pwc-archive/papers-with-abstracts huggingface dataset:
Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids
Authors: Qifeng Chen, Vladlen Koltun
Date: 2016-04-12 00:00:00
Proceeding: CVPR 2016 6
Tasks: global-optimization, Optical Flow Estimation

We present a global optimization approach to optical flow estimation. The
approach optimizes a classical optical flow objective over the full space of
mappings between discrete grids. No descriptor matching is used. The highly
regular structure of the space of mappings enables optimizations that reduce
the computational complexity of the algorithm's inner loop from quadratic to
linear and support efficient matching of tens of thousands of nodes to tens of
thousands of displacements. We show that one-shot global optimization of a
classical Horn-Schunck-type objective over regular grids at a single resolution
is sufficient to initialize continuous interpolation and achieve
state-of-the-art performance on challenging modern benchmarks.
An Unbiased Data Collection and Content Exploitation/Exploration Strategy for Personalization
Authors: Liangjie Hong, Adnan Boz
Date: 2016-04-12 00:00:00
Tasks: Recommendation Systems, Thompson Sampling

One of missions for personalization systems and recommender systems is to
show content items according to users' personal interests. In order to achieve
such goal, these systems are learning user interests over time and trying to
present content items tailoring to user profiles. Recommending items according
to users' preferences has been investigated extensively in the past few years,
mainly thanks for the popularity of Netflix competition. In a real setting,
users may be attracted by a subset of those items and interact with them, only
leaving partial feedbacks to the system to learn in the next cycle, which leads
to significant biases into systems and hence results in a situation where user
engagement metrics cannot be improved over time. The problem is not just for
one component of the system. The data collected from users is usually used in
many different tasks, including learning ranking functions, building user
profiles and constructing content classifiers. Once the data is biased, all
these downstream use cases would be impacted as well. Therefore, it would be
beneficial to gather unbiased data through user interactions. Traditionally,
unbiased data collection is done through showing items uniformly sampling from
the content pool. However, this simple scheme is not feasible as it risks user
engagement metrics and it takes long time to gather user feedbacks. In this
paper, we introduce a user-friendly unbiased data collection framework, by
utilizing methods developed in the exploitation and exploration literature. We
discuss how the framework is different from normal multi-armed bandit problems
and why such method is needed. We layout a novel Thompson sampling for
Bernoulli ranked-list to effectively balance user experiences and data
collection. The proposed method is validated from a real bucket test and we
show strong results comparing to old algorithms
GPU-FV: Realtime Fisher Vector and Its Applications in Video Monitoring
Authors: Wenying Ma, Liangliang Cao, Lei Yu, Guoping Long, Yucheng Li
Date: 2016-04-12 00:00:00
Tasks: CPU, GPU, Retrieval

Fisher vector has been widely used in many multimedia retrieval and visual
recognition applications with good performance. However, the computation
complexity prevents its usage in real-time video monitoring. In this work, we
proposed and implemented GPU-FV, a fast Fisher vector extraction method with
the help of modern GPUs. The challenge of implementing Fisher vector on GPUs
lies in the data dependency in feature extraction and expensive memory access
in Fisher vector computing. To handle these challenges, we carefully designed
GPU-FV in a way that utilizes the computing power of GPU as much as possible,
and applied optimizations such as loop tiling to boost the performance. GPU-FV
is about 12 times faster than the CPU version, and 50\% faster than a
non-optimized GPU implementation. For standard video input (320*240), GPU-FV
can process each frame within 34ms on a model GPU. Our experiments show that
GPU-FV obtains a similar recognition accuracy as traditional FV on VOC 2007 and
Caltech 256 image sets. We also applied GPU-FV for realtime video monitoring
tasks and found that GPU-FV outperforms a number of previous works. Especially,
when the number of training examples are small, GPU-FV outperforms the recent
popular deep CNN features borrowed from ImageNet. The code can be downloaded
from the following link https://bitbucket.org/mawenjing/gpu-fv.
Structured Matrix Recovery via the Generalized Dantzig Selector
Authors: Sheng Chen, Arindam Banerjee
Date: 2016-04-12 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Recommendation Systems

In recent years, structured matrix recovery problems have gained considerable
attention for its real world applications, such as recommender systems and
computer vision. Much of the existing work has focused on matrices with
low-rank structure, and limited progress has been made matrices with other
types of structure. In this paper we present non-asymptotic analysis for
estimation of generally structured matrices via the generalized Dantzig
selector under generic sub-Gaussian measurements. We show that the estimation
error can always be succinctly expressed in terms of a few geometric measures
of suitable sets which only depend on the structure of the underlying true
matrix. In addition, we derive the general bounds on these geometric measures
for structures characterized by unitarily invariant norms, which is a large
family covering most matrix norms of practical interest. Examples are provided
to illustrate the utility of our theoretical development.
A C++ library for Multimodal Deep Learning
Authors: Jian Jin
Date: 2015-12-22 00:00:00
Tasks: Deep Learning, Multimodal Deep Learning, Philosophy

MDL, Multimodal Deep Learning Library, is a deep learning framework that
supports multiple models, and this document explains its philosophy and
functionality. MDL runs on Linux, Mac, and Unix platforms. It depends on
OpenCV.
Structure Inference Machines: Recurrent Neural Networks for Analyzing Relations in Group Activity Recognition
Authors: Zhiwei Deng, Arash Vahdat, Hexiang Hu, Greg Mori
Date: 2015-11-13 00:00:00
Proceeding: CVPR 2016 6
Tasks: Activity Recognition, Group Activity Recognition

Rich semantic relations are important in a variety of visual recognition
problems. As a concrete example, group activity recognition involves the
interactions and relative spatial relations of a set of people in a scene.
State of the art recognition methods center on deep learning approaches for
training highly effective, complex classifiers for interpreting images.
However, bridging the relatively low-level concepts output by these methods to
interpret higher-level compositional scenes remains a challenge. Graphical
models are a standard tool for this task. In this paper, we propose a method to
integrate graphical models and deep neural networks into a joint framework.
Instead of using a traditional inference method, we use a sequential inference
modeled by a recurrent neural network. Beyond this, the appropriate structure
for inference can be learned by imposing gates on edges between nodes.
Empirical results on group activity recognition demonstrate the potential of
this model to handle highly structured learning tasks.
Visualizing and Understanding Deep Texture Representations
Authors: Tsung-Yu Lin, Subhransu Maji
Date: 2015-11-16 00:00:00
Proceeding: CVPR 2016 6
Tasks: Attribute, Image Manipulation, Scene Recognition, Texture Classification

A number of recent approaches have used deep convolutional neural networks
(CNNs) to build texture representations. Nevertheless, it is still unclear how
these models represent texture and invariances to categorical variations. This
work conducts a systematic evaluation of recent CNN-based texture descriptors
for recognition and attempts to understand the nature of invariances captured
by these representations. First we show that the recently proposed bilinear CNN
model [25] is an excellent general-purpose texture descriptor and compares
favorably to other CNN-based descriptors on various texture and scene
recognition benchmarks. The model is translationally invariant and obtains
better accuracy on the ImageNet dataset without requiring spatial jittering of
data compared to corresponding models trained with spatial jittering. Based on
recent work [13, 28] we propose a technique to visualize pre-images, providing
a means for understanding categorical properties that are captured by these
representations. Finally, we show preliminary results on how a unified
parametric model of texture analysis and synthesis can be used for
attribute-based image manipulation, e.g. to make an image more swirly,
honeycombed, or knitted. The source code and additional visualizations are
available at http://vis-www.cs.umass.edu/texture
Backward-Forward Search for Manipulation Planning
Authors: Caelan Reed Garrett, Tomas Lozano-Perez, Leslie Pack Kaelbling
Date: 2016-04-12 00:00:00

In this paper we address planning problems in high-dimensional hybrid
configuration spaces, with a particular focus on manipulation planning problems
involving many objects. We present the hybrid backward-forward (HBF) planning
algorithm that uses a backward identification of constraints to direct the
sampling of the infinite action space in a forward search from the initial
state towards a goal configuration. The resulting planner is probabilistically
complete and can effectively construct long manipulation plans requiring both
prehensile and nonprehensile actions in cluttered environments.
Resource Allocation with Population Dynamics
Authors: Jonathan Epperlein, Jakub Marecek
Date: 2016-04-12 00:00:00

Many analyses of resource-allocation problems employ simplistic models of the
population. Using the example of a resource-allocation problem of Marecek et
al. [arXiv:1406.7639], we introduce rather a general behavioural model, where
the evolution of a heterogeneous population of agents is governed by a Markov
chain. Still, we are able to show that the distribution of agents across
resources converges in distribution, for suitable means of information
provision, under certain assumptions. The model and proof techniques may have
wider applicability.
Multi-modal Fusion for Diabetes Mellitus and Impaired Glucose Regulation Detection
Authors: Jinxing Li, David Zhang, Yongcheng Li, Jian Wu
Date: 2016-04-12 00:00:00
Tasks: Multi-modal Classification

Effective and accurate diagnosis of Diabetes Mellitus (DM), as well as its
early stage Impaired Glucose Regulation (IGR), has attracted much attention
recently. Traditional Chinese Medicine (TCM) [3], [5] etc. has proved that
tongue, face and sublingual diagnosis as a noninvasive method is a reasonable
way for disease detection. However, most previous works only focus on a single
modality (tongue, face or sublingual) for diagnosis, although different
modalities may provide complementary information for the diagnosis of DM and
IGR. In this paper, we propose a novel multi-modal classification method to
discriminate between DM (or IGR) and healthy controls. Specially, the tongue,
facial and sublingual images are first collected by using a non-invasive
capture device. The color, texture and geometry features of these three types
of images are then extracted, respectively. Finally, our so-called multi-modal
similar and specific learning (MMSSL) approach is proposed to combine features
of tongue, face and sublingual, which not only exploits the correlation but
also extracts individual components among them. Experimental results on a
dataset consisting of 192 Healthy, 198 DM and 114 IGR samples (all samples were
obtained from Guangdong Provincial Hospital of Traditional Chinese Medicine)
substantiate the effectiveness and superiority of our proposed method for the
diagnosis of DM and IGR, compared to the case of using a single modality.
A Convex Surrogate Operator for General Non-Modular Loss Functions
Authors: Jiaqian Yu, Matthew Blaschko
Date: 2016-04-12 00:00:00

Empirical risk minimization frequently employs convex surrogates to
underlying discrete loss functions in order to achieve computational
tractability during optimization. However, classical convex surrogates can only
tightly bound modular loss functions, sub-modular functions or supermodular
functions separately while maintaining polynomial time computation. In this
work, a novel generic convex surrogate for general non-modular loss functions
is introduced, which provides for the first time a tractable solution for loss
functions that are neither super-modular nor submodular. This convex surro-gate
is based on a submodular-supermodular decomposition for which the existence and
uniqueness is proven in this paper. It takes the sum of two convex surrogates
that separately bound the supermodular component and the submodular component
using slack-rescaling and the Lov{\'a}sz hinge, respectively. It is further
proven that this surrogate is convex , piecewise linear, an extension of the
loss function, and for which subgradient computation is polynomial time.
Empirical results are reported on a non-submodular loss based on the
S{{\o}}rensen-Dice difference function, and a real-world face track dataset
with tens of thousands of frames, demonstrating the improved performance,
efficiency, and scalabil-ity of the novel convex surrogate.
Order-Fractal transition in abstract paintings
Authors: E. M. De la Calleja, F. Cervantes, J. De la Calleja
Date: 2015-10-22 00:00:00

We report the degree of order of twenty-two Jackson Pollock's paintings using
\emph{Hausdorff-Besicovitch fractal dimension}. Through the maximum value of
each multi-fractal spectrum, the artworks are classify by the year in which
they were painted. It has been reported that Pollock's paintings are fractal
and it increased on his latest works. However our results show that fractal
dimension of the paintings are on a range of fractal dimension with values
close to two. We identify this behavior as a fractal-order transition. Based on
the study of disorder-order transition in physical systems, we interpreted the
fractal-order transition through its dark paint strokes in Pollocks' paintings,
as structured lines following a power law measured by fractal dimension. We
obtain self-similarity in some specific Pollock's paintings, that reveal an
important dependence on the scale of observation. We also characterize by its
fractal spectrum, the called \emph{Teri's Find}. We obtained similar spectrums
between \emph{Teri's Find} and \emph{Number 5} from Pollock, suggesting that
fractal dimension cannot be completely rejected as a quantitative parameter to
authenticate this kind of artworks.
Improving sentence compression by learning to predict gaze
Authors: Sigrid Klerke, Yoav Goldberg, Anders Søgaard
Date: 2016-04-12 00:00:00
Proceeding: NAACL 2016 6
Tasks: Multi-Task Learning, Sentence, Sentence Compression

We show how eye-tracking corpora can be used to improve sentence compression
models, presenting a novel multi-task learning algorithm based on multi-layer
LSTMs. We obtain performance competitive with or better than state-of-the-art
approaches.
Optimal Margin Distribution Machine
Authors: Teng Zhang, Zhi-Hua Zhou
Date: 2016-04-12 00:00:00

Support vector machine (SVM) has been one of the most popular learning
algorithms, with the central idea of maximizing the minimum margin, i.e., the
smallest distance from the instances to the classification boundary. Recent
theoretical results, however, disclosed that maximizing the minimum margin does
not necessarily lead to better generalization performances, and instead, the
margin distribution has been proven to be more crucial. Based on this idea, we
propose a new method, named Optimal margin Distribution Machine (ODM), which
tries to achieve a better generalization performance by optimizing the margin
distribution. We characterize the margin distribution by the first- and
second-order statistics, i.e., the margin mean and variance. The proposed
method is a general learning approach which can be used in any place where SVM
can be applied, and their superiority is verified both theoretically and
empirically in this paper.
Loss Bounds and Time Complexity for Speed Priors
Authors: Daniel Filan, Marcus Hutter, Jan Leike
Date: 2016-04-12 00:00:00

This paper establishes for the first time the predictive performance of speed
priors and their computational complexity. A speed prior is essentially a
probability distribution that puts low probability on strings that are not
efficiently computable. We propose a variant to the original speed prior
(Schmidhuber, 2002), and show that our prior can predict sequences drawn from
probability measures that are estimable in polynomial time. Our speed prior is
computable in doubly-exponential time, but not in polynomial time. On a
polynomial time computable sequence our speed prior is computable in
exponential time. We show better upper complexity bounds for Schmidhuber's
speed prior under the same conditions, and that it predicts deterministic
sequences that are computable in polynomial time; however, we also show that it
is not computable in polynomial time, and the question of its predictive
properties for stochastic sequences remains open.
Applying Ontological Modeling on Quranic Nature Domain
Authors: A. B. M. Shamsuzzaman Sadi, Towfique Anam, Mohamed Abdirazak, Abdillahi Hasan Adnan, Sazid Zaman Khan, Mohamed Mahmudur Rahman, Ghassan Samara
Date: 2016-04-12 00:00:00
Tasks: Translation

The holy Quran is the holy book of the Muslims. It contains information about
many domains. Often people search for particular concepts of holy Quran based
on the relations among concepts. An ontological modeling of holy Quran can be
useful in such a scenario. In this paper, we have modeled nature related
concepts of holy Quran using OWL (Web Ontology Language) / RDF (Resource
Description Framework). Our methodology involves identifying nature related
concepts mentioned in holy Quran and identifying relations among those
concepts. These concepts and relations are represented as classes/instances and
properties of an OWL ontology. Later, in the result section it is shown that,
using the Ontological model, SPARQL queries can retrieve verses and concepts of
interest. Thus, this modeling helps semantic search and query on the holy
Quran. In this work, we have used English translation of the holy Quran by
Sahih International, Protege OWL Editor and for querying we have used SPARQL.
Optimal Route Planning with Prioritized Task Scheduling for AUV Missions
Authors: S. Mahmoud Zadeh, D. Powers, K. Sammut, A. Lammas, A. M. Yazdani
Date: 2016-04-12 00:00:00
Tasks: Motion Planning, Scheduling

This paper presents a solution to Autonomous Underwater Vehicles (AUVs) large
scale route planning and task assignment joint problem. Given a set of
constraints (e.g., time) and a set of task priority values, the goal is to find
the optimal route for underwater mission that maximizes the sum of the
priorities and minimizes the total risk percentage while meeting the given
constraints. Making use of the heuristic nature of genetic and swarm
intelligence algorithms in solving NP-hard graph problems, Particle Swarm
Optimization (PSO) and Genetic Algorithm (GA) are employed to find the optimum
solution, where each individual in the population is a candidate solution
(route). To evaluate the robustness of the proposed methods, the performance of
the all PS and GA algorithms are examined and compared for a number of Monte
Carlo runs. Simulation results suggest that the routes generated by both
algorithms are feasible and reliable enough, and applicable for underwater
motion planning. However, the GA-based route planner produces superior results
comparing to the results obtained from the PSO based route planner.
Confidence Decision Trees via Online and Active Learning for Streaming (BIG) Data
Authors: Rocco De Rosa
Date: 2016-04-12 00:00:00
Tasks: Active Learning

Decision tree classifiers are a widely used tool in data stream mining. The
use of confidence intervals to estimate the gain associated with each split
leads to very effective methods, like the popular Hoeffding tree algorithm.
From a statistical viewpoint, the analysis of decision tree classifiers in a
streaming setting requires knowing when enough new information has been
collected to justify splitting a leaf. Although some of the issues in the
statistical analysis of Hoeffding trees have been already clarified, a general
and rigorous study of confidence intervals for splitting criteria is missing.
We fill this gap by deriving accurate confidence intervals to estimate the
splitting gain in decision tree learning with respect to three criteria:
entropy, Gini index, and a third index proposed by Kearns and Mansour. Our
confidence intervals depend in a more detailed way on the tree parameters. We
also extend our confidence analysis to a selective sampling setting, in which
the decision tree learner adaptively decides which labels to query in the
stream. We furnish theoretical guarantee bounding the probability that the
classification is non-optimal learning the decision tree via our selective
sampling strategy. Experiments on real and synthetic data in a streaming
setting show that our trees are indeed more accurate than trees with the same
number of leaves generated by other techniques and our active learning module
permits to save labeling cost. In addition, comparing our labeling strategy
with recent methods, we show that our approach is more robust and consistent
respect all the other techniques applied to incremental decision trees.
The Right Mutation Strength for Multi-Valued Decision Variables
Authors: Benjamin Doerr, Carola Doerr, Timo Kötzing
Date: 2016-04-12 00:00:00
Tasks: Evolutionary Algorithms, Position

The most common representation in evolutionary computation are bit strings.
This is ideal to model binary decision variables, but less useful for variables
taking more values. With very little theoretical work existing on how to use
evolutionary algorithms for such optimization problems, we study the run time
of simple evolutionary algorithms on some OneMax-like functions defined over
$\Omega = \{0, 1, \dots, r-1\}^n$. More precisely, we regard a variety of
problem classes requesting the component-wise minimization of the distance to
an unknown target vector $z \in \Omega$. For such problems we see a crucial
difference in how we extend the standard-bit mutation operator to these
multi-valued domains. While it is natural to select each position of the
solution vector to be changed independently with probability $1/n$, there are
various ways to then change such a position. If we change each selected
position to a random value different from the original one, we obtain an
expected run time of $\Theta(nr \log n)$. If we change each selected position
by either $+1$ or $-1$ (random choice), the optimization time reduces to
$\Theta(nr + n\log n)$. If we use a random mutation strength $i \in
\{0,1,\ldots,r-1\}^n$ with probability inversely proportional to $i$ and change
the selected position by either $+i$ or $-i$ (random choice), then the
optimization time becomes $\Theta(n \log(r)(\log(n)+\log(r)))$, bringing down
the dependence on $r$ from linear to polylogarithmic. One of our results
depends on a new variant of the lower bounding multiplicative drift theorem.