pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Spatial Context-Sensitive Cloud/Cloud-Shadow Detection in Multi-Source Multi-Spectral Earth Observation Images: AutoCloud+
Authors: Andrea Baraldi
Date: 2017-01-16 00:00:00
Tasks: Earth Observation, Image Enhancement, Retrieval, Shadow Detection

The proposed Earth observation (EO) based value adding system (EO VAS),
hereafter identified as AutoCloud+, consists of an innovative EO image
understanding system (EO IUS) design and implementation capable of automatic
spatial context sensitive cloud/cloud shadow detection in multi source multi
spectral (MS) EO imagery, whether or not radiometrically calibrated, acquired
by multiple platforms, either spaceborne or airborne, including unmanned aerial
vehicles (UAVs). It is worth mentioning that the same EO IUS architecture is
suitable for a large variety of EO based value adding products and services,
including: (i) low level image enhancement applications, such as automatic MS
image topographic correction, co registration, mosaicking and compositing, (ii)
high level MS image land cover (LC) and LC change (LCC) classification and
(iii) content based image storage/retrieval in massive multi source EO image
databases (big data mining).
Geometric features for voxel-based surface recognition
Authors: Dmitry Yarotsky
Date: 2017-01-16 00:00:00
Tasks: General Classification, Retrieval

We introduce a library of geometric voxel features for CAD surface
recognition/retrieval tasks. Our features include local versions of the
intrinsic volumes (the usual 3D volume, surface area, integrated mean and
Gaussian curvature) and a few closely related quantities. We also compute Haar
wavelet and statistical distribution features by aggregating raw voxel
features. We apply our features to object classification on the ESB data set
and demonstrate accurate results with a small number of shallow decision trees.
Automatic Synchronization of Multi-User Photo Galleries
Authors: E. Sansone, K. Apostolidis, N. Conci, G. Boato, V. Mezaris, F. G. B. De Natale
Date: 2016-08-24 00:00:00

In this paper we address the issue of photo galleries synchronization, where
pictures related to the same event are collected by different users. Existing
solutions to address the problem are usually based on unrealistic assumptions,
like time consistency across photo galleries, and often heavily rely on
heuristics, limiting therefore the applicability to real-world scenarios. We
propose a solution that achieves better generalization performance for the
synchronization task compared to the available literature. The method is
characterized by three stages: at first, deep convolutional neural network
features are used to assess the visual similarity among the photos; then, pairs
of similar photos are detected across different galleries and used to construct
a graph; eventually, a probabilistic graphical model is used to estimate the
temporal offset of each pair of galleries, by traversing the minimum spanning
tree extracted from this graph. The experimental evaluation is conducted on
four publicly available datasets covering different types of events,
demonstrating the strength of our proposed method. A thorough discussion of the
obtained results is provided for a critical assessment of the quality in
synchronization.
Thompson Sampling For Stochastic Bandits with Graph Feedback
Authors: Aristide C. Y. Tossou, Christos Dimitrakakis, Devdatt Dubhashi
Date: 2017-01-16 00:00:00
Tasks: Thompson Sampling

We present a novel extension of Thompson Sampling for stochastic sequential
decision problems with graph feedback, even when the graph structure itself is
unknown and/or changing. We provide theoretical guarantees on the Bayesian
regret of the algorithm, linking its performance to the underlying properties
of the graph. Thompson Sampling has the advantage of being applicable without
the need to construct complicated upper confidence bounds for different
problems. We illustrate its performance through extensive experimental results
on real and simulated networks with graph feedback. More specifically, we
tested our algorithms on power law, planted partitions and Erdo's-Renyi graphs,
as well as on graphs derived from Facebook and Flixster data. These all show
that our algorithms clearly outperform related methods that employ upper
confidence bounds, even if the latter use more information about the graph.
Achieving Privacy in the Adversarial Multi-Armed Bandit
Authors: Aristide C. Y. Tossou, Christos Dimitrakakis
Date: 2017-01-16 00:00:00

In this paper, we improve the previously best known regret bound to achieve
$\epsilon$-differential privacy in oblivious adversarial bandits from
$\mathcal{O}{(T^{2/3}/\epsilon)}$ to $\mathcal{O}{(\sqrt{T} \ln T /\epsilon)}$.
This is achieved by combining a Laplace Mechanism with EXP3. We show that
though EXP3 is already differentially private, it leaks a linear amount of
information in $T$. However, we can improve this privacy by relying on its
intrinsic exponential mechanism for selecting actions. This allows us to reach
$\mathcal{O}{(\sqrt{\ln T})}$-DP, with a regret of $\mathcal{O}{(T^{2/3})}$
that holds against an adaptive adversary, an improvement from the best known of
$\mathcal{O}{(T^{3/4})}$. This is done by using an algorithm that run EXP3 in a
mini-batch loop. Finally, we run experiments that clearly demonstrate the
validity of our theoretical analysis.
Bandwidth limited object recognition in high resolution imagery
Authors: Laura Lopez-Fuentes, Andrew D. Bagdanov, Joost Van de Weijer, Harald Skinnemoen
Date: 2017-01-16 00:00:00
Tasks: Object, object-detection, Object Detection, Object Recognition, Vocal Bursts Intensity Prediction

This paper proposes a novel method to optimize bandwidth usage for object
detection in critical communication scenarios. We develop two operating models
of active information seeking. The first model identifies promising regions in
low resolution imagery and progressively requests higher resolution regions on
which to perform recognition of higher semantic quality. The second model
identifies promising regions in low resolution imagery while simultaneously
predicting the approximate location of the object of higher semantic quality.
From this general framework, we develop a car recognition system via
identification of its license plate and evaluate the performance of both models
on a car dataset that we introduce. Results are compared with traditional JPEG
compression and demonstrate that our system saves up to one order of magnitude
of bandwidth while sacrificing little in terms of recognition performance.
Sparse Kernel Canonical Correlation Analysis via $\ell_1$-regularization
Authors: Xiaowei Zhang, Delin Chu, Li-Zhi Liao, Michael K. Ng
Date: 2017-01-16 00:00:00

Canonical correlation analysis (CCA) is a multivariate statistical technique
for finding the linear relationship between two sets of variables. The kernel
generalization of CCA named kernel CCA has been proposed to find nonlinear
relations between datasets. Despite their wide usage, they have one common
limitation that is the lack of sparsity in their solution. In this paper, we
consider sparse kernel CCA and propose a novel sparse kernel CCA algorithm
(SKCCA). Our algorithm is based on a relationship between kernel CCA and least
squares. Sparsity of the dual transformations is introduced by penalizing the
$\ell_{1}$-norm of dual vectors. Experiments demonstrate that our algorithm not
only performs well in computing sparse dual transformations but also can
alleviate the over-fitting problem of kernel CCA.
Deep Memory Networks for Attitude Identification
Authors: Cheng Li, Xiaoxiao Guo, Qiaozhu Mei
Date: 2017-01-16 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

We consider the task of identifying attitudes towards a given set of entities
from text. Conventionally, this task is decomposed into two separate subtasks:
target detection that identifies whether each entity is mentioned in the text,
either explicitly or implicitly, and polarity classification that classifies
the exact sentiment towards an identified entity (the target) into positive,
negative, or neutral.
  Instead, we show that attitude identification can be solved with an
end-to-end machine learning architecture, in which the two subtasks are
interleaved by a deep memory network. In this way, signals produced in target
detection provide clues for polarity classification, and reversely, the
predicted polarity provides feedback to the identification of targets.
Moreover, the treatments for the set of targets also influence each other --
the learned representations may share the same semantics for some targets but
vary for others. The proposed deep memory network, the AttNet, outperforms
methods that do not consider the interactions between the subtasks or those
among the targets, including conventional machine learning methods and the
state-of-the-art deep learning models.
A Watermarking Technique Using Discrete Curvelet Transform for Security of Multiple Biometric Features
Authors: Rohit M. Thanki, Ved Vyas Dwivedi, Komal R. Borisagar
Date: 2017-01-16 00:00:00
Tasks: Edge Detection

The robustness and security of the biometric watermarking approach can be
improved by using a multiple watermarking. This multiple watermarking proposed
for improving security of biometric features and data. When the imposter tries
to create the spoofed biometric feature, the invisible biometric watermark
features can provide appropriate protection to multimedia data. In this paper,
a biometric watermarking technique with multiple biometric watermarks are
proposed in which biometric features of fingerprint, face, iris and signature
is embedded in the image. Before embedding, fingerprint, iris, face and
signature features are extracted using Shen-Castan edge detection and Principal
Component Analysis. These all biometric watermark features are embedded into
various mid band frequency curvelet coefficients of host image. All four
fingerprint features, iris features, facial features and signature features are
the biometric characteristics of the individual and they are used for cross
verification and copyright protection if any manipulation occurs. The proposed
technique is fragile enough; features cannot be extracted from the watermarked
image when an imposter tries to remove watermark features illegally. It can use
for multiple copyright authentication and verification.
3D Fully Convolutional Network for Vehicle Detection in Point Cloud
Authors: Bo Li
Date: 2016-11-24 00:00:00
Tasks: Autonomous Driving, object-detection, Object Detection, vehicle detection

2D fully convolutional network has been recently successfully applied to
object detection from images. In this paper, we extend the fully convolutional
network based detection techniques to 3D and apply it to point cloud data. The
proposed approach is verified on the task of vehicle detection from lidar point
cloud for autonomous driving. Experiments on the KITTI dataset shows a
significant performance improvement over the previous point cloud based
detection approaches.
Two decades of local binary patterns: A survey
Authors: Matti Pietikäinen, Guoying Zhao
Date: 2016-12-20 00:00:00
Tasks: Survey, Vocal Bursts Valence Prediction

Texture is an important characteristic for many types of images. In recent
years very discriminative and computationally efficient local texture
descriptors based on local binary patterns (LBP) have been developed, which has
led to significant progress in applying texture methods to different problems
and applications. Due to this progress, the division between texture
descriptors and more generic image or video descriptors has been disappearing.
A large number of different variants of LBP have been developed to improve its
robustness, and to increase its discriminative power and applicability to
different types of problems. In this chapter, the most recent and important
variants of LBP in 2D, spatiotemporal, 3D, and 4D domains are surveyed.
Interesting new developments of LBP in 1D signal analysis are also considered.
Finally, some future challenges for research are presented.
Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks
Authors: Vahid Behzadan, Arslan Munir
Date: 2017-01-16 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep learning classifiers are known to be inherently vulnerable to
manipulation by intentionally perturbed inputs, named adversarial examples. In
this work, we establish that reinforcement learning techniques based on Deep
Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and
verify the transferability of adversarial examples across different DQN models.
Furthermore, we present a novel class of attacks based on this vulnerability
that enable policy manipulation and induction in the learning process of DQNs.
We propose an attack mechanism that exploits the transferability of adversarial
examples to implement policy induction attacks on DQNs, and demonstrate its
efficacy and impact through experimental study of a game-learning scenario.
Near Optimal Behavior via Approximate State Abstraction
Authors: David Abel, D. Ellis Hershkowitz, Michael L. Littman
Date: 2017-01-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The combinatorial explosion that plagues planning and reinforcement learning
(RL) algorithms can be moderated using state abstraction. Prohibitively large
task representations can be condensed such that essential information is
preserved, and consequently, solutions are tractably computable. However, exact
abstractions, which treat only fully-identical situations as equivalent, fail
to present opportunities for abstraction in environments where no two
situations are exactly alike. In this work, we investigate approximate state
abstractions, which treat nearly-identical situations as equivalent. We present
theoretical guarantees of the quality of behaviors derived from four types of
approximate abstractions. Additionally, we empirically demonstrate that
approximate abstractions lead to reduction in task complexity and bounded loss
of optimality of behavior in a variety of environments.
Alternating Direction Method of Multipliers for Sparse Convolutional Neural Networks
Authors: Farkhondeh Kiaee, Christian Gagné, Mahdieh Abbasi
Date: 2016-11-05 00:00:00

The storage and computation requirements of Convolutional Neural Networks
(CNNs) can be prohibitive for exploiting these models over low-power or
embedded devices. This paper reduces the computational complexity of the CNNs
by minimizing an objective function, including the recognition loss that is
augmented with a sparsity-promoting penalty term. The sparsity structure of the
network is identified using the Alternating Direction Method of Multipliers
(ADMM), which is widely used in large optimization problems. This method
alternates between promoting the sparsity of the network and optimizing the
recognition performance, which allows us to exploit the two-part structure of
the corresponding objective functions. In particular, we take advantage of the
separability of the sparsity-inducing penalty functions to decompose the
minimization problem into sub-problems that can be solved sequentially.
Applying our method to a variety of state-of-the-art CNN models, our proposed
method is able to simplify the original model, generating models with less
computation and fewer parameters, while maintaining and often improving
generalization performance. Accomplishments on a variety of models strongly
verify that our proposed ADMM-based method can be a very useful tool for
simplifying and improving deep CNNs.
Light Source Estimation with Analytical Path-tracing
Authors: Mike Kasper, Nima Keivan, Gabe Sibley, Christoffer Heckman
Date: 2017-01-15 00:00:00
Tasks: Lighting Estimation, Outdoor Light Source Estimation

We present a novel algorithm for light source estimation in scenes
reconstructed with a RGB-D camera based on an analytically-derived formulation
of path-tracing. Our algorithm traces the reconstructed scene with a custom
path-tracer and computes the analytical derivatives of the light transport
equation from principles in optics. These derivatives are then used to perform
gradient descent, minimizing the photometric error between one or more captured
reference images and renders of our current lighting estimation using an
environment map parameterization for light sources. We show that our approach
of modeling all light sources as points at infinity approximates lights located
near the scene with surprising accuracy. Due to the analytical formulation of
derivatives, optimization to the solution is considerably accelerated. We
verify our algorithm using both real and synthetic data.
Agent-Agnostic Human-in-the-Loop Reinforcement Learning
Authors: David Abel, John Salvatier, Andreas Stuhlmüller, Owain Evans
Date: 2017-01-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Providing Reinforcement Learning agents with expert advice can dramatically
improve various aspects of learning. Prior work has developed teaching
protocols that enable agents to learn efficiently in complex environments; many
of these methods tailor the teacher's guidance to agents with a particular
representation or underlying learning scheme, offering effective but
specialized teaching procedures. In this work, we explore protocol programs, an
agent-agnostic schema for Human-in-the-Loop Reinforcement Learning. Our goal is
to incorporate the beneficial properties of a human teacher into Reinforcement
Learning without making strong assumptions about the inner workings of the
agent. We show how to represent existing approaches such as action pruning,
reward shaping, and training in simulation as special cases of our schema and
conduct preliminary experiments on simple domains.
Quantifying literature quality using complexity criteria
Authors: Gerardo Febres, Klaus Jaffe
Date: 2014-01-28 00:00:00
Tasks: Diversity

We measured entropy and symbolic diversity for English and Spanish texts
including literature Nobel laureates and other famous authors. Entropy, symbol
diversity and symbol frequency profiles were compared for these four groups. We
also built a scale sensitive to the quality of writing and evaluated its
relationship with the Flesch's readability index for English and the
Szigriszt's perspicuity index for Spanish. Results suggest a correlation
between entropy and word diversity with quality of writing. Text genre also
influences the resulting entropy and diversity of the text. Results suggest the
plausibility of automated quality assessment of texts.
Dialog Context Language Modeling with Recurrent Neural Networks
Authors: Bing Liu, Ian Lane
Date: 2017-01-15 00:00:00
Tasks: Language Modeling, Language Modelling

In this work, we propose contextual language models that incorporate dialog
level discourse information into language modeling. Previous works on
contextual language model treat preceding utterances as a sequence of inputs,
without considering dialog interactions. We design recurrent neural network
(RNN) based contextual language models that specially track the interactions
between speakers in a dialog. Experiment results on Switchboard Dialog Act
Corpus show that the proposed model outperforms conventional single turn based
RNN language model by 3.3% on perplexity. The proposed models also demonstrate
advantageous performance over other competitive contextual language models.
Representation Learning Models for Entity Search
Authors: Shijia E, Yang Xiang, Mohan Zhang
Date: 2016-10-28 00:00:00
Tasks: Representation Learning

We focus on the problem of learning distributed representations for entity
search queries, named entities, and their short descriptions. With our
representation learning models, the entity search query, named entity and
description can be represented as low-dimensional vectors. Our goal is to
develop a simple but effective model that can make the distributed
representations of query related entities similar to the query in the vector
space. Hence, we propose three kinds of learning strategies, and the difference
between them mainly lies in how to deal with the relationship between an entity
and its description. We analyze the strengths and weaknesses of each learning
strategy and validate our methods on public datasets which contain four kinds
of named entities, i.e., movies, TV shows, restaurants and celebrities. The
experimental results indicate that our proposed methods can adapt to different
types of entity search queries, and outperform the current state-of-the-art
methods based on keyword matching and vanilla word2vec models. Besides, the
proposed methods can be trained fast and be easily extended to other similar
tasks.
Distributed Hessian-Free Optimization for Deep Neural Network
Authors: Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takáč
Date: 2016-06-02 00:00:00
Tasks: CPU, speech-recognition, Speech Recognition

Training deep neural network is a high dimensional and a highly non-convex
optimization problem. Stochastic gradient descent (SGD) algorithm and it's
variations are the current state-of-the-art solvers for this task. However, due
to non-covexity nature of the problem, it was observed that SGD slows down near
saddle point. Recent empirical work claim that by detecting and escaping saddle
point efficiently, it's more likely to improve training performance. With this
objective, we revisit Hessian-free optimization method for deep networks. We
also develop its distributed variant and demonstrate superior scaling potential
to SGD, which allows more efficiently utilizing larger computing resources thus
enabling large models and faster time to obtain desired solution. Furthermore,
unlike truncated Newton method (Marten's HF) that ignores negative curvature
information by using na\"ive conjugate gradient method and Gauss-Newton Hessian
approximation information - we propose a novel algorithm to explore negative
curvature direction by solving the sub-problem with stabilized bi-conjugate
method involving possible indefinite stochastic Hessian information. We show
that these techniques accelerate the training process for both the standard
MNIST dataset and also the TIMIT speech recognition problem, demonstrating
robust performance with upto an order of magnitude larger batch sizes. This
increased scaling potential is illustrated with near linear speed-up on upto 16
CPU nodes for a simple 4-layer network.
Iterative Block Tensor Singular Value Thresholding for Extraction of Low Rank Component of Image Data
Authors: Longxi Chen, Yipeng Liu, Ce Zhu
Date: 2017-01-15 00:00:00

Tensor principal component analysis (TPCA) is a multi-linear extension of
principal component analysis which converts a set of correlated measurements
into several principal components. In this paper, we propose a new robust TPCA
method to extract the princi- pal components of the multi-way data based on
tensor singular value decomposition. The tensor is split into a number of
blocks of the same size. The low rank component of each block tensor is
extracted using iterative tensor singular value thresholding method. The prin-
cipal components of the multi-way data are the concatenation of all the low
rank components of all the block tensors. We give the block tensor incoherence
conditions to guarantee the successful decom- position. This factorization has
similar optimality properties to that of low rank matrix derived from singular
value decomposition. Ex- perimentally, we demonstrate its effectiveness in two
applications, including motion separation for surveillance videos and
illumination normalization for face images.