pwc-archive/papers-with-abstracts huggingface dataset:
Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction
Authors: Voot Tangkaratt, Hiroaki Sasaki, Masashi Sugiyama
Date: 2015-08-05 00:00:00
Tasks: Dimensionality Reduction

A typical goal of supervised dimension reduction is to find a low-dimensional
subspace of the input space such that the projected input variables preserve
maximal information about the output variables. The dependence maximization
approach solves the supervised dimension reduction problem through maximizing a
statistical dependence between projected input variables and output variables.
A well-known statistical dependence measure is mutual information (MI) which is
based on the Kullback-Leibler (KL) divergence. However, it is known that the KL
divergence is sensitive to outliers. On the other hand, quadratic MI (QMI) is a
variant of MI based on the $L_2$ distance which is more robust against outliers
than the KL divergence, and a computationally efficient method to estimate QMI
from data, called least-squares QMI (LSQMI), has been proposed recently. For
these reasons, developing a supervised dimension reduction method based on
LSQMI seems promising. However, not QMI itself, but the derivative of QMI is
needed for subspace search in supervised dimension reduction, and the
derivative of an accurate QMI estimator is not necessarily a good estimator of
the derivative of QMI. In this paper, we propose to directly estimate the
derivative of QMI without estimating QMI itself. We show that the direct
estimation of the derivative of QMI is more accurate than the derivative of the
estimated QMI. Finally, we develop a supervised dimension reduction algorithm
which efficiently uses the proposed derivative estimator, and demonstrate
through experiments that the proposed method is more robust against outliers
than existing methods.
Learning from LDA using Deep Neural Networks
Authors: Dongxu Zhang, Tianyi Luo, Dong Wang, Rong Liu
Date: 2015-08-05 00:00:00
Tasks: Document Classification, General Classification, Transfer Learning

Latent Dirichlet Allocation (LDA) is a three-level hierarchical Bayesian
model for topic inference. In spite of its great success, inferring the latent
topic distribution with LDA is time-consuming. Motivated by the transfer
learning approach proposed by~\newcite{hinton2015distilling}, we present a
novel method that uses LDA to supervise the training of a deep neural network
(DNN), so that the DNN can approximate the costly LDA inference with less
computation. Our experiments on a document classification task show that a
simple DNN can learn the LDA behavior pretty well, while the inference is
speeded up tens or hundreds of times.
INsight: A Neuromorphic Computing System for Evaluation of Large Neural Networks
Authors: Jaeyong Chung, Taehwan Shin, Yongshin Kang
Date: 2015-08-05 00:00:00
Tasks: General Classification, image-classification, Image Classification, object-detection, Object Detection

Deep neural networks have been demonstrated impressive results in various
cognitive tasks such as object detection and image classification. In order to
execute large networks, Von Neumann computers store the large number of weight
parameters in external memories, and processing elements are timed-shared,
which leads to power-hungry I/O operations and processing bottlenecks. This
paper describes a neuromorphic computing system that is designed from the
ground up for the energy-efficient evaluation of large-scale neural networks.
The computing system consists of a non-conventional compiler, a neuromorphic
architecture, and a space-efficient microarchitecture that leverages existing
integrated circuit design methodologies. The compiler factorizes a trained,
feedforward network into a sparsely connected network, compresses the weights
linearly, and generates a time delay neural network reducing the number of
connections. The connections and units in the simplified network are mapped to
silicon synapses and neurons. We demonstrate an implementation of the
neuromorphic computing system based on a field-programmable gate array that
performs the MNIST hand-written digit classification with 97.64% accuracy.
On the Linear Belief Compression of POMDPs: A re-examination of current methods
Authors: Zhuoran Wang, Paul A. Crook, Wenshuo Tang, Oliver Lemon
Date: 2015-08-05 00:00:00
Tasks: Heuristic Search

Belief compression improves the tractability of large-scale partially
observable Markov decision processes (POMDPs) by finding projections from
high-dimensional belief space onto low-dimensional approximations, where
solving to obtain action selection policies requires fewer computations. This
paper develops a unified theoretical framework to analyse three existing linear
belief compression approaches, including value-directed compression and two
non-negative matrix factorisation (NMF) based algorithms. The results indicate
that all the three known belief compression methods have their own critical
deficiencies. Therefore, projective NMF belief compression is proposed (P-NMF),
aiming to overcome the drawbacks of the existing techniques. The performance of
the proposed algorithm is examined on four POMDP problems of reasonably large
scale, in comparison with existing techniques. Additionally, the
competitiveness of belief compression is compared empirically to a
state-of-the-art heuristic search based POMDP solver and their relative merits
in solving large-scale POMDPs are investigated.
Dimension Reduction with Non-degrading Generalization
Authors: Pitoyo Hartono
Date: 2015-08-05 00:00:00
Tasks: Data Visualization, Dimensionality Reduction, General Classification

Visualizing high dimensional data by projecting them into two or three
dimensional space is one of the most effective ways to intuitively understand
the data's underlying characteristics, for example their class neighborhood
structure. While data visualization in low dimensional space can be efficient
for revealing the data's underlying characteristics, classifying a new sample
in the reduced-dimensional space is not always beneficial because of the loss
of information in expressing the data. It is possible to classify the data in
the high dimensional space, while visualizing them in the low dimensional
space, but in this case, the visualization is often meaningless because it
fails to illustrate the underlying characteristics that are crucial for the
classification process.
  In this paper, the performance-preserving property of the previously proposed
Restricted Radial Basis Function Network in reducing the dimension of labeled
data is explained. Here, it is argued through empirical experiments that the
internal representation of the Restricted Radial Basis Function Network, which
during the supervised learning process organizes a visualizable two dimensional
map, does not only preserve the topographical structure of high dimensional
data but also captures their class neighborhood structures that are important
for classifying them. Hence, unlike many of the existing dimension reduction
methods, the Restricted Radial Basis Function Network offers two dimensional
visualization that is strongly correlated with the classification process.
Progressive EM for Latent Tree Models and Hierarchical Topic Detection
Authors: Peixian Chen, Nevin L. Zhang, Leonard K. M. Poon, Zhourong Chen
Date: 2015-08-05 00:00:00
Tasks: parameter estimation

Hierarchical latent tree analysis (HLTA) is recently proposed as a new method
for topic detection. It differs fundamentally from the LDA-based methods in
terms of topic definition, topic-document relationship, and learning method. It
has been shown to discover significantly more coherent topics and better topic
hierarchies. However, HLTA relies on the Expectation-Maximization (EM)
algorithm for parameter estimation and hence is not efficient enough to deal
with large datasets. In this paper, we propose a method to drastically speed up
HLTA using a technique inspired by recent advances in the moments method.
Empirical experiments show that our method greatly improves the efficiency of
HLTA. It is as efficient as the state-of-the-art LDA-based method for
hierarchical topic detection and finds substantially better topics and topic
hierarchies.
3D Automatic Segmentation Method for Retinal Optical Coherence Tomography Volume Data Using Boundary Surface Enhancement
Authors: Yankui Sun, Tian Zhang, Yue Zhao, Yufan He
Date: 2015-08-05 00:00:00
Tasks: Segmentation

With the introduction of spectral-domain optical coherence tomography
(SDOCT), much larger image datasets are routinely acquired compared to what was
possible using the previous generation of time-domain OCT. Thus, there is a
critical need for the development of 3D segmentation methods for processing
these data. We present here a novel 3D automatic segmentation method for
retinal OCT volume data. Briefly, to segment a boundary surface, two OCT volume
datasets are obtained by using a 3D smoothing filter and a 3D differential
filter. Their linear combination is then calculated to generate new volume data
with an enhanced boundary surface, where pixel intensity, boundary position
information, and intensity changes on both sides of the boundary surface are
used simultaneously. Next, preliminary discrete boundary points are detected
from the A-Scans of the volume data. Finally, surface smoothness constraints
and a dynamic threshold are applied to obtain a smoothed boundary surface by
correcting a small number of error points. Our method can extract retinal layer
boundary surfaces sequentially with a decreasing search region of volume data.
We performed automatic segmentation on eight human OCT volume datasets acquired
from a commercial Spectralis OCT system, where each volume of data consisted of
97 OCT images with a resolution of 496 512; experimental results show that this
method can accurately segment seven layer boundary surfaces in normal as well
as some abnormal eyes.
Distant Supervision for Entity Linking
Authors: Miao Fan, Qiang Zhou, Thomas Fang Zheng
Date: 2015-05-14 00:00:00
Proceeding: PACLIC 2015 10
Tasks: Descriptive, Entity Linking

Entity linking is an indispensable operation of populating knowledge
repositories for information extraction. It studies on aligning a textual
entity mention to its corresponding disambiguated entry in a knowledge
repository. In this paper, we propose a new paradigm named distantly supervised
entity linking (DSEL), in the sense that the disambiguated entities that belong
to a huge knowledge repository (Freebase) are automatically aligned to the
corresponding descriptive webpages (Wiki pages). In this way, a large scale of
weakly labeled data can be generated without manual annotation and fed to a
classifier for linking more newly discovered entities. Compared with
traditional paradigms based on solo knowledge base, DSEL benefits more via
jointly leveraging the respective advantages of Freebase and Wikipedia.
Specifically, the proposed paradigm facilitates bridging the disambiguated
labels (Freebase) of entities and their textual descriptions (Wikipedia) for
Web-scale entities. Experiments conducted on a dataset of 140,000 items and
60,000 features achieve a baseline F1-measure of 0.517. Furthermore, we analyze
the feature performance and improve the F1-measure to 0.545.
Asynchronous stochastic convex optimization
Authors: John C. Duchi, Sorathan Chaturapruek, Christopher Ré
Date: 2015-08-04 00:00:00
Tasks: Stochastic Optimization

We show that asymptotically, completely asynchronous stochastic gradient
procedures achieve optimal (even to constant factors) convergence rates for the
solution of convex optimization problems under nearly the same conditions
required for asymptotic optimality of standard stochastic gradient procedures.
Roughly, the noise inherent to the stochastic approximation scheme dominates
any noise from asynchrony. We also give empirical evidence demonstrating the
strong performance of asynchronous, parallel stochastic optimization schemes,
demonstrating that the robustness inherent to stochastic approximation problems
allows substantially faster parallel and asynchronous solution methods.
Qualitative Decision Methods for Multi-Attribute Decision Making
Authors: Ankit Agrawal
Date: 2015-08-04 00:00:00
Tasks: Attribute, Decision Making

The fundamental problem underlying all multi-criteria decision analysis
(MCDA) problems is that of dominance between any two alternatives: "Given two
alternatives A and B, each described by a set criteria, is A preferred to B
with respect to a set of decision maker (DM) preferences over the criteria?".
Depending on the application in which MCDA is performed, the alternatives may
represent strategies and policies for business, potential locations for setting
up new facilities, designs of buildings, etc. The general objective of MCDA is
to enable the DM to order all alternatives in order of the stated preferences,
and choose the ones that are best, i.e., optimal with respect to the
preferences over the criteria. This article presents and summarizes a recently
developed MCDA framework that orders the set of alternatives when the relative
importance preferences are incomplete, imprecise, or qualitative in nature.
A Deep-structured Conditional Random Field Model for Object Silhouette Tracking
Authors: Mohammad Shafiee, Zohreh Azimifar, Alexander Wong
Date: 2015-01-05 00:00:00
Tasks: Object, Optical Flow Estimation

In this work, we introduce a deep-structured conditional random field
(DS-CRF) model for the purpose of state-based object silhouette tracking. The
proposed DS-CRF model consists of a series of state layers, where each state
layer spatially characterizes the object silhouette at a particular point in
time. The interactions between adjacent state layers are established by
inter-layer connectivity dynamically determined based on inter-frame optical
flow. By incorporate both spatial and temporal context in a dynamic fashion
within such a deep-structured probabilistic graphical model, the proposed
DS-CRF model allows us to develop a framework that can accurately and
efficiently track object silhouettes that can change greatly over time, as well
as under different situations such as occlusion and multiple targets within the
scene. Experiment results using video surveillance datasets containing
different scenarios such as occlusion and multiple targets showed that the
proposed DS-CRF approach provides strong object silhouette tracking performance
when compared to baseline methods such as mean-shift tracking, as well as
state-of-the-art methods such as context tracking and boosted particle
filtering.
Semantic Pose using Deep Networks Trained on Synthetic RGB-D
Authors: Jeremie Papon, Markus Schoeler
Date: 2015-08-04 00:00:00
Proceeding: ICCV 2015 12
Tasks: GPU, Scene Understanding, Transfer Learning

In this work we address the problem of indoor scene understanding from RGB-D
images. Specifically, we propose to find instances of common furniture classes,
their spatial extent, and their pose with respect to generalized class models.
To accomplish this, we use a deep, wide, multi-output convolutional neural
network (CNN) that predicts class, pose, and location of possible objects
simultaneously. To overcome the lack of large annotated RGB-D training sets
(especially those with pose), we use an on-the-fly rendering pipeline that
generates realistic cluttered room scenes in parallel to training. We then
perform transfer learning on the relatively small amount of publicly available
annotated RGB-D data, and find that our model is able to successfully annotate
even highly challenging real scenes. Importantly, our trained network is able
to understand noisy and sparse observations of highly cluttered scenes with a
remarkable degree of accuracy, inferring class and pose from a very limited set
of cues. Additionally, our neural network is only moderately deep and computes
class, pose and position in tandem, so the overall run-time is significantly
faster than existing methods, estimating all output parameters simultaneously
in parallel on a GPU in seconds.
Identifying Avatar Aliases in Starcraft 2
Authors: Olivier Cavadenti, Victor Codocedo, Jean-François Boulicaut, Mehdi Kaytoue
Date: 2015-08-04 00:00:00
Tasks: Starcraft

In electronic sports, cyberathletes conceal their online training using
different avatars (virtual identities), allowing them not being recognized by
the opponents they may face in future competitions. In this article, we propose
a method to tackle this avatar aliases identification problem. Our method
trains a classifier on behavioural data and processes the confusion matrix to
output label pairs which concentrate confusion. We experimented with Starcraft
2 and report our first results.
Object localization in ImageNet by looking out of the window
Authors: Alexander Vezhnevets, Vittorio Ferrari
Date: 2015-01-06 00:00:00
Tasks: General Classification, Object Localization

We propose a method for annotating the location of objects in ImageNet.
Traditionally, this is cast as an image window classification problem, where
each window is considered independently and scored based on its appearance
alone. Instead, we propose a method which scores each candidate window in the
context of all other windows in the image, taking into account their similarity
in appearance space as well as their spatial relations in the image plane. We
devise a fast and exact procedure to optimize our scoring function over all
candidate windows in an image, and we learn its parameters using structured
output regression. We demonstrate on 92000 images from ImageNet that this
significantly improves localization over recent techniques that score windows
in isolation.
Online Domain Adaptation for Multi-Object Tracking
Authors: Adrien Gaidon, Eleonora Vig
Date: 2015-08-04 00:00:00
Tasks: Domain Adaptation, Multi-Object Tracking, Multi-Task Learning, Object, Object Tracking, Online Domain Adaptation

Automatically detecting, labeling, and tracking objects in videos depends
first and foremost on accurate category-level object detectors. These might,
however, not always be available in practice, as acquiring high-quality large
scale labeled training datasets is either too costly or impractical for all
possible real-world application scenarios. A scalable solution consists in
re-using object detectors pre-trained on generic datasets. This work is the
first to investigate the problem of on-line domain adaptation of object
detectors for causal multi-object tracking (MOT). We propose to alleviate the
dataset bias by adapting detectors from category to instances, and back: (i) we
jointly learn all target models by adapting them from the pre-trained one, and
(ii) we also adapt the pre-trained model on-line. We introduce an on-line
multi-task learning algorithm to efficiently share parameters and reduce drift,
while gradually improving recall. Our approach is applicable to any linear
object detector, and we evaluate both cheap "mini-Fisher Vectors" and expensive
"off-the-shelf" ConvNet features. We quantitatively measure the benefit of our
domain adaptation strategy on the KITTI tracking benchmark and on a new dataset
(PASCAL-to-KITTI) we introduce to study the domain mismatch problem in MOT.
Recognition of Emotions using Kinects
Authors: Shun Li, Changye Zhu, Liqing Cui, Nan Zhao, Baobin Li, Tingshao Zhu
Date: 2015-08-04 00:00:00
Tasks: Emotion Recognition

Psychological studies indicate that emotional states are expressed in the way
people walk and the human gait is investigated in terms of its ability to
reveal a person's emotional state. And Microsoft Kinect is a rapidly
developing, inexpensive, portable and no-marker motion capture system. This
paper gives a new referable method to do emotion recognition, by using
Microsoft Kinect to do gait pattern analysis, which has not been reported. $59$
subjects are recruited in this study and their gait patterns are record by two
Kinect cameras. Significant joints selecting, Coordinate system transforming,
Slider window gauss filter, Differential operation, and Data segmentation are
used in data preprocessing. Feature extracting is based on Fourier
transformation. By using the NaiveBayes, RandomForests, libSVM and SMO
classification, the recognition rate of natural and unnatural emotions can
reach above 70%.It is concluded that using the Kinect system can be a new
method in recognition of emotions.
Predicting respiratory motion for real-time tumour tracking in radiotherapy
Authors: Tomas Krilavicius, Indre Zliobaite, Henrikas Simonavicius, Laimonas Jarusevicius
Date: 2015-08-04 00:00:00
Tasks: motion prediction, Position

Purpose. Radiation therapy is a local treatment aimed at cells in and around
a tumor. The goal of this study is to develop an algorithmic solution for
predicting the position of a target in 3D in real time, aiming for the short
fixed calibration time for each patient at the beginning of the procedure.
Accurate predictions of lung tumor motion are expected to improve the precision
of radiation treatment by controlling the position of a couch or a beam in
order to compensate for respiratory motion during radiation treatment.
  Methods. For developing the algorithmic solution, data mining techniques are
used. A model form from the family of exponential smoothing is assumed, and the
model parameters are fitted by minimizing the absolute disposition error, and
the fluctuations of the prediction signal (jitter). The predictive performance
is evaluated retrospectively on clinical datasets capturing different behavior
(being quiet, talking, laughing), and validated in real-time on a prototype
system with respiratory motion imitation.
  Results. An algorithmic solution for respiratory motion prediction (called
ExSmi) is designed. ExSmi achieves good accuracy of prediction (error $4-9$
mm/s) with acceptable jitter values (5-7 mm/s), as tested on out-of-sample
data. The datasets, the code for algorithms and the experiments are openly
available for research purposes on a dedicated website.
  Conclusions. The developed algorithmic solution performs well to be
prototyped and deployed in applications of radiotherapy.
Multi-Label Active Learning from Crowds
Authors: Shao-Yuan Li, Yuan Jiang, Zhi-Hua Zhou
Date: 2015-08-04 00:00:00
Tasks: Active Learning, Diversity

Multi-label active learning is a hot topic in reducing the label cost by
optimally choosing the most valuable instance to query its label from an
oracle. In this paper, we consider the poolbased multi-label active learning
under the crowdsourcing setting, where during the active query process, instead
of resorting to a high cost oracle for the ground-truth, multiple low cost
imperfect annotators with various expertise are available for labeling. To deal
with this problem, we propose the MAC (Multi-label Active learning from Crowds)
approach which incorporate the local influence of label correlations to build a
probabilistic model over the multi-label classifier and annotators. Based on
this model, we can estimate the labels for instances as well as the expertise
of each annotator. Then we propose the instance selection and annotator
selection criteria that consider the uncertainty/diversity of instances and the
reliability of annotators, such that the most reliable annotator will be
queried for the most valuable instances. Experimental results demonstrate the
effectiveness of the proposed approach.