35.1 kg$\cdot$m$^{-2}$). Using time-synchronized
ground-truth waveforms, spatial correlation priors were computed and projected
into a co-aligned importance-weighted Cartesian space. A modified
Parzen-Rosenblatt kernel density estimation method was used to compute the
continuous resolution-agnostic probabilistic pulsatility model. The model
identified locations that consistently exhibited pulsatility across the sample.
Blood pulse waveform signals extracted with the model exhibited significantly
stronger temporal correlation ($W=35,p<0.01$) and spectral SNR ($W=31,p<0.01$)
compared to uniform spatial averaging. Heart rate estimation was in strong
agreement with true heart rate ($r^2=0.9619$, error $(\mu,\sigma)=(0.52,1.69)$
bpm).
Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
Authors: Federica Bogo, Angjoo Kanazawa, Christoph Lassner, Peter Gehler, Javier Romero, Michael J. Black
Date: 2016-07-27 00:00:00
Tasks: 3D Human Pose Estimation

We describe the first method to automatically estimate the 3D pose of the
human body as well as its 3D shape from a single unconstrained image. We
estimate a full 3D mesh and show that 2D joints alone carry a surprising amount
of information about body shape. The problem is challenging because of the
complexity of the human body, articulation, occlusion, clothing, lighting, and
the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a
recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D
body joint locations. We then fit (top-down) a recently published statistical
body shape model, called SMPL, to the 2D joints. We do so by minimizing an
objective function that penalizes the error between the projected 3D model
joints and detected 2D joints. Because SMPL captures correlations in human
shape across the population, we are able to robustly fit it to very little
data. We further leverage the 3D model to prevent solutions that cause
interpenetration. We evaluate our method, SMPLify, on the Leeds Sports,
HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect
to the state of the art.