pwc-archive/papers-with-abstracts huggingface dataset:
CNN based texture synthesize with Semantic segment
Authors: Xianye Liang, Bocheng Zhuo, Peijie Li, Liangju He
Date: 2016-05-16 00:00:00
Tasks: Deep Learning

Deep learning algorithm display powerful ability in Computer Vision area, in
recent year, the CNN has been applied to solve problems in the subarea of
Image-generating, which has been widely applied in areas such as photo editing,
image design, computer animation, real-time rendering for large scale of scenes
and for visual effects in movies. However in the texture synthesize procedure.
The state-of-art CNN can not capture the spatial location of texture in image,
lead to significant distortion after texture synthesize, we propose a new way
to generating-image by adding the semantic segment step with deep learning
algorithm as Pre-Processing and analyze the outcome.
On the Complexity of Connection Games
Authors: Édouard Bonnet, Florian Jamain, Abdallah Saffidine
Date: 2016-05-16 00:00:00
Tasks: Position

In this paper, we study three connection games among the most widely played:
Havannah, Twixt, and Slither. We show that determining the outcome of an
arbitrary input position is PSPACE-complete in all three cases. Our reductions
are based on the popular graph problem Generalized Geography and on Hex itself.
We also consider the complexity of generalizations of Hex parameterized by the
length of the solution and establish that while Short Generalized Hex is
W[1]-hard, Short Hex is FPT. Finally, we prove that the ultra-weak solution to
the empty starting position in hex cannot be fully adapted to any of these
three games.
Estimation of Fiber Orientations Using Neighborhood Information
Authors: Chuyang Ye, Jiachen Zhuo, Rao P. Gullapalli, Jerry L. Prince
Date: 2016-01-16 00:00:00

Data from diffusion magnetic resonance imaging (dMRI) can be used to
reconstruct fiber tracts, for example, in muscle and white matter. Estimation
of fiber orientations (FOs) is a crucial step in the reconstruction process and
these estimates can be corrupted by noise. In this paper, a new method called
Fiber Orientation Reconstruction using Neighborhood Information (FORNI) is
described and shown to reduce the effects of noise and improve FO estimation
performance by incorporating spatial consistency. FORNI uses a fixed tensor
basis to model the diffusion weighted signals, which has the advantage of
providing an explicit relationship between the basis vectors and the FOs. FO
spatial coherence is encouraged using weighted l1-norm regularization terms,
which contain the interaction of directional information between neighbor
voxels. Data fidelity is encouraged using a squared error between the observed
and reconstructed diffusion weighted signals. After appropriate weighting of
these competing objectives, the resulting objective function is minimized using
a block coordinate descent algorithm, and a straightforward parallelization
strategy is used to speed up processing. Experiments were performed on a
digital crossing phantom, ex vivo tongue dMRI data, and in vivo brain dMRI data
for both qualitative and quantitative evaluation. The results demonstrate that
FORNI improves the quality of FO estimation over other state of the art
algorithms.
On Avoidance Learning with Partial Observability
Authors: Tom J. Ameloot
Date: 2016-05-16 00:00:00

We study a framework where agents have to avoid aversive signals. The agents
are given only partial information, in the form of features that are
projections of task states. Additionally, the agents have to cope with
non-determinism, defined as unpredictability on the way that actions are
executed. The goal of each agent is to define its behavior based on
feature-action pairs that reliably avoid aversive signals. We study a learning
algorithm, called A-learning, that exhibits fixpoint convergence, where the
belief of the allowed feature-action pairs eventually becomes fixed. A-learning
is parameter-free and easy to implement.
OBDA Constraints for Effective Query Answering (Extended Version)
Authors: Dag Hovland, Davide Lanti, Martin Rezk, Guohui Xiao
Date: 2016-05-13 00:00:00
Tasks: Translation

In Ontology Based Data Access (OBDA) users pose SPARQL queries over an
ontology that lies on top of relational datasources. These queries are
translated on-the-fly into SQL queries by OBDA systems. Standard SPARQL-to-SQL
translation techniques in OBDA often produce SQL queries containing redundant
joins and unions, even after a number of semantic and structural optimizations.
These redundancies are detrimental to the performance of query answering,
especially in complex industrial OBDA scenarios with large enterprise
databases. To address this issue, we introduce two novel notions of OBDA
constraints and show how to exploit them for efficient query answering. We
conduct an extensive set of experiments on large datasets using real world data
and queries, showing that these techniques strongly improve the performance of
query answering up to orders of magnitude.
High-Performance Computing for Scheduling Decision Support: A Parallel Depth-First Search Heuristic
Authors: Gerhard Rauchecker, Guido Schryen
Date: 2016-05-16 00:00:00
Tasks: Decision Making, Management, Scheduling

Many academic disciplines - including information systems, computer science,
and operations management - face scheduling problems as important decision
making tasks. Since many scheduling problems are NP-hard in the strong sense,
there is a need for developing solution heuristics. For scheduling problems
with setup times on unrelated parallel machines, there is limited research on
solution methods and to the best of our knowledge, parallel computer
architectures have not yet been taken advantage of. We address this gap by
proposing and implementing a new solution heuristic and by testing different
parallelization strategies. In our computational experiments, we show that our
heuristic calculates near-optimal solutions even for large instances and that
computing time can be reduced substantially by our parallelization approach.
Information-theoretic Interestingness Measures for Cross-Ontology Data Mining
Authors: Prashanti Manda, Fiona McCarthy, Bindu Nanduri, Hui Wang, Susan M. Bridges
Date: 2015-04-29 00:00:00
Tasks: Anatomy

Community annotation of biological entities with concepts from multiple
bio-ontologies has created large and growing repositories of ontology-based
annotation data with embedded implicit relationships among orthogonal
ontologies. Development of efficient data mining methods and metrics to mine
and assess the quality of the mined relationships has not kept pace with the
growth of annotation data. In this study, we present a data mining method that
uses ontology-guided generalization to discover relationships across ontologies
along with a new interestingness metric based on information theory. We apply
our data mining algorithm and interestingness measures to datasets from the
Gene Expression Database at the Mouse Genome Informatics as a preliminary proof
of concept to mine relationships between developmental stages in the mouse
anatomy ontology and Gene Ontology concepts (biological process, molecular
function and cellular component). In addition, we present a comparison of our
interestingness metric to four existing metrics. Ontology-based annotation
datasets provide a valuable resource for discovery of relationships across
ontologies. The use of efficient data mining methods and appropriate
interestingness metrics enables the identification of high quality
relationships.
A Critical Examination of RESCAL for Completion of Knowledge Bases with Transitive Relations
Authors: Pushpendre Rastogi, Benjamin Van Durme
Date: 2016-05-16 00:00:00
Tasks: Knowledge Graphs, Link Prediction

Link prediction in large knowledge graphs has received a lot of attention
recently because of its importance for inferring missing relations and for
completing and improving noisily extracted knowledge graphs. Over the years a
number of machine learning researchers have presented various models for
predicting the presence of missing relations in a knowledge base. Although all
the previous methods are presented with empirical results that show high
performance on select datasets, there is almost no previous work on
understanding the connection between properties of a knowledge base and the
performance of a model. In this paper we analyze the RESCAL method and prove
that it can not encode asymmetric transitive relations in knowledge bases.
Exclusivity Regularized Machine
Authors: Xiaojie Guo
Date: 2016-03-28 00:00:00
Tasks: Diversity

It has been recognized that the diversity of base learners is of utmost
importance to a good ensemble. This paper defines a novel measurement of
diversity, termed as exclusivity. With the designed exclusivity, we further
propose an ensemble model, namely Exclusivity Regularized Machine (ERM), to
jointly suppress the training error of ensemble and enhance the diversity
between bases. Moreover, an Augmented Lagrange Multiplier based algorithm is
customized to effectively and efficiently seek the optimal solution of ERM.
Theoretical analysis on convergence and global optimality of the proposed
algorithm, as well as experiments are provided to reveal the efficacy of our
method and show its superiority over state-of-the-art alternatives in terms of
accuracy and efficiency.
Solve-Select-Scale: A Three Step Process For Sparse Signal Estimation
Authors: Mithun Das Gupta
Date: 2016-05-16 00:00:00
Tasks: compressed sensing

In the theory of compressed sensing (CS), the sparsity $\|x\|_0$ of the
unknown signal $\mathbf{x} \in \mathcal{R}^n$ is of prime importance and the
focus of reconstruction algorithms has mainly been either $\|x\|_0$ or its
convex relaxation (via $\|x\|_1$). However, it is typically unknown in practice
and has remained a challenge when nothing about the size of the support is
known. As pointed recently, $\|x\|_0$ might not be the best metric to minimize
directly, both due to its inherent complexity as well as its noise performance.
Recently a novel stable measure of sparsity $s(\mathbf{x}) :=
\|\mathbf{x}\|_1^2/\|\mathbf{x}\|_2^2$ has been investigated by Lopes
\cite{Lopes2012}, which is a sharp lower bound on $\|\mathbf{x}\|_0$. The
estimation procedure for this measure uses only a small number of linear
measurements, does not rely on any sparsity assumptions, and requires very
little computation. The usage of the quantity $s(\mathbf{x})$ in sparse signal
estimation problems has not received much importance yet. We develop the idea
of incorporating $s(\mathbf{x})$ into the signal estimation framework. We also
provide a three step algorithm to solve problems of the form $\mathbf{Ax=b}$
with no additional assumptions on the original signal $\mathbf{x}$.
Alternating optimization method based on nonnegative matrix factorizations for deep neural networks
Authors: Tetsuya Sakurai, Akira Imakura, Yuto Inoue, Yasunori Futamura
Date: 2016-05-16 00:00:00

The backpropagation algorithm for calculating gradients has been widely used
in computation of weights for deep neural networks (DNNs). This method requires
derivatives of objective functions and has some difficulties finding
appropriate parameters such as learning rate. In this paper, we propose a novel
approach for computing weight matrices of fully-connected DNNs by using two
types of semi-nonnegative matrix factorizations (semi-NMFs). In this method,
optimization processes are performed by calculating weight matrices
alternately, and backpropagation (BP) is not used. We also present a method to
calculate stacked autoencoder using a NMF. The output results of the
autoencoder are used as pre-training data for DNNs. The experimental results
show that our method using three types of NMFs attains similar error rates to
the conventional DNNs with BP.
Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient
Authors: Tianbao Yang, Lijun Zhang, Rong Jin, Jin-Feng Yi
Date: 2016-05-16 00:00:00

This work focuses on dynamic regret of online convex optimization that
compares the performance of online learning to a clairvoyant who knows the
sequence of loss functions in advance and hence selects the minimizer of the
loss function at each step. By assuming that the clairvoyant moves slowly
(i.e., the minimizers change slowly), we present several improved
variation-based upper bounds of the dynamic regret under the true and noisy
gradient feedback, which are {\it optimal} in light of the presented lower
bounds. The key to our analysis is to explore a regularity metric that measures
the temporal changes in the clairvoyant's minimizers, to which we refer as {\it
path variation}. Firstly, we present a general lower bound in terms of the path
variation, and then show that under full information or gradient feedback we
are able to achieve an optimal dynamic regret. Secondly, we present a lower
bound with noisy gradient feedback and then show that we can achieve optimal
dynamic regrets under a stochastic gradient feedback and two-point bandit
feedback. Moreover, for a sequence of smooth loss functions that admit a small
variation in the gradients, our dynamic regret under the two-point bandit
feedback matches what is achieved with full information.
Heart Beat Characterization from Ballistocardiogram Signals using Extended Functions of Multiple Instances
Authors: Changzhe Jiao, Princess Lyons, Alina Zare, Licet Rosales, Marjorie Skubic
Date: 2016-05-16 00:00:00
Tasks: Heart rate estimation, Multiple Instance Learning

A multiple instance learning (MIL) method, extended Function of Multiple
Instances ($e$FUMI), is applied to ballistocardiogram (BCG) signals produced by
a hydraulic bed sensor. The goal of this approach is to learn a personalized
heartbeat "concept" for an individual. This heartbeat concept is a prototype
(or "signature") that characterizes the heartbeat pattern for an individual in
ballistocardiogram data. The $e$FUMI method models the problem of learning a
heartbeat concept from a BCG signal as a MIL problem. This approach elegantly
addresses the uncertainty inherent in a BCG signal e. g., misalignment between
training data and ground truth, mis-collection of heartbeat by some
transducers, etc. Given a BCG training signal coupled with a ground truth
signal (e.g., a pulse finger sensor), training "bags" labeled with only binary
labels denoting if a training bag contains a heartbeat signal or not can be
generated. Then, using these bags, $e$FUMI learns a personalized concept of
heartbeat for a subject as well as several non-heartbeat background concepts.
After learning the heartbeat concept, heartbeat detection and heart rate
estimation can be applied to test data. Experimental results show that the
estimated heartbeat concept found by $e$FUMI is more representative and a more
discriminative prototype of the heartbeat signals than those found by
comparison MIL methods in the literature.
Learning to Rank Personalized Search Results in Professional Networks
Authors: Viet Ha-Thuc, Shakti Sinha
Date: 2016-05-16 00:00:00
Tasks: Learning-To-Rank

LinkedIn search is deeply personalized - for the same queries, different
searchers expect completely different results. This paper presents our approach
to achieving this by mining various data sources available in LinkedIn to infer
searchers' intents (such as hiring, job seeking, etc.), as well as extending
the concept of homophily to capture the searcher-result similarities on many
aspects. Then, learning-to-rank (LTR) is applied to combine these signals with
standard search features.
Rank Pooling for Action Recognition
Authors: Basura Fernando, Efstratios Gavves, Jose Oramas, Amir Ghodrati, Tinne Tuytelaars
Date: 2015-12-06 00:00:00
Tasks: Action Recognition, Gesture Recognition, Learning-To-Rank, Temporal Action Localization

We propose a function-based temporal pooling method that captures the latent
structure of the video sequence data - e.g. how frame-level features evolve
over time in a video. We show how the parameters of a function that has been
fit to the video data can serve as a robust new video representation. As a
specific example, we learn a pooling function via ranking machines. By learning
to rank the frame-level features of a video in chronological order, we obtain a
new representation that captures the video-wide temporal dynamics of a video,
suitable for action recognition. Other than ranking functions, we explore
different parametric models that could also explain the temporal changes in
videos. The proposed functional pooling methods, and rank pooling in
particular, is easy to interpret and implement, fast to compute and effective
in recognizing a wide variety of actions. We evaluate our method on various
benchmarks for generic action, fine-grained action and gesture recognition.
Results show that rank pooling brings an absolute improvement of 7-10 average
pooling baseline. At the same time, rank pooling is compatible with and
complementary to several appearance and local motion based methods and
features, such as improved trajectories and deep learning features.
Quantized Convolutional Neural Networks for Mobile Devices
Authors: Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, Jian Cheng
Date: 2015-12-21 00:00:00
Proceeding: CVPR 2016 6
Tasks: General Classification

Recently, convolutional neural networks (CNN) have demonstrated impressive
performance in various computer vision tasks. However, high performance
hardware is typically indispensable for the application of CNN models due to
the high computation complexity, which prohibits their further extensions. In
this paper, we propose an efficient framework, namely Quantized CNN, to
simultaneously speed-up the computation and reduce the storage and memory
overhead of CNN models. Both filter kernels in convolutional layers and
weighting matrices in fully-connected layers are quantized, aiming at
minimizing the estimation error of each layer's response. Extensive experiments
on the ILSVRC-12 benchmark demonstrate 4~6x speed-up and 15~20x compression
with merely one percentage loss of classification accuracy. With our quantized
CNN model, even mobile devices can accurately classify images within one
second.
DeepLearningKit - an GPU Optimized Deep Learning Framework for Apple's iOS, OS X and tvOS developed in Metal and Swift
Authors: Amund Tveit, Torbjørn Morland, Thomas Brox Røst
Date: 2016-05-15 00:00:00
Tasks: Deep Learning, GPU

In this paper we present DeepLearningKit - an open source framework that
supports using pretrained deep learning models (convolutional neural networks)
for iOS, OS X and tvOS. DeepLearningKit is developed in Metal in order to
utilize the GPU efficiently and Swift for integration with applications, e.g.
iOS-based mobile apps on iPhone/iPad, tvOS-based apps for the big screen, or OS
X desktop applications. The goal is to support using deep learning models
trained with popular frameworks such as Caffe, Torch, TensorFlow, Theano,
Pylearn, Deeplearning4J and Mocha. Given the massive GPU resources and time
required to train Deep Learning models we suggest an App Store like model to
distribute and download pretrained and reusable Deep Learning models.
Improving the Neural Algorithm of Artistic Style
Authors: Roman Novak, Yaroslav Nikulin
Date: 2016-05-15 00:00:00
Tasks: Style Transfer

In this work we investigate different avenues of improving the Neural
Algorithm of Artistic Style (by Leon A. Gatys, Alexander S. Ecker and Matthias
Bethge, arXiv:1508.06576).
  While showing great results when transferring homogeneous and repetitive
patterns, the original style representation often fails to capture more complex
properties, like having separate styles of foreground and background. This
leads to visual artifacts and undesirable textures appearing in unexpected
regions when performing style transfer.
  We tackle this issue with a variety of approaches, mostly by modifying the
style representation in order for it to capture more information and impose a
tighter constraint on the style transfer result.
  In our experiments, we subjectively evaluate our best method as producing
from barely noticeable to significant improvements in the quality of style
transfer.
Novelty Detection in MultiClass Scenarios with Incomplete Set of Class Labels
Authors: Nomi Vinokurov, Daphna Weinshall
Date: 2016-04-21 00:00:00
Tasks: Novelty Detection

We address the problem of novelty detection in multiclass scenarios where
some class labels are missing from the training set. Our method is based on the
initial assignment of confidence values, which measure the affinity between a
new test point and each known class. We first compare the values of the two top
elements in this vector of confidence values. In the heart of our method lies
the training of an ensemble of classifiers, each trained to discriminate known
from novel classes based on some partition of the training data into
presumed-known and presumednovel classes. Our final novelty score is derived
from the output of this ensemble of classifiers. We evaluated our method on two
datasets of images containing a relatively large number of classes - the
Caltech-256 and Cifar-100 datasets. We compared our method to 3 alternative
methods which represent commonly used approaches, including the one-class SVM,
novelty based on k-NN, novelty based on maximal confidence, and the recent
KNFST method. The results show a very clear and marked advantage for our method
over all alternative methods, in an experimental setup where class labels are
missing during training.
Data Collection for Interactive Learning through the Dialog
Authors: Miroslav Vodolán, Filip Jurčíček
Date: 2016-03-31 00:00:00
Tasks: Open-Domain Dialog

This paper presents a dataset collected from natural dialogs which enables to
test the ability of dialog systems to learn new facts from user utterances
throughout the dialog. This interactive learning will help with one of the most
prevailing problems of open domain dialog system, which is the sparsity of
facts a dialog system can reason about. The proposed dataset, consisting of
1900 collected dialogs, allows simulation of an interactive gaining of
denotations and questions explanations from users which can be used for the
interactive learning.
On Some Properties of Calibrated Trifocal Tensors
Authors: Evgeniy Martyushev
Date: 2016-01-07 00:00:00

In two-view geometry, the essential matrix describes the relative position
and orientation of two calibrated images. In three views, a similar role is
assigned to the calibrated trifocal tensor. It is a particular case of the
(uncalibrated) trifocal tensor and thus it inherits all its properties but, due
to the smaller degrees of freedom, satisfies a number of additional algebraic
constraints. Some of them are described in this paper. More specifically, we
define a new notion --- the trifocal essential matrix. On the one hand, it is a
generalization of the ordinary (bifocal) essential matrix, and, on the other
hand, it is closely related to the calibrated trifocal tensor. We prove the two
necessary and sufficient conditions that characterize the set of trifocal
essential matrices. Based on these characterizations, we propose three
necessary conditions on a calibrated trifocal tensor. They have a form of 15
quartic and 99 quintic polynomial equations. We show that in the practically
significant real case the 15 quartic constraints are also sufficient.