pwc-archive/papers-with-abstracts huggingface dataset:
No Spare Parts: Sharing Part Detectors for Image Categorization
Authors: Pascal Mettes, Jan C. van Gemert, Cees G. M. Snoek
Date: 2015-10-16 00:00:00
Tasks: Image Categorization

This work aims for image categorization using a representation of distinctive
parts. Different from existing part-based work, we argue that parts are
naturally shared between image categories and should be modeled as such. We
motivate our approach with a quantitative and qualitative analysis by
backtracking where selected parts come from. Our analysis shows that in
addition to the category parts defining the class, the parts coming from the
background context and parts from other image categories improve categorization
performance. Part selection should not be done separately for each category,
but instead be shared and optimized over all categories. To incorporate part
sharing between categories, we present an algorithm based on AdaBoost to
jointly optimize part sharing and selection, as well as fusion with the global
image representation. We achieve results competitive to the state-of-the-art on
object, scene, and action categories, further improving over deep convolutional
neural networks.
DNA Image Pro -- A Tool for Generating Pixel Patterns using DNA Tile Assembly
Authors: Dixita Limbachiya, Dhaval Trivedi, Manish K. Gupta
Date: 2016-07-12 00:00:00

Self-assembly is a process found everywhere in the Nature. In particular, it
is known that DNA self-assembly is Turing universal. Thus one can do arbitrary
computations or build nano-structures using DNA self-assembly. In order to
understand the DNA self-assembly process, many mathematical models have been
proposed in the literature. In particular, abstract Tile Assembly Model (aTAM)
received much attention. In this work, we investigate pixel pattern generation
using aTAM. For a given image, a tile assembly system is given which can
generate the image by self-assembly process. We also consider image blocks with
specific cyclic pixel patterns (uniform shift and non uniform shift) self
assembly. A software, DNA Image Pro, for generating pixel patterns using DNA
tile assembly is also given.
Adapting Sampling Interval of Sensor Networks Using On-Line Reinforcement Learning
Authors: Gabriel Martins Dias, Maddalena Nurchis, Boris Bellalta
Date: 2016-06-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Monitoring Wireless Sensor Networks (WSNs) are composed of sensor nodes that
report temperature, relative humidity, and other environmental parameters. The
time between two successive measurements is a critical parameter to set during
the WSN configuration because it can impact the WSN's lifetime, the wireless
medium contention and the quality of the reported data. As trends in monitored
parameters can significantly vary between scenarios and within time,
identifying a sampling interval suitable for several cases is also challenging.
In this work, we propose a dynamic sampling rate adaptation scheme based on
reinforcement learning, able to tune sensors' sampling interval on-the-fly,
according to environmental conditions and application requirements. The primary
goal is to set the sampling interval to the best value possible so as to avoid
oversampling and save energy, while not missing environmental changes that can
be relevant for the application. In simulations, our mechanism could reduce up
to 73% the total number of transmissions compared to a fixed strategy and,
simultaneously, keep the average quality of information provided by the WSN.
The inherent flexibility of the reinforcement learning algorithm facilitates
its use in several scenarios, so as to exploit the broad scope of the Internet
of Things.
Bayesian Inference of Bijective Non-Rigid Shape Correspondence
Authors: Matthias Vestner, Roee Litman, Alex Bronstein, Emanuele Rodolà, Daniel Cremers
Date: 2016-07-12 00:00:00
Tasks: Bayesian Inference

Many algorithms for the computation of correspondences between deformable
shapes rely on some variant of nearest neighbor matching in a descriptor space.
Such are, for example, various point-wise correspondence recovery algorithms
used as a postprocessing stage in the functional correspondence framework. In
this paper, we show that such frequently used techniques in practice suffer
from lack of accuracy and result in poor surjectivity. We propose an
alternative recovery technique guaranteeing a bijective correspondence and
producing significantly higher accuracy. We derive the proposed method from a
statistical framework of Bayesian inference and demonstrate its performance on
several challenging deformable 3D shape matching datasets.
Multi-modal image retrieval with random walk on multi-layer graphs
Authors: Renata Khasanova, Xiaowen Dong, Pascal Frossard
Date: 2016-07-12 00:00:00
Tasks: Image Retrieval, Retrieval

The analysis of large collections of image data is still a challenging
problem due to the difficulty of capturing the true concepts in visual data.
The similarity between images could be computed using different and possibly
multimodal features such as color or edge information or even text labels. This
motivates the design of image analysis solutions that are able to effectively
integrate the multi-view information provided by different feature sets. We
therefore propose a new image retrieval solution that is able to sort images
through a random walk on a multi-layer graph, where each layer corresponds to a
different type of information about the image data. We study in depth the
design of the image graph and propose in particular an effective method to
select the edge weights for the multi-layer graph, such that the image ranking
scores are optimised. We then provide extensive experiments in different
real-world photo collections, which confirm the high performance of our new
image retrieval algorithm that generally surpasses state-of-the-art solutions
due to a more meaningful image similarity computation.
Parsimonious Mixed-Effects HodgeRank for Crowdsourced Preference Aggregation
Authors: Qianqian Xu, Jiechao Xiong, Xiaochun Cao, Yuan YAO
Date: 2016-07-12 00:00:00

In crowdsourced preference aggregation, it is often assumed that all the
annotators are subject to a common preference or utility function which
generates their comparison behaviors in experiments. However, in reality
annotators are subject to variations due to multi-criteria, abnormal, or a
mixture of such behaviors. In this paper, we propose a parsimonious
mixed-effects model based on HodgeRank, which takes into account both the fixed
effect that the majority of annotators follows a common linear utility model,
and the random effect that a small subset of annotators might deviate from the
common significantly and exhibits strongly personalized preferences. HodgeRank
has been successfully applied to subjective quality evaluation of multimedia
and resolves pairwise crowdsourced ranking data into a global consensus ranking
and cyclic conflicts of interests. As an extension, our proposed methodology
further explores the conflicts of interests through the random effect in
annotator specific variations. The key algorithm in this paper establishes a
dynamic path from the common utility to individual variations, with different
levels of parsimony or sparsity on personalization, based on newly developed
Linearized Bregman Algorithms with Inverse Scale Space method. Finally the
validity of the methodology are supported by experiments with both simulated
examples and three real-world crowdsourcing datasets, which shows that our
proposed method exhibits better performance (i.e. smaller test error) compared
with HodgeRank due to its parsimonious property.
Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods
Authors: Yuanzhi Li, Andrej Risteski
Date: 2016-07-12 00:00:00
Proceeding: NeurIPS 2016 12

The well known maximum-entropy principle due to Jaynes, which states that
given mean parameters, the maximum entropy distribution matching them is in an
exponential family, has been very popular in machine learning due to its
"Occam's razor" interpretation. Unfortunately, calculating the potentials in
the maximum-entropy distribution is intractable \cite{bresler2014hardness}. We
provide computationally efficient versions of this principle when the mean
parameters are pairwise moments: we design distributions that approximately
match given pairwise moments, while having entropy which is comparable to the
maximum entropy distribution matching those moments.
  We additionally provide surprising applications of the approximate maximum
entropy principle to designing provable variational methods for partition
function calculations for Ising models without any assumptions on the
potentials of the model. More precisely, we show that in every temperature, we
can get approximation guarantees for the log-partition function comparable to
those in the low-temperature limit, which is the setting of optimization of
quadratic forms over the hypercube. \cite{alon2006approximating}
Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks
Authors: Devansh Arpit, Yingbo Zhou, Bhargava U. Kota, Venu Govindaraju
Date: 2016-03-04 00:00:00

While the authors of Batch Normalization (BN) identify and address an
important problem involved in training deep networks-- Internal Covariate
Shift-- the current solution has certain drawbacks. Specifically, BN depends on
batch statistics for layerwise input normalization during training which makes
the estimates of mean and standard deviation of input (distribution) to hidden
layers inaccurate for validation due to shifting parameter values (especially
during initial training epochs). Also, BN cannot be used with batch-size 1
during training. We address these drawbacks by proposing a non-adaptive
normalization technique for removing internal covariate shift, that we call
Normalization Propagation. Our approach does not depend on batch statistics,
but rather uses a data-independent parametric estimate of mean and
standard-deviation in every layer thus being computationally faster compared
with BN. We exploit the observation that the pre-activation before Rectified
Linear Units follow Gaussian distribution in deep networks, and that once the
first and second order statistics of any given dataset are normalized, we can
forward propagate this normalization without the need for recalculating the
approximate statistics for hidden layers.
Extended Graded Modalities in Strategy Logic
Authors: Benjamin Aminof, Vadim Malvone, Aniello Murano, Sasha Rubin
Date: 2016-07-12 00:00:00

Strategy Logic (SL) is a logical formalism for strategic reasoning in
multi-agent systems. Its main feature is that it has variables for strategies
that are associated to specific agents with a binding operator. We introduce
Graded Strategy Logic (GradedSL), an extension of SL by graded quantifiers over
tuples of strategy variables, i.e., "there exist at least g different tuples
(x_1,...,x_n) of strategies" where g is a cardinal from the set N union
{aleph_0, aleph_1, 2^aleph_0}. We prove that the model-checking problem of
GradedSL is decidable. We then turn to the complexity of fragments of GradedSL.
When the g's are restricted to finite cardinals, written GradedNSL, the
complexity of model-checking is no harder than for SL, i.e., it is
non-elementary in the quantifier rank. We illustrate our formalism by showing
how to count the number of different strategy profiles that are Nash equilibria
(NE), or subgame-perfect equilibria (SPE). By analyzing the structure of the
specific formulas involved, we conclude that the important problems of checking
for the existence of a unique NE or SPE can both be solved in 2ExpTime, which
is not harder than merely checking for the existence of such equilibria.
Iterative Judgment Aggregation
Authors: Marija Slavkovik, Wojciech Jamroga
Date: 2016-04-21 00:00:00
Tasks: Decision Making

Judgment aggregation problems form a class of collective decision-making
problems represented in an abstract way, subsuming some well known problems
such as voting. A collective decision can be reached in many ways, but a direct
one-step aggregation of individual decisions is arguably most studied. Another
way to reach collective decisions is by iterative consensus building --
allowing each decision-maker to change their individual decision in response to
the choices of the other agents until a consensus is reached. Iterative
consensus building has so far only been studied for voting problems. Here we
propose an iterative judgment aggregation algorithm, based on movements in an
undirected graph, and we study for which instances it terminates with a
consensus. We also compare the computational complexity of our iterative
procedure with that of related judgment aggregation operators.
RGBD Salient Object Detection via Deep Fusion
Authors: Liangqiong Qu, Shengfeng He, Jiawei Zhang, Jiandong Tian, Yandong Tang, Qingxiong Yang
Date: 2016-07-12 00:00:00
Tasks: Object, object-detection, Object Detection, RGB-D Salient Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

Numerous efforts have been made to design different low level saliency cues
for the RGBD saliency detection, such as color or depth contrast features,
background and color compactness priors. However, how these saliency cues
interact with each other and how to incorporate these low level saliency cues
effectively to generate a master saliency map remain a challenging problem. In
this paper, we design a new convolutional neural network (CNN) to fuse
different low level saliency cues into hierarchical features for automatically
detecting salient objects in RGBD images. In contrast to the existing works
that directly feed raw image pixels to the CNN, the proposed method takes
advantage of the knowledge in traditional saliency detection by adopting
various meaningful and well-designed saliency feature vectors as input. This
can guide the training of CNN towards detecting salient object more effectively
due to the reduced learning ambiguity. We then integrate a Laplacian
propagation framework with the learned CNN to extract a spatially consistent
saliency map by exploiting the intrinsic structure of the input image.
Extensive quantitative and qualitative experimental evaluations on three
datasets demonstrate that the proposed method consistently outperforms
state-of-the-art methods.
Populations can be essential in tracking dynamic optima
Authors: Duc-Cuong Dang, Thomas Jansen, Per Kristian Lehre
Date: 2016-07-12 00:00:00
Tasks: Evolutionary Algorithms

Real-world optimisation problems are often dynamic. Previously good solutions
must be updated or replaced due to changes in objectives and constraints. It is
often claimed that evolutionary algorithms are particularly suitable for
dynamic optimisation because a large population can contain different solutions
that may be useful in the future. However, rigorous theoretical demonstrations
for how populations in dynamic optimisation can be essential are sparse and
restricted to special cases.
  This paper provides theoretical explanations of how populations can be
essential in evolutionary dynamic optimisation in a general and natural
setting. We describe a natural class of dynamic optimisation problems where a
sufficiently large population is necessary to keep track of moving optima
reliably. We establish a relationship between the population-size and the
probability that the algorithm loses track of the optimum.
Predicting the evolution of stationary graph signals
Authors: Andreas Loukas, Nathanael Perraudin
Date: 2016-07-12 00:00:00

An emerging way of tackling the dimensionality issues arising in the modeling
of a multivariate process is to assume that the inherent data structure can be
captured by a graph. Nevertheless, though state-of-the-art graph-based methods
have been successful for many learning tasks, they do not consider
time-evolving signals and thus are not suitable for prediction. Based on the
recently introduced joint stationarity framework for time-vertex processes,
this letter considers multivariate models that exploit the graph topology so as
to facilitate the prediction. The resulting method yields similar accuracy to
the joint (time-graph) mean-squared error estimator but at lower complexity,
and outperforms purely time-based methods.
Camera Elevation Estimation from a Single Mountain Landscape Photograph
Authors: Martin Cadik, Jan Vasicek, Michal Hradis, Filip Radenovic, Ondrej Chum
Date: 2016-07-12 00:00:00

This work addresses the problem of camera elevation estimation from a single
photograph in an outdoor environment. We introduce a new benchmark dataset of
one-hundred thousand images with annotated camera elevation called Alps100K. We
propose and experimentally evaluate two automatic data-driven approaches to
camera elevation estimation: one based on convolutional neural networks, the
other on local features. To compare the proposed methods to human performance,
an experiment with 100 subjects is conducted. The experimental results show
that both proposed approaches outperform humans and that the best result is
achieved by their combination.
From Dependence to Causation
Authors: David Lopez-Paz
Date: 2016-07-12 00:00:00
Tasks: BIG-bench Machine Learning, Causal Inference, Object Recognition

Machine learning is the science of discovering statistical dependencies in
data, and the use of those dependencies to perform predictions. During the last
decade, machine learning has made spectacular progress, surpassing human
performance in complex tasks such as object recognition, car driving, and
computer gaming. However, the central role of prediction in machine learning
avoids progress towards general-purpose artificial intelligence. As one way
forward, we argue that causal inference is a fundamental component of human
intelligence, yet ignored by learning algorithms.
  Causal inference is the problem of uncovering the cause-effect relationships
between the variables of a data generating system. Causal structures provide
understanding about how these systems behave under changing, unseen
environments. In turn, knowledge about these causal dynamics allows to answer
"what if" questions, describing the potential responses of the system under
hypothetical manipulations and interventions. Thus, understanding cause and
effect is one step from machine learning towards machine reasoning and machine
intelligence. But, currently available causal inference algorithms operate in
specific regimes, and rely on assumptions that are difficult to verify in
practice.
  This thesis advances the art of causal inference in three different ways.
First, we develop a framework for the study of statistical dependence based on
copulas and random features. Second, we build on this framework to interpret
the problem of causal inference as the task of distribution classification,
yielding a family of novel causal inference algorithms. Third, we discover
causal structures in convolutional neural network features using our
algorithms. The algorithms presented in this thesis are scalable, exhibit
strong theoretical guarantees, and achieve state-of-the-art performance in a
variety of real-world benchmarks.
Automatic Bridge Bidding Using Deep Reinforcement Learning
Authors: Chih-Kuan Yeh, Hsuan-Tien Lin
Date: 2016-07-12 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Bridge is among the zero-sum games for which artificial intelligence has not
yet outperformed expert human players. The main difficulty lies in the bidding
phase of bridge, which requires cooperative decision making under partial
information. Existing artificial intelligence systems for bridge bidding rely
on and are thus restricted by human-designed bidding systems or features. In
this work, we propose a pioneering bridge bidding system without the aid of
human domain knowledge. The system is based on a novel deep reinforcement
learning model, which extracts sophisticated features and learns to bid
automatically based on raw card data. The model includes an
upper-confidence-bound algorithm and additional techniques to achieve a balance
between exploration and exploitation. Our experiments validate the promising
performance of our proposed model. In particular, the model advances from
having no knowledge about bidding to achieving superior performance when
compared with a champion-winning computer bridge program that implements a
human-designed bidding system.
Boundary conditions for Shape from Shading
Authors: Lyes Abada, Saliha Aouat, Omar El Farouk Bourahla
Date: 2016-07-12 00:00:00
Tasks: 3D Reconstruction, Object

The Shape From Shading is one of a computer vision field. It studies the 3D
reconstruction of an object from a single grayscale image. The difficulty of
this field can be expressed in the local ambiguity (convex / concave). J.Shi
and Q.Zhu have proposed a method (Global View) to solve the local ambiguity.
This method based on the graph theory and the relationship between the singular
points. In this work we will show that the use of singular points is not
sufficient and requires further information on the object to resolve this
ambiguity.
A Machine learning approach for Shape From Shading
Authors: Lyes Abada, Saliha Aouat
Date: 2016-07-12 00:00:00
Tasks: BIG-bench Machine Learning

The aim of Shape From Shading (SFS) problem is to reconstruct the relief of
an object from a single gray level image. In this paper we present a new method
to solve the problem of SFS using Machine learning method. Our approach belongs
to Local resolution category. The orientation of each part of the object is
represented by the perpendicular vector to the surface (Normal Vector), this
vector is defined by two angles SLANT and TILT, such as the TILT is the angle
between the normal vector and Z-axis, and the SLANT is the angle between the
the X-axis and the projection of the normal to the plane. The TILT can be
determined from the gray level, the unknown is the SLANT. To calculate the
normal of each part of the surface (pixel) a supervised Machine learning method
has been proposed. This method divided into three steps: the first step is the
preparation of the training data from 3D mathematical functions and synthetic
objects. The second step is the creation of database of examples from 3D
objects (off-line process). The third step is the application of test images
(on-line process). The idea is to find for each pixel of the test image the
most similar element in the examples database using a similarity value.
On 1-Laplacian Elliptic Equations Modeling Magnetic Resonance Image Rician Denoising
Authors: Adrian Martin, Emanuele Schiavi, Sergio Segura de Leon
Date: 2015-10-10 00:00:00
Tasks: Denoising

Modeling magnitude Magnetic Resonance Images (MRI) rician denoising in a
Bayesian or generalized Tikhonov framework using Total Variation (TV) leads
naturally to the consideration of nonlinear elliptic equations. These involve
the so called $1$-Laplacian operator and special care is needed to properly
formulate the problem. The rician statistics of the data are introduced through
a singular equation with a reaction term defined in terms of modified first
order Bessel functions. An existence theory is provided here together with
other qualitative properties of the solutions. Remarkably, each positive global
minimum of the associated functional is one of such solutions. Moreover, we
directly solve this non--smooth non--convex minimization problem using a
convergent Proximal Point Algorithm. Numerical results based on synthetic and
real MRI demonstrate a better performance of the proposed method when compared
to previous TV based models for rician denoising which regularize or convexify
the problem. Finally, an application on real Diffusion Tensor Images, a
strongly affected by rician noise MRI modality, is presented and discussed.