pwc-archive/papers-with-abstracts huggingface dataset:
Preoperative Volume Determination for Pituitary Adenoma
Authors: Dzenan Zukic, Jan Egger, Miriam H. A. Bauer, Daniela Kuhnt, Barbara Carl, Bernd Freisleben, Andreas Kolb, Christopher Nimsky
Date: 2016-02-05 00:00:00
Tasks: Segmentation

The most common sellar lesion is the pituitary adenoma, and sellar tumors are
approximately 10-15% of all intracranial neoplasms. Manual slice-by-slice
segmentation takes quite some time that can be reduced by using the appropriate
algorithms. In this contribution, we present a segmentation method for
pituitary adenoma. The method is based on an algorithm that we have applied
recently to segmenting glioblastoma multiforme. A modification of this scheme
is used for adenoma segmentation that is much harder to perform, due to lack of
contrast-enhanced boundaries. In our experimental evaluation, neurosurgeons
performed manual slice-by-slice segmentation of ten magnetic resonance imaging
(MRI) cases. The segmentations were compared to the segmentation results of the
proposed method using the Dice Similarity Coefficient (DSC). The average DSC
for all datasets was 75.92% +/- 7.24%. A manual segmentation took about four
minutes and our algorithm required about one second.
Word Representations, Tree Models and Syntactic Functions
Authors: Simon Šuster, Gertjan van Noord, Ivan Titov
Date: 2015-08-31 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Representation Learning

Word representations induced from models with discrete latent variables
(e.g.\ HMMs) have been shown to be beneficial in many NLP applications. In this
work, we exploit labeled syntactic dependency trees and formalize the induction
problem as unsupervised learning of tree-structured hidden Markov models.
Syntactic functions are used as additional observed variables in the model,
influencing both transition and emission components. Such syntactic information
can potentially lead to capturing more fine-grain and functional distinctions
between words, which, in turn, may be desirable in many NLP applications. We
evaluate the word representations on two tasks -- named entity recognition and
semantic frame identification. We observe improvements from exploiting
syntactic function information in both cases, and the results rivaling those of
state-of-the-art representation learning methods. Additionally, we revisit the
relationship between sequential and unlabeled-tree models and find that the
advantage of the latter is not self-evident.
Wayfinding and cognitive maps for pedestrian models
Authors: Erik Andresen, David Haensel, Mohcine Chraibi, Armin Seyfried
Date: 2016-02-05 00:00:00
Tasks: Position

Usually, routing models in pedestrian dynamics assume that agents have
fulfilled and global knowledge about the building's structure. However, they
neglect the fact that pedestrians possess no or only parts of information about
their position relative to final exits and possible routes leading to them. To
get a more realistic description we introduce the systematics of gathering and
using spatial knowledge. A new wayfinding model for pedestrian dynamics is
proposed. The model defines for every pedestrian an individual knowledge
representation implying inaccuracies and uncertainties. In addition,
knowledge-driven search strategies are introduced. The presented concept is
tested on a fictive example scenario.
Automatic and Quantitative evaluation of attribute discovery methods
Authors: Liangchen Liu, Arnold Wiliem, Shaokang Chen, Brian C. Lovell
Date: 2016-02-05 00:00:00
Tasks: Attribute, image-classification, Image Classification

Many automatic attribute discovery methods have been developed to extract a
set of visual attributes from images for various tasks. However, despite good
performance in some image classification tasks, it is difficult to evaluate
whether these methods discover meaningful attributes and which one is the best
to find the attributes for image descriptions. An intuitive way to evaluate
this is to manually verify whether consistent identifiable visual concepts
exist to distinguish between positive and negative images of an attribute. This
manual checking is tedious, labor intensive and expensive and it is very hard
to get quantitative comparisons between different methods. In this work, we
tackle this problem by proposing an attribute meaningfulness metric, that can
perform automatic evaluation on the meaningfulness of attribute sets as well as
achieving quantitative comparisons. We apply our proposed metric to recent
automatic attribute discovery methods and popular hashing methods on three
attribute datasets. A user study is also conducted to validate the
effectiveness of the metric. In our evaluation, we gleaned some insights that
could be beneficial in developing automatic attribute discovery methods to
generate meaningful attributes. To the best of our knowledge, this is the first
work to quantitatively measure the semantic content of automatically discovered
attributes.
Bags of Affine Subspaces for Robust Object Tracking
Authors: Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash T. Harandi
Date: 2014-08-11 00:00:00
Tasks: Object, Object Tracking

We propose an adaptive tracking algorithm where the object is modelled as a
continuously updated bag of affine subspaces, with each subspace constructed
from the object's appearance over several consecutive frames. In contrast to
linear subspaces, affine subspaces explicitly model the origin of subspaces.
Furthermore, instead of using a brittle point-to-subspace distance during the
search for the object in a new frame, we propose to use a subspace-to-subspace
distance by representing candidate image areas also as affine subspaces.
Distances between subspaces are then obtained by exploiting the non-Euclidean
geometry of Grassmann manifolds. Experiments on challenging videos (containing
object occlusions, deformations, as well as variations in pose and
illumination) indicate that the proposed method achieves higher tracking
accuracy than several recent discriminative trackers.
Fantastic 4 system for NIST 2015 Language Recognition Evaluation
Authors: Kong Aik Lee, Ville Hautamäki, Anthony Larcher, Wei Rao, Hanwu Sun, Trung Hieu Nguyen, Guangsen Wang, Aleksandr Sizov, Ivan Kukanov, Amir Poorjam, Trung Ngo Trong, Xiong Xiao, Cheng-Lin Xu, Hai-Hua Xu, Bin Ma, Haizhou Li, Sylvain Meignier
Date: 2016-02-05 00:00:00
Tasks: regression

This article describes the systems jointly submitted by Institute for
Infocomm (I$^2$R), the Laboratoire d'Informatique de l'Universit\'e du Maine
(LIUM), Nanyang Technology University (NTU) and the University of Eastern
Finland (UEF) for 2015 NIST Language Recognition Evaluation (LRE). The
submitted system is a fusion of nine sub-systems based on i-vectors extracted
from different types of features. Given the i-vectors, several classifiers are
adopted for the language detection task including support vector machines
(SVM), multi-class logistic regression (MCLR), Probabilistic Linear
Discriminant Analysis (PLDA) and Deep Neural Networks (DNN).
On Feature based Delaunay Triangulation for Palmprint Recognition
Authors: Zanobya N. Khan, Rashid Jalal Qureshi, Jamil Ahmad
Date: 2016-02-05 00:00:00
Tasks: Binarization

Authentication of individuals via palmprint based biometric system is
becoming very popular due to its reliability as it contains unique and stable
features. In this paper, we present a novel approach for palmprint recognition
and its representation. To extract the palm lines, local thresholding technique
Niblack binarization algorithm is adopted. The endpoints of these lines are
determined and a connection is created among them using the Delaunay
triangulation thereby generating a distinct topological structure of each
palmprint. Next, we extract different geometric as well as quantitative
features from the triangles of the Delaunay triangulation that assist in
identifying different individuals. To ensure that the proposed approach is
invariant to rotation and scaling, features were made relative to topological
and geometrical structure of the palmprint. The similarity of the two
palmprints is computed using the weighted sum approach and compared with the
k-nearest neighbor. The experimental results obtained reflect the effectiveness
of the proposed approach to discriminate between different palmprint images and
thus achieved a recognition rate of 90% over large databases.
Fast Multiplier Methods to Optimize Non-exhaustive, Overlapping Clustering
Authors: Yangyang Hou, Joyce Jiyoung Whang, David F. Gleich, Inderjit S. Dhillon
Date: 2016-02-05 00:00:00
Tasks: Clustering

Clustering is one of the most fundamental and important tasks in data mining.
Traditional clustering algorithms, such as K-means, assign every data point to
exactly one cluster. However, in real-world datasets, the clusters may overlap
with each other. Furthermore, often, there are outliers that should not belong
to any cluster. We recently proposed the NEO-K-Means (Non-Exhaustive,
Overlapping K-Means) objective as a way to address both issues in an integrated
fashion. Optimizing this discrete objective is NP-hard, and even though there
is a convex relaxation of the objective, straightforward convex optimization
approaches are too expensive for large datasets. A practical alternative is to
use a low-rank factorization of the solution matrix in the convex formulation.
The resulting optimization problem is non-convex, and we can locally optimize
the objective function using an augmented Lagrangian method. In this paper, we
consider two fast multiplier methods to accelerate the convergence of an
augmented Lagrangian scheme: a proximal method of multipliers and an
alternating direction method of multipliers (ADMM). For the proximal augmented
Lagrangian or proximal method of multipliers, we show a convergence result for
the non-convex case with bound-constrained subproblems. These methods are up to
13 times faster---with no change in quality---compared with a standard
augmented Lagrangian method on problems with over 10,000 variables and bring
runtimes down from over an hour to around 5 minutes.
Optimizing affinity-based binary hashing using auxiliary coordinates
Authors: Ramin Raziperchikolaei, Miguel Á. Carreira-Perpiñán
Date: 2015-01-21 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Image Retrieval, Retrieval

In supervised binary hashing, one wants to learn a function that maps a
high-dimensional feature vector to a vector of binary codes, for application to
fast image retrieval. This typically results in a difficult optimization
problem, nonconvex and nonsmooth, because of the discrete variables involved.
Much work has simply relaxed the problem during training, solving a continuous
optimization, and truncating the codes a posteriori. This gives reasonable
results but is quite suboptimal. Recent work has tried to optimize the
objective directly over the binary codes and achieved better results, but the
hash function was still learned a posteriori, which remains suboptimal. We
propose a general framework for learning hash functions using affinity-based
loss functions that uses auxiliary coordinates. This closes the loop and
optimizes jointly over the hash functions and the binary codes so that they
gradually match each other. The resulting algorithm can be seen as a corrected,
iterated version of the procedure of optimizing first over the codes and then
learning the hash function. Compared to this, our optimization is guaranteed to
obtain better hash functions while being not much slower, as demonstrated
experimentally in various supervised datasets. In addition, our framework
facilitates the design of optimization algorithms for arbitrary types of loss
and hash functions.
Generate Image Descriptions based on Deep RNN and Memory Cells for Images Features
Authors: Shijian Tang, Song Han
Date: 2016-02-05 00:00:00

Generating natural language descriptions for images is a challenging task.
The traditional way is to use the convolutional neural network (CNN) to extract
image features, followed by recurrent neural network (RNN) to generate
sentences. In this paper, we present a new model that added memory cells to
gate the feeding of image features to the deep neural network. The intuition is
enabling our model to memorize how much information from images should be fed
at each stage of the RNN. Experiments on Flickr8K and Flickr30K datasets showed
that our model outperforms other state-of-the-art models with higher BLEU
scores.
Search Tracker: Human-derived object tracking in-the-wild through large-scale search and retrieval
Authors: Archith J. Bency, S. Karthikeyan, Carter De Leo, Santhoshkumar Sunderrajan, B. S. Manjunath
Date: 2016-02-05 00:00:00
Tasks: Object Tracking, Retrieval

Humans use context and scene knowledge to easily localize moving objects in
conditions of complex illumination changes, scene clutter and occlusions. In
this paper, we present a method to leverage human knowledge in the form of
annotated video libraries in a novel search and retrieval based setting to
track objects in unseen video sequences. For every video sequence, a document
that represents motion information is generated. Documents of the unseen video
are queried against the library at multiple scales to find videos with similar
motion characteristics. This provides us with coarse localization of objects in
the unseen video. We further adapt these retrieved object locations to the new
video using an efficient warping scheme. The proposed method is validated on
in-the-wild video surveillance datasets where we outperform state-of-the-art
appearance-based trackers. We also introduce a new challenging dataset with
complex object appearance changes.
Training Recurrent Neural Networks by Diffusion
Authors: Hossein Mobahi
Date: 2016-01-16 00:00:00

This work presents a new algorithm for training recurrent neural networks
(although ideas are applicable to feedforward networks as well). The algorithm
is derived from a theory in nonconvex optimization related to the diffusion
equation. The contributions made in this work are two fold. First, we show how
some seemingly disconnected mechanisms used in deep learning such as smart
initialization, annealed learning rate, layerwise pretraining, and noise
injection (as done in dropout and SGD) arise naturally and automatically from
this framework, without manually crafting them into the algorithms. Second, we
present some preliminary results on comparing the proposed method against SGD.
It turns out that the new algorithm can achieve similar level of generalization
accuracy of SGD in much fewer number of epochs.
Boolean Matrix Factorization and Noisy Completion via Message Passing
Authors: Siamak Ravanbakhsh, Barnabas Poczos, Russell Greiner
Date: 2015-09-28 00:00:00
Tasks: Collaborative Filtering, Matrix Completion

Boolean matrix factorization and Boolean matrix completion from noisy
observations are desirable unsupervised data-analysis methods due to their
interpretability, but hard to perform due to their NP-hardness. We treat these
problems as maximum a posteriori inference problems in a graphical model and
present a message passing approach that scales linearly with the number of
observations and factors. Our empirical study demonstrates that message passing
is able to recover low-rank Boolean matrices, in the boundaries of
theoretically possible recovery and compares favorably with state-of-the-art in
real-world applications, such collaborative filtering with large-scale Boolean
data.
Random Feature Maps via a Layered Random Projection (LaRP) Framework for Object Classification
Authors: A. G. Chung, M. J. Shafiee, A. Wong
Date: 2016-02-04 00:00:00
Tasks: General Classification, Object

The approximation of nonlinear kernels via linear feature maps has recently
gained interest due to their applications in reducing the training and testing
time of kernel-based learning algorithms. Current random projection methods
avoid the curse of dimensionality by embedding the nonlinear feature space into
a low dimensional Euclidean space to create nonlinear kernels. We introduce a
Layered Random Projection (LaRP) framework, where we model the linear kernels
and nonlinearity separately for increased training efficiency. The proposed
LaRP framework was assessed using the MNIST hand-written digits database and
the COIL-100 object database, and showed notable improvement in object
classification performance relative to other state-of-the-art random projection
methods.
Modeling User Exposure in Recommendation
Authors: Dawen Liang, Laurent Charlin, James McInerney, David M. Blei
Date: 2015-10-23 00:00:00
Tasks: Collaborative Filtering, Common Sense Reasoning

Collaborative filtering analyzes user preferences for items (e.g., books,
movies, restaurants, academic papers) by exploiting the similarity patterns
across users. In implicit feedback settings, all the items, including the ones
that a user did not consume, are taken into consideration. But this assumption
does not accord with the common sense understanding that users have a limited
scope and awareness of items. For example, a user might not have heard of a
certain paper, or might live too far away from a restaurant to experience it.
In the language of causal analysis, the assignment mechanism (i.e., the items
that a user is exposed to) is a latent variable that may change for various
user/item combinations. In this paper, we propose a new probabilistic approach
that directly incorporates user exposure to items into collaborative filtering.
The exposure is modeled as a latent variable and the model infers its value
from data. In doing so, we recover one of the most successful state-of-the-art
approaches as a special case of our model, and provide a plug-in method for
conditioning exposure on various forms of exposure covariates (e.g., topics in
text, venue locations). We show that our scalable inference algorithm
outperforms existing benchmarks in four different domains both with and without
exposure covariates.
Spatial Transformer Networks
Authors: Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu
Date: 2015-06-05 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Translation

Convolutional Neural Networks define an exceptionally powerful class of
models, but are still limited by the lack of ability to be spatially invariant
to the input data in a computationally and parameter efficient manner. In this
work we introduce a new learnable module, the Spatial Transformer, which
explicitly allows the spatial manipulation of data within the network. This
differentiable module can be inserted into existing convolutional
architectures, giving neural networks the ability to actively spatially
transform feature maps, conditional on the feature map itself, without any
extra training supervision or modification to the optimisation process. We show
that the use of spatial transformers results in models which learn invariance
to translation, scale, rotation and more generic warping, resulting in
state-of-the-art performance on several benchmarks, and for a number of classes
of transformations.
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing
Authors: Fei Zhu, Abderrahim Halimi, Paul Honeine, Badong Chen, Nanning Zheng
Date: 2016-02-04 00:00:00
Tasks: Hyperspectral Unmixing

In hyperspectral images, some spectral bands suffer from low signal-to-noise
ratio due to noisy acquisition and atmospheric effects, thus requiring robust
techniques for the unmixing problem. This paper presents a robust supervised
spectral unmixing approach for hyperspectral images. The robustness is achieved
by writing the unmixing problem as the maximization of the correntropy
criterion subject to the most commonly used constraints. Two unmixing problems
are derived: the first problem considers the fully-constrained unmixing, with
both the non-negativity and sum-to-one constraints, while the second one deals
with the non-negativity and the sparsity-promoting of the abundances. The
corresponding optimization problems are solved efficiently using an alternating
direction method of multipliers (ADMM) approach. Experiments on synthetic and
real hyperspectral images validate the performance of the proposed algorithms
for different scenarios, demonstrating that the correntropy-based unmixing is
robust to outlier bands.
NeRD: a Neural Response Divergence Approach to Visual Salience Detection
Authors: M. J. Shafiee, P. Siva, C. Scharfenberger, P. Fieguth, A. Wong
Date: 2016-02-04 00:00:00
Tasks: Object Recognition

In this paper, a novel approach to visual salience detection via Neural
Response Divergence (NeRD) is proposed, where synaptic portions of deep neural
networks, previously trained for complex object recognition, are leveraged to
compute low level cues that can be used to compute image region
distinctiveness. Based on this concept , an efficient visual salience detection
framework is proposed using deep convolutional StochasticNets. Experimental
results using CSSD and MSRA10k natural image datasets show that the proposed
NeRD approach can achieve improved performance when compared to
state-of-the-art image saliency approaches, while the attaining low
computational complexity necessary for near-real-time computer vision
applications.
Formal Verification of Autonomous Vehicle Platooning
Authors: Maryam Kamali, Louise A. Dennis, Owen McAree, Michael Fisher, Sandor M. Veres
Date: 2016-02-04 00:00:00
Tasks: Autonomous Vehicles, Decision Making

The coordination of multiple autonomous vehicles into convoys or platoons is
expected on our highways in the near future. However, before such platoons can
be deployed, the new autonomous behaviors of the vehicles in these platoons
must be certified. An appropriate representation for vehicle platooning is as a
multi-agent system in which each agent captures the "autonomous decisions"
carried out by each vehicle. In order to ensure that these autonomous
decision-making agents in vehicle platoons never violate safety requirements,
we use formal verification. However, as the formal verification technique used
to verify the agent code does not scale to the full system and as the global
verification technique does not capture the essential verification of
autonomous behavior, we use a combination of the two approaches. This mixed
strategy allows us to verify safety requirements not only of a model of the
system, but of the actual agent code used to program the autonomous vehicles.