pwc-archive/papers-with-abstracts huggingface dataset:
The Asymptotic Performance of Linear Echo State Neural Networks
Authors: Romain Couillet, Gilles Wainrib, Harry Sevi, Hafiz Tiomoko Ali
Date: 2016-03-25 00:00:00

In this article, a study of the mean-square error (MSE) performance of linear
echo-state neural networks is performed, both for training and testing tasks.
Considering the realistic setting of noise present at the network nodes, we
derive deterministic equivalents for the aforementioned MSE in the limit where
the number of input data $T$ and network size $n$ both grow large. Specializing
then the network connectivity matrix to specific random settings, we further
obtain simple formulas that provide new insights on the performance of such
networks.
Markov substitute processes : a new model for linguistics and beyond
Authors: Olivier Catoni, Thomas Mainguy
Date: 2016-03-25 00:00:00
Tasks: Form

We introduce Markov substitute processes, a new model at the crossroad of
statistics and formal grammars, and prove its main property : Markov substitute
processes with a given support form an exponential family.
A movie genre prediction based on Multivariate Bernoulli model and genre correlations
Authors: Eric Makita, Artem Lenskiy
Date: 2016-03-25 00:00:00

Movie ratings play an important role both in determining the likelihood of a
potential viewer to watch the movie and in reflecting the current viewer
satisfaction with the movie. They are available in several sources like the
television guide, best-selling reference books, newspaper columns, and
television programs. Furthermore, movie ratings are crucial for recommendation
engines that track the behavior of all users and utilize the information to
suggest items they might like. Movie ratings in most cases, thus, provide
information that might be more important than movie feature-based data. It is
intuitively appealing that information about the viewing preferences in movie
genres is sufficient for predicting a genre of an unlabeled movie. In order to
predict movie genres, we treat ratings as a feature vector, apply the Bernoulli
event model to estimate the likelihood of a movies given genre, and evaluate
the posterior probability of the genre of a given movie using the Bayes rule.
The goal of the proposed technique is to efficiently use the movie ratings for
the task of predicting movie genres. In our approach we attempted to answer the
question: "Given the set of users who watched a movie, is it possible to
predict the genre of a movie based on its ratings?" Our simulation results with
MovieLens 100k data demonstrated the efficiency and accuracy of our proposed
technique, achieving 59% prediction rate for exact prediction and 69% when
including correlated genres.
A multinomial probabilistic model for movie genre predictions
Authors: Eric Makita, Artem Lenskiy
Date: 2016-03-25 00:00:00
Tasks: model, Prediction, Recommendation Systems

This paper proposes a movie genre-prediction based on multinomial probability
model. To the best of our knowledge, this problem has not been addressed yet in
the field of recommender system. The prediction of a movie genre has many
practical applications including complementing the items categories given by
experts and providing a surprise effect in the recommendations given to a user.
We employ mulitnomial event model to estimate a likelihood of a movie given
genre and the Bayes rule to evaluate the posterior probability of a genre given
a movie. Experiments with the MovieLens dataset validate our approach. We
achieved 70% prediction rate using only 15% of the whole set for training.
Deep Learning At Scale and At Ease
Authors: Wei Wang, Gang Chen, Haibo Chen, Tien Tuan Anh Dinh, Jinyang Gao, Beng Chin Ooi, Kian-Lee Tan, Sheng Wang
Date: 2016-03-25 00:00:00
Tasks: Deep Learning, image-classification, Image Classification

Recently, deep learning techniques have enjoyed success in various multimedia
applications, such as image classification and multi-modal data analysis. Large
deep learning models are developed for learning rich representations of complex
data. There are two challenges to overcome before deep learning can be widely
adopted in multimedia and other applications. One is usability, namely the
implementation of different models and training algorithms must be done by
non-experts without much effort especially when the model is large and complex.
The other is scalability, that is the deep learning system must be able to
provision for a huge demand of computing resources for training large models
with massive datasets. To address these two challenges, in this paper, we
design a distributed deep learning platform called SINGA which has an intuitive
programming model based on the common layer abstraction of deep learning
models. Good scalability is achieved through flexible distributed training
architecture and specific optimization techniques. SINGA runs on GPUs as well
as on CPUs, and we show that it outperforms many other state-of-the-art deep
learning systems. Our experience with developing and training deep learning
models for real-life multimedia applications in SINGA shows that the platform
is both usable and scalable.
Early Detection of Combustion Instabilities using Deep Convolutional Selective Autoencoders on Hi-speed Flame Video
Authors: Adedotun Akintayo, Kin Gwn Lore, Soumalya Sarkar, Soumik Sarkar
Date: 2016-03-25 00:00:00
Tasks: Descriptive

This paper proposes an end-to-end convolutional selective autoencoder
approach for early detection of combustion instabilities using rapidly arriving
flame image frames. The instabilities arising in combustion processes cause
significant deterioration and safety issues in various human-engineered systems
such as land and air based gas turbine engines. These properties are described
as self-sustaining, large amplitude pressure oscillations and show varying
spatial scales periodic coherent vortex structure shedding. However, such
instability is extremely difficult to detect before a combustion process
becomes completely unstable due to its sudden (bifurcation-type) nature. In
this context, an autoencoder is trained to selectively mask stable flame and
allow unstable flame image frames. In that process, the model learns to
identify and extract rich descriptive and explanatory flame shape features.
With such a training scheme, the selective autoencoder is shown to be able to
detect subtle instability features as a combustion process makes transition
from stable to unstable region. As a consequence, the deep learning tool-chain
can perform as an early detection framework for combustion instabilities that
will have a transformative impact on the safety and performance of modern
engines.
An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection
Authors: Adedotun Akintayo, Nigel Lee, Vikas Chawla, Mark Mullaney, Christopher Marett, Asheesh Singh, Arti Singh, Greg Tylka, Baskar Ganapathysubramaniam, Soumik Sarkar
Date: 2016-03-25 00:00:00
Tasks: object-detection, Object Detection

This paper proposes a novel selective autoencoder approach within the
framework of deep convolutional networks. The crux of the idea is to train a
deep convolutional autoencoder to suppress undesired parts of an image frame
while allowing the desired parts resulting in efficient object detection. The
efficacy of the framework is demonstrated on a critical plant science problem.
In the United States, approximately $1 billion is lost per annum due to a
nematode infection on soybean plants. Currently, plant-pathologists rely on
labor-intensive and time-consuming identification of Soybean Cyst Nematode
(SCN) eggs in soil samples via manual microscopy. The proposed framework
attempts to significantly expedite the process by using a series of manually
labeled microscopic images for training followed by automated high-throughput
egg detection. The problem is particularly difficult due to the presence of a
large population of non-egg particles (disturbances) in the image frames that
are very similar to SCN eggs in shape, pose and illumination. Therefore, the
selective autoencoder is trained to learn unique features related to the
invariant shapes and sizes of the SCN eggs without handcrafting. After that, a
composite non-maximum suppression and differencing is applied at the
post-processing stage.
Training-Free Synthesized Face Sketch Recognition Using Image Quality Assessment Metrics
Authors: Nannan Wang, Jie Li, Leiyu Sun, Bin Song, Xinbo Gao
Date: 2016-03-25 00:00:00
Tasks: Face Recognition, Face Sketch Synthesis, Full reference image quality assessment, Full-Reference Image Quality Assessment, Image Quality Assessment, Sketch Recognition

Face sketch synthesis has wide applications ranging from digital
entertainments to law enforcements. Objective image quality assessment scores
and face recognition accuracy are two mainly used tools to evaluate the
synthesis performance. In this paper, we proposed a synthesized face sketch
recognition framework based on full-reference image quality assessment metrics.
Synthesized sketches generated from four state-of-the-art methods are utilized
to test the performance of the proposed recognition framework. For the image
quality assessment metrics, we employed the classical structured similarity
index metric and other three prevalent metrics: visual information fidelity,
feature similarity index metric and gradient magnitude similarity deviation.
Extensive experiments compared with baseline methods illustrate the
effectiveness of the proposed synthesized face sketch recognition framework.
Data and implementation code in this paper are available online at
www.ihitworld.com/WNN/IQA_Sketch.zip.
Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting
Authors: Hanzi Wang, Guobao Xiao, Yan Yan, David Suter
Date: 2016-03-25 00:00:00
Proceeding: ICCV 2015 12

In this paper, we propose a novel geometric model fitting method, called
Mode-Seeking on Hypergraphs (MSH),to deal with multi-structure data even in the
presence of severe outliers. The proposed method formulates geometric model
fitting as a mode seeking problem on a hypergraph in which vertices represent
model hypotheses and hyperedges denote data points. MSH intuitively detects
model instances by a simple and effective mode seeking algorithm. In addition
to the mode seeking algorithm, MSH includes a similarity measure between
vertices on the hypergraph and a weight-aware sampling technique. The proposed
method not only alleviates sensitivity to the data distribution, but also is
scalable to large scale problems. Experimental results further demonstrate that
the proposed method has significant superiority over the state-of-the-art
fitting methods on both synthetic data and real images.
An Effective Unconstrained Correlation Filter and Its Kernelization for Face Recognition
Authors: Yan Yan, Hanzi Wang, Cuihua Li, Chenhui Yang, Bineng Zhong
Date: 2016-03-25 00:00:00
Tasks: Face Recognition, Robust Face Recognition

In this paper, an effective unconstrained correlation filter called Uncon-
strained Optimal Origin Tradeoff Filter (UOOTF) is presented and applied to
robust face recognition. Compared with the conventional correlation filters in
Class-dependence Feature Analysis (CFA), UOOTF improves the overall performance
for unseen patterns by removing the hard constraints on the origin correlation
outputs during the filter design. To handle non-linearly separable
distributions between different classes, we further develop a non- linear
extension of UOOTF based on the kernel technique. The kernel ex- tension of
UOOTF allows for higher flexibility of the decision boundary due to a wider
range of non-linearity properties. Experimental results demon- strate the
effectiveness of the proposed unconstrained correlation filter and its
kernelization in the task of face recognition.
Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition
Authors: Yan Yan, Hanzi Wang, Si Chen, Xiaochun Cao, David Zhang
Date: 2016-03-25 00:00:00

This paper presents a novel quadratic projection based feature extraction
framework, where a set of quadratic matrices is learned to distinguish each
class from all other classes. We formulate quadratic matrix learning (QML) as a
standard semidefinite programming (SDP) problem. However, the con- ventional
interior-point SDP solvers do not scale well to the problem of QML for
high-dimensional data. To solve the scalability of QML, we develop an efficient
algorithm, termed DualQML, based on the Lagrange duality theory, to extract
nonlinear features. To evaluate the feasibility and effectiveness of the
proposed framework, we conduct extensive experiments on biometric recognition.
Experimental results on three representative biometric recogni- tion tasks,
including face, palmprint, and ear recognition, demonstrate the superiority of
the DualQML-based feature extraction algorithm compared to the current
state-of-the-art algorithms
Contextual Action Recognition with R*CNN
Authors: Georgia Gkioxari, Ross Girshick, Jitendra Malik
Date: 2015-05-05 00:00:00
Proceeding: ICCV 2015 12
Tasks: Action Recognition, Attribute, General Classification, Human-Object Interaction Detection, Temporal Action Localization

There are multiple cues in an image which reveal what action a person is
performing. For example, a jogger has a pose that is characteristic for
jogging, but the scene (e.g. road, trail) and the presence of other joggers can
be an additional source of information. In this work, we exploit the simple
observation that actions are accompanied by contextual cues to build a strong
action recognition system. We adapt RCNN to use more than one region for
classification while still maintaining the ability to localize the action. We
call our system R*CNN. The action-specific models and the feature maps are
trained jointly, allowing for action specific representations to emerge. R*CNN
achieves 90.2% mean AP on the PASAL VOC Action dataset, outperforming all other
approaches in the field by a significant margin. Last, we show that R*CNN is
not limited to action recognition. In particular, R*CNN can also be used to
tackle fine-grained tasks such as attribute classification. We validate this
claim by reporting state-of-the-art performance on the Berkeley Attributes of
People dataset.
Text-Attentional Convolutional Neural Networks for Scene Text Detection
Authors: Tong He, Weilin Huang, Yu Qiao, Jian Yao
Date: 2015-10-12 00:00:00
Tasks: Multi-Task Learning, Scene Text Detection, text-classification, Text Classification, Text Detection

Recent deep learning models have demonstrated strong capabilities for
classifying text and non-text components in natural images. They extract a
high-level feature computed globally from a whole image component (patch),
where the cluttered background information may dominate true text features in
the deep representation. This leads to less discriminative power and poorer
robustness. In this work, we present a new system for scene text detection by
proposing a novel Text-Attentional Convolutional Neural Network (Text-CNN) that
particularly focuses on extracting text-related regions and features from the
image components. We develop a new learning mechanism to train the Text-CNN
with multi-level and rich supervised information, including text region mask,
character label, and binary text/nontext information. The rich supervision
information enables the Text-CNN with a strong capability for discriminating
ambiguous texts, and also increases its robustness against complicated
background components. The training process is formulated as a multi-task
learning problem, where low-level supervised information greatly facilitates
main task of text/non-text classification. In addition, a powerful low-level
detector called Contrast- Enhancement Maximally Stable Extremal Regions
(CE-MSERs) is developed, which extends the widely-used MSERs by enhancing
intensity contrast between text patterns and background. This allows it to
detect highly challenging text patterns, resulting in a higher recall. Our
approach achieved promising results on the ICDAR 2013 dataset, with a F-measure
of 0.82, improving the state-of-the-art results substantially.
Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks
Authors: Wentao Zhu, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Yanghao Li, Li Shen, Xiaohui Xie
Date: 2016-03-24 00:00:00
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Skeleton based action recognition distinguishes human actions using the
trajectories of skeleton joints, which provide a very good representation for
describing actions. Considering that recurrent neural networks (RNNs) with Long
Short-Term Memory (LSTM) can learn feature representations and model long-term
temporal dependencies automatically, we propose an end-to-end fully connected
deep LSTM network for skeleton based action recognition. Inspired by the
observation that the co-occurrences of the joints intrinsically characterize
human actions, we take the skeleton as the input at each time slot and
introduce a novel regularization scheme to learn the co-occurrence features of
skeleton joints. To train the deep LSTM network effectively, we propose a new
dropout algorithm which simultaneously operates on the gates, cells, and output
responses of the LSTM neurons. Experimental results on three human action
recognition datasets consistently demonstrate the effectiveness of the proposed
model.
Seeing Invisible Poses: Estimating 3D Body Pose from Egocentric Video
Authors: Hao Jiang, Kristen Grauman
Date: 2016-03-24 00:00:00
Proceeding: CVPR 2017 7

Understanding the camera wearer's activity is central to egocentric vision,
yet one key facet of that activity is inherently invisible to the camera--the
wearer's body pose. Prior work focuses on estimating the pose of hands and arms
when they come into view, but this 1) gives an incomplete view of the full body
posture, and 2) prevents any pose estimate at all in many frames, since the
hands are only visible in a fraction of daily life activities. We propose to
infer the "invisible pose" of a person behind the egocentric camera. Given a
single video, our efficient learning-based approach returns the full body 3D
joint positions for each frame. Our method exploits cues from the dynamic
motion signatures of the surrounding scene--which changes predictably as a
function of body pose--as well as static scene structures that reveal the
viewpoint (e.g., sitting vs. standing). We further introduce a novel energy
minimization scheme to infer the pose sequence. It uses soft predictions of the
poses per time instant together with a non-parametric model of human pose
dynamics over longer windows. Our method outperforms an array of possible
alternatives, including deep learning approaches for direct pose regression
from images.
A universal tradeoff between power, precision and speed in physical communication
Authors: Subhaneil Lahiri, Jascha Sohl-Dickstein, Surya Ganguli
Date: 2016-03-24 00:00:00
Tasks: Friction

Maximizing the speed and precision of communication while minimizing power
dissipation is a fundamental engineering design goal. Also, biological systems
achieve remarkable speed, precision and power efficiency using poorly
understood physical design principles. Powerful theories like information
theory and thermodynamics do not provide general limits on power, precision and
speed. Here we go beyond these classical theories to prove that the product of
precision and speed is universally bounded by power dissipation in any physical
communication channel whose dynamics is faster than that of the signal.
Moreover, our derivation involves a novel connection between friction and
information geometry. These results may yield insight into both the engineering
design of communication devices and the structure and function of biological
signaling systems.
Pathway Lasso: Estimate and Select Sparse Mediation Pathways with High Dimensional Mediators
Authors: Yi Zhao, Xi Luo
Date: 2016-03-24 00:00:00
Tasks: Model Selection

In many scientific studies, it becomes increasingly important to delineate
the causal pathways through a large number of mediators, such as genetic and
brain mediators. Structural equation modeling (SEM) is a popular technique to
estimate the pathway effects, commonly expressed as products of coefficients.
However, it becomes unstable to fit such models with high dimensional
mediators, especially for a general setting where all the mediators are
causally dependent but the exact causal relationships between them are unknown.
This paper proposes a sparse mediation model using a regularized SEM approach,
where sparsity here means that a small number of mediators have nonzero
mediation effects between a treatment and an outcome. To address the model
selection challenge, we innovate by introducing a new penalty called Pathway
Lasso. This penalty function is a convex relaxation of the non-convex product
function, and it enables a computationally tractable optimization criterion to
estimate and select many pathway effects simultaneously. We develop a fast
ADMM-type algorithm to compute the model parameters, and we show that the
iterative updates can be expressed in closed form. On both simulated data and a
real fMRI dataset, the proposed approach yields higher pathway selection
accuracy and lower estimation bias than other competing methods.
Coarse-to-Fine Segmentation With Shape-Tailored Scale Spaces
Authors: Ganesh Sundaramoorthi, Naeemullah Khan, Byung-Woo Hong
Date: 2016-03-24 00:00:00
Tasks: Motion Segmentation, Segmentation

We formulate a general energy and method for segmentation that is designed to
have preference for segmenting the coarse structure over the fine structure of
the data, without smoothing across boundaries of regions. The energy is
formulated by considering data terms at a continuum of scales from the scale
space computed from the Heat Equation within regions, and integrating these
terms over all time. We show that the energy may be approximately optimized
without solving for the entire scale space, but rather solving time-independent
linear equations at the native scale of the image, making the method
computationally feasible. We provide a multi-region scheme, and apply our
method to motion segmentation. Experiments on a benchmark dataset shows that
our method is less sensitive to clutter or other undesirable fine-scale
structure, and leads to better performance in motion segmentation.
Skill-Based Differences in Spatio-Temporal Team Behavior in Defence of The Ancients 2
Authors: Anders Drachen, Matthew Yancey, John Maguire, Derrek Chu, Iris Yuhui Wang, Tobias Mahlmann, Matthias Schubert, Diego Klabjan
Date: 2016-03-24 00:00:00
Tasks: Clustering, Dota 2, Time Series, Time Series Analysis, Time Series Clustering

Multiplayer Online Battle Arena (MOBA) games are among the most played
digital games in the world. In these games, teams of players fight against each
other in arena environments, and the gameplay is focused on tactical combat.
Mastering MOBAs requires extensive practice, as is exemplified in the popular
MOBA Defence of the Ancients 2 (DotA 2). In this paper, we present three
data-driven measures of spatio-temporal behavior in DotA 2: 1) Zone changes; 2)
Distribution of team members and: 3) Time series clustering via a fuzzy
approach. We present a method for obtaining accurate positional data from DotA
2. We investigate how behavior varies across these measures as a function of
the skill level of teams, using four tiers from novice to professional players.
Results indicate that spatio-temporal behavior of MOBA teams is related to team
skill, with professional teams having smaller within-team distances and
conducting more zone changes than amateur teams. The temporal distribution of
the within-team distances of professional and high-skilled teams also generally
follows patterns distinct from lower skill ranks.