pwc-archive/papers-with-abstracts huggingface dataset:
Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks
Authors: Li Wang, Ting Liu, Bing Wang, Xulei Yang, Gang Wang
Date: 2018-01-06 00:00:00
Tasks: Object, Object Tracking, Visual Object Tracking

Recently, deep learning has achieved very promising results in visual object
tracking. Deep neural networks in existing tracking methods require a lot of
training data to learn a large number of parameters. However, training data is
not sufficient for visual object tracking as annotations of a target object are
only available in the first frame of a test sequence. In this paper, we propose
to learn hierarchical features for visual object tracking by using tree
structure based Recursive Neural Networks (RNN), which have fewer parameters
than other deep neural networks, e.g. Convolutional Neural Networks (CNN).
First, we learn RNN parameters to discriminate between the target object and
background in the first frame of a test sequence. Tree structure over local
patches of an exemplar region is randomly generated by using a bottom-up greedy
search strategy. Given the learned RNN parameters, we create two dictionaries
regarding target regions and corresponding local patches based on the learned
hierarchical features from both top and leaf nodes of multiple random trees. In
each of the subsequent frames, we conduct sparse dictionary coding on all
candidates to select the best candidate as the new target location. In
addition, we online update two dictionaries to handle appearance changes of
target objects. Experimental results demonstrate that our feature learning
algorithm can significantly improve tracking performance on benchmark datasets.
Multiscale Sparse Microcanonical Models
Authors: Joan Bruna, Stephane Mallat
Date: 2018-01-06 00:00:00
Tasks: Gaussian Processes, Point Processes, Texture Synthesis

We study approximations of non-Gaussian stationary processes having long range correlations with microcanonical models. These models are conditioned by the empirical value of an energy vector, evaluated on a single realization. Asymptotic properties of maximum entropy microcanonical and macrocanonical processes and their convergence to Gibbs measures are reviewed. We show that the Jacobian of the energy vector controls the entropy rate of microcanonical processes. Sampling maximum entropy processes through MCMC algorithms require too many operations when the number of constraints is large. We define microcanonical gradient descent processes by transporting a maximum entropy measure with a gradient descent algorithm which enforces the energy conditions. Convergence and symmetries are analyzed. Approximations of non-Gaussian processes with long range interactions are defined with multiscale energy vectors computed with wavelet and scattering transforms. Sparsity properties are captured with $\bf l^1$ norms. Approximations of Gaussian, Ising and point processes are studied, as well as image and audio texture synthesis.
Design Exploration of Hybrid CMOS-OxRAM Deep Generative Architectures
Authors: Vivek Parmar, Manan Suri
Date: 2018-01-06 00:00:00
Tasks: Denoising

Deep Learning and its applications have gained tremendous interest recently
in both academia and industry. Restricted Boltzmann Machines (RBMs) offer a key
methodology to implement deep learning paradigms. This paper presents a novel
approach for realizing hybrid CMOS-OxRAM based deep generative models (DGM). In
our proposed hybrid DGM architectures, HfOx based (filamentary-type switching)
OxRAM devices are extensively used for realizing multiple computational and
non-computational functions such as: (i) Synapses (weights), (ii) internal
neuron-state storage, (iii) stochastic neuron activation and (iv) programmable
signal normalization. To validate the proposed scheme we have simulated two
different architectures: (i) Deep Belief Network (DBN) and (ii) Stacked
Denoising Autoencoder for classification and reconstruction of hand-written
digits from a reduced MNIST dataset of 6000 images. Contrastive-divergence (CD)
specially optimized for OxRAM devices was used to drive the synaptic weight
update mechanism of each layer in the network. Overall learning rule was based
on greedy-layer wise learning with no back propagation which allows the network
to be trained to a good pre-training stage. Performance of the simulated hybrid
CMOS-RRAM DGM model matches closely with software based model for a 2-layers
deep network. Top-3 test accuracy achieved by the DBN was 95.5%. MSE of the SDA
network was 0.003, lower than software based approach. Endurance analysis of
the simulated architectures show that for 200 epochs of training (single RBM
layer), maximum switching events/per OxRAM device was ~ 7000 cycles.
Using reinforcement learning to learn how to play text-based games
Authors: Mikuláš Zelinka
Date: 2018-01-06 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), text-based games, Transfer Learning

The ability to learn optimal control policies in systems where action space
is defined by sentences in natural language would allow many interesting
real-world applications such as automatic optimisation of dialogue systems.
Text-based games with multiple endings and rewards are a promising platform for
this task, since their feedback allows us to employ reinforcement learning
techniques to jointly learn text representations and control policies. We
present a general text game playing agent, testing its generalisation and
transfer learning performance and showing its ability to play multiple games at
once. We also present pyfiction, an open-source library for universal access to
different text games that could, together with our agent that implements its
interface, serve as a baseline for future research.
A Machine Learning Framework for Register Placement Optimization in Digital Circuit Design
Authors: Karthik Airani, Rohit Guttal
Date: 2018-01-06 00:00:00
Tasks: BIG-bench Machine Learning

In modern digital circuit back-end design, designers heavily rely on
electronic-design-automoation (EDA) tool to close timing. However, the
heuristic algorithms used in the place and route tool usually does not result
in optimal solution. Thus, significant design effort is used to tune parameters
or provide user constraints or guidelines to improve the tool performance. In
this paper, we targeted at those optimization space left behind by the EDA
tools and propose a machine learning framework that helps to define what are
the guidelines and constraints for registers placement, which can yield better
performance and quality for back-end design. In other words, the framework is
trying to learn what are the flaws of the existing EDA tools and tries to
optimize it by providing additional information. We discuss what is the proper
input feature vector to be extracted, and what is metric to be used for
reference output. We also develop a scheme to generate perturbed training
samples using existing design based on Gaussian randomization. By applying our
methodology, we are able to improve the design runtime by up to 36% and timing
quality by up to 23%.
Domain-Specific Face Synthesis for Video Face Recognition from a Single Sample Per Person
Authors: Fania Mokhayeri, Eric Granger, Guillaume-Alexandre Bilodeau
Date: 2018-01-06 00:00:00
Tasks: 3D Reconstruction, Clustering, Face Generation, Face Recognition

The performance of still-to-video FR systems can decline significantly
because faces captured in unconstrained operational domain (OD) over multiple
video cameras have a different underlying data distribution compared to faces
captured under controlled conditions in the enrollment domain (ED) with a still
camera. This is particularly true when individuals are enrolled to the system
using a single reference still. To improve the robustness of these systems, it
is possible to augment the reference set by generating synthetic faces based on
the original still. However, without knowledge of the OD, many synthetic images
must be generated to account for all possible capture conditions. FR systems
may, therefore, require complex implementations and yield lower accuracy when
training on many less relevant images. This paper introduces an algorithm for
domain-specific face synthesis (DSFS) that exploits the representative
intra-class variation information available from the OD. Prior to operation, a
compact set of faces from unknown persons appearing in the OD is selected
through clustering in the captured condition space. The domain-specific
variations of these face images are projected onto the reference stills by
integrating an image-based face relighting technique inside the 3D
reconstruction framework. A compact set of synthetic faces is generated that
resemble individuals of interest under the capture conditions relevant to the
OD. In a particular implementation based on sparse representation
classification, the synthetic faces generated with the DSFS are employed to
form a cross-domain dictionary that account for structured sparsity.
Experimental results reveal that augmenting the reference gallery set of FR
systems using the proposed DSFS approach can provide a higher level of accuracy
compared to state-of-the-art approaches, with only a moderate increase in its
computational complexity.
Cross-Media Similarity Evaluation for Web Image Retrieval in the Wild
Authors: Jianfeng Dong, Xirong Li, Duanqing Xu
Date: 2017-09-05 00:00:00
Tasks: Image Retrieval, Retrieval

In order to retrieve unlabeled images by textual queries, cross-media
similarity computation is a key ingredient. Although novel methods are
continuously introduced, little has been done to evaluate these methods
together with large-scale query log analysis. Consequently, how far have these
methods brought us in answering real-user queries is unclear. Given baseline
methods that compute cross-media similarity using relatively simple text/image
matching, how much progress have advanced models made is also unclear. This
paper takes a pragmatic approach to answering the two questions. Queries are
automatically categorized according to the proposed query visualness measure,
and later connected to the evaluation of multiple cross-media similarity models
on three test sets. Such a connection reveals that the success of the
state-of-the-art is mainly attributed to their good performance on
visual-oriented queries, while these queries account for only a small part of
real-user queries. To quantify the current progress, we propose a simple
text2image method, representing a novel test query by a set of images selected
from large-scale query log. Consequently, computing cross-media similarity
between the test query and a given image boils down to comparing the visual
similarity between the given image and the selected images. Image retrieval
experiments on the challenging Clickture dataset show that the proposed
text2image compares favorably to recent deep learning based alternatives.
A Note on the Inception Score
Authors: Shane Barratt, Rishi Sharma
Date: 2018-01-06 00:00:00

Deep generative models are powerful tools that have produced impressive
results in recent years. These advances have been for the most part empirically
driven, making it essential that we use high quality evaluation metrics. In
this paper, we provide new insights into the Inception Score, a recently
proposed and widely used evaluation metric for generative models, and
demonstrate that it fails to provide useful guidance when comparing models. We
discuss both suboptimalities of the metric itself and issues with its
application. Finally, we call for researchers to be more systematic and careful
when evaluating and comparing generative models, as the advancement of the
field depends upon it.
Distance formulas capable of unifying Euclidian space and probability space
Authors: Zecang Gu, Ling Dong
Date: 2018-01-06 00:00:00
Tasks: BIG-bench Machine Learning

For pattern recognition like image recognition, it has become clear that each
machine-learning dictionary data actually became data in probability space
belonging to Euclidean space. However, the distances in the Euclidean space and
the distances in the probability space are separated and ununified when machine
learning is introduced in the pattern recognition. There is still a problem
that it is impossible to directly calculate an accurate matching relation
between the sampling data of the read image and the learned dictionary data. In
this research, we focused on the reason why the distance is changed and the
extent of change when passing through the probability space from the original
Euclidean distance among data belonging to multiple probability spaces
containing Euclidean space. By finding the reason of the cause of the distance
error and finding the formula expressing the error quantitatively, a possible
distance formula to unify Euclidean space and probability space is found. Based
on the results of this research, the relationship between machine-learning
dictionary data and sampling data was clearly understood for pattern
recognition. As a result, the calculation of collation among data and
machine-learning to compete mutually between data are cleared, and complicated
calculations became unnecessary. Finally, using actual pattern recognition
data, experimental demonstration of a possible distance formula to unify
Euclidean space and probability space discovered by this research was carried
out, and the effectiveness of the result was confirmed.
Compressive sensing adaptation for polynomial chaos expansions
Authors: Panagiotis Tsilifis, Xun Huan, Cosmin Safta, Khachik Sargsyan, Guilhem Lacaze, Joseph C. Oefelein, Habib N. Najm, Roger G. Ghanem
Date: 2018-01-06 00:00:00
Tasks: Compressive Sensing

Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of
the underlying Gaussian germ. Several rotations have been proposed in the
literature resulting in adaptations with different convergence properties. In
this paper we present a new adaptation mechanism that builds on compressive
sensing algorithms, resulting in a reduced polynomial chaos approximation with
optimal sparsity. The developed adaptation algorithm consists of a two-step
optimization procedure that computes the optimal coefficients and the input
projection matrix of a low dimensional chaos expansion with respect to an
optimally rotated basis. We demonstrate the attractive features of our
algorithm through several numerical examples including the application on
Large-Eddy Simulation (LES) calculations of turbulent combustion in a HIFiRE
scramjet engine.
Frame-based Sparse Analysis and Synthesis Signal Representations and Parseval K-SVD
Authors: Wen-Liang Hwang, Ping-Tzan Huang, Tai-Lang Jong
Date: 2018-01-06 00:00:00
Tasks: Dictionary Learning

Frames are the foundation of the linear operators used in the decomposition
and reconstruction of signals, such as the discrete Fourier transform, Gabor,
wavelets, and curvelet transforms. The emergence of sparse representation
models has shifted of the emphasis in frame theory toward sparse
l1-minimization problems. In this paper, we apply frame theory to the sparse
representation of signals in which a synthesis dictionary is used for a frame
and an analysis dictionary is used for a dual frame. We sought to formulate a
novel dual frame design in which the sparse vector obtained through the
decomposition of any signal is also the sparse solution representing signals
based on a reconstruction frame. Our findings demonstrate that this type of
dual frame cannot be constructed for over-complete frames, thereby precluding
the use of any linear analysis operator in driving the sparse synthesis
coefficient for signal representation. Nonetheless, the best approximation to
the sparse synthesis solution can be derived from the analysis coefficient
using the canonical dual frame. In this study, we developed a novel dictionary
learning algorithm (called Parseval K-SVD) to learn a tight-frame dictionary.
We then leveraged the analysis and synthesis perspectives of signal
representation with frames to derive optimization formulations for problems
pertaining to image recovery. Our preliminary, results demonstrate that the
images recovered using this approach are correlated to the frame bounds of
dictionaries, thereby demonstrating the importance of using different
dictionaries for different applications.
Relaxation heuristics for the set multicover problem with generalized upper bound constraints
Authors: Shunji Umetani, Masanao Arakawa, Mutsunori Yagiura
Date: 2017-05-14 00:00:00

We consider an extension of the set covering problem (SCP) introducing
(i)~multicover and (ii)~generalized upper bound (GUB)~constraints. For the
conventional SCP, the pricing method has been introduced to reduce the size of
instances, and several efficient heuristic algorithms based on such reduction
techniques have been developed to solve large-scale instances. However, GUB
constraints often make the pricing method less effective, because they often
prevent solutions from containing highly evaluated variables together. To
overcome this problem, we develop heuristic algorithms to reduce the size of
instances, in which new evaluation schemes of variables are introduced taking
account of GUB constraints. We also develop an efficient implementation of a
2-flip neighborhood local search algorithm that reduces the number of
candidates in the neighborhood without sacrificing the solution quality. In
order to guide the search to visit a wide variety of good solutions, we also
introduce a path relinking method that generates new solutions by combining two
or more solutions obtained so far. According to computational comparison on
benchmark instances, the proposed method succeeds in selecting a small number
of promising variables properly and performs quite effectively even for
large-scale instances having hard GUB constraints.
Adversarial Perturbation Intensity Achieving Chosen Intra-Technique Transferability Level for Logistic Regression
Authors: Martin Gubri
Date: 2018-01-06 00:00:00
Tasks: regression

Machine Learning models have been shown to be vulnerable to adversarial
examples, ie. the manipulation of data by a attacker to defeat a defender's
classifier at test time. We present a novel probabilistic definition of
adversarial examples in perfect or limited knowledge setting using prior
probability distributions on the defender's classifier. Using the asymptotic
properties of the logistic regression, we derive a closed-form expression of
the intensity of any adversarial perturbation, in order to achieve a given
expected misclassification rate. This technique is relevant in a threat model
of known model specifications and unknown training data. To our knowledge, this
is the first method that allows an attacker to directly choose the probability
of attack success. We evaluate our approach on two real-world datasets.
Face Flashing: a Secure Liveness Detection Protocol based on Light Reflections
Authors: Di Tang, Zhe Zhou, Yinqian Zhang, Kehuan Zhang
Date: 2018-01-06 00:00:00
Tasks: Face Verification

Face authentication systems are becoming increasingly prevalent, especially
with the rapid development of Deep Learning technologies. However, human facial
information is easy to be captured and reproduced, which makes face
authentication systems vulnerable to various attacks. Liveness detection is an
important defense technique to prevent such attacks, but existing solutions did
not provide clear and strong security guarantees, especially in terms of time.
  To overcome these limitations, we propose a new liveness detection protocol
called Face Flashing that significantly increases the bar for launching
successful attacks on face authentication systems. By randomly flashing
well-designed pictures on a screen and analyzing the reflected light, our
protocol has leveraged physical characteristics of human faces: reflection
processing at the speed of light, unique textual features, and uneven 3D
shapes. Cooperating with working mechanism of the screen and digital cameras,
our protocol is able to detect subtle traces left by an attacking process.
  To demonstrate the effectiveness of Face Flashing, we implemented a prototype
and performed thorough evaluations with large data set collected from
real-world scenarios. The results show that our Timing Verification can
effectively detect the time gap between legitimate authentications and
malicious cases. Our Face Verification can also differentiate 2D plane from 3D
objects accurately. The overall accuracy of our liveness detection system is
98.8\%, and its robustness was evaluated in different scenarios. In the worst
case, our system's accuracy decreased to a still-high 97.3\%.
Discriminative Learning of the Prototype Set for Nearest Neighbor Classification
Authors: Shin Ando
Date: 2015-09-27 00:00:00
Tasks: Classification, General Classification, Prototype Selection

The nearest neighbor rule is a classic yet essential classification model,
particularly in problems where the supervising information is given by pairwise
dissimilarities and the embedding function are not easily obtained. Prototype
selection provides means of generalization and improving efficiency of the
nearest neighbor model, but many existing methods assume and rely on the
analyses of the input vector space. In this paper, we explore a
dissimilarity-based, parametrized model of the nearest neighbor rule. In the
proposed model, the selection of the nearest prototypes is influenced by the
parameters of the respective prototypes. It provides a formulation for
minimizing the violation of the extended nearest neighbor rule over the
training set in a tractable form to exploit numerical techniques. We show that
the minimization problem reduces to a large-margin principle learning and
demonstrate its advantage by empirical comparisons with other prototype
selection methods.
A Comprehensive Survey of Ontology Summarization: Measures and Methods
Authors: Seyedamin Pouriyeh, Mehdi Allahyari, Krys Kochut, Hamid Reza Arabnia
Date: 2018-01-05 00:00:00
Tasks: Survey

The Semantic Web is becoming a large scale framework that enables data to be
published, shared, and reused in the form of ontologies. The ontology which is
considered as basic building block of semantic web consists of two layers
including data and schema layer. With the current exponential development of
ontologies in both data size and complexity of schemas, ontology understanding
which is playing an important role in different tasks such as ontology
engineering, ontology learning, etc., is becoming more difficult. Ontology
summarization as a way to distill knowledge from an ontology and generate an
abridge version to facilitate a better understanding is getting more attention
recently. There are various approaches available for ontology summarization
which are focusing on different measures in order to produce a proper summary
for a given ontology. In this paper, we mainly focus on the common metrics
which are using for ontology summarization and meet the state-of-the-art in
ontology summarization.