pwc-archive/papers-with-abstracts huggingface dataset:
WhittleSearch: Interactive Image Search with Relative Attribute Feedback
Authors: Adriana Kovashka, Devi Parikh, Kristen Grauman
Date: 2015-05-15 00:00:00
Tasks: Attribute, Image Retrieval

We propose a novel mode of feedback for image search, where a user describes
which properties of exemplar images should be adjusted in order to more closely
match his/her mental model of the image sought. For example, perusing image
results for a query "black shoes", the user might state, "Show me shoe images
like these, but sportier." Offline, our approach first learns a set of ranking
functions, each of which predicts the relative strength of a nameable attribute
in an image (e.g., sportiness). At query time, the system presents the user
with a set of exemplar images, and the user relates them to his/her target
image with comparative statements. Using a series of such constraints in the
multi-dimensional attribute space, our method iteratively updates its relevance
function and re-ranks the database of images. To determine which exemplar
images receive feedback from the user, we present two variants of the approach:
one where the feedback is user-initiated and another where the feedback is
actively system-initiated. In either case, our approach allows a user to
efficiently "whittle away" irrelevant portions of the visual feature space,
using semantic language to precisely communicate her preferences to the system.
We demonstrate our technique for refining image search for people, products,
and scenes, and we show that it outperforms traditional binary relevance
feedback in terms of search speed and accuracy. In addition, the ordinal nature
of relative attributes helps make our active approach efficient -- both
computationally for the machine when selecting the reference images, and for
the user by requiring less user interaction than conventional passive and
active methods.
Ensemble of Example-Dependent Cost-Sensitive Decision Trees
Authors: Alejandro Correa Bahnsen, Djamila Aouada, Bjorn Ottersten
Date: 2015-05-18 00:00:00
Tasks: Fraud Detection, General Classification, Marketing

Several real-world classification problems are example-dependent
cost-sensitive in nature, where the costs due to misclassification vary between
examples and not only within classes. However, standard classification methods
do not take these costs into account, and assume a constant cost of
misclassification errors. In previous works, some methods that take into
account the financial costs into the training of different algorithms have been
proposed, with the example-dependent cost-sensitive decision tree algorithm
being the one that gives the highest savings. In this paper we propose a new
framework of ensembles of example-dependent cost-sensitive decision-trees. The
framework consists in creating different example-dependent cost-sensitive
decision trees on random subsamples of the training set, and then combining
them using three different combination approaches. Moreover, we propose two new
cost-sensitive combination approaches; cost-sensitive weighted voting and
cost-sensitive stacking, the latter being based on the cost-sensitive logistic
regression method. Finally, using five different databases, from four
real-world applications: credit card fraud detection, churn modeling, credit
scoring and direct marketing, we evaluate the proposed method against
state-of-the-art example-dependent cost-sensitive techniques, namely,
cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision
trees. The results show that the proposed algorithms have better results for
all databases, in the sense of higher savings.
Graph Partitioning via Parallel Submodular Approximation to Accelerate Distributed Machine Learning
Authors: Mu Li, Dave G. Andersen, Alexander J. Smola
Date: 2015-05-18 00:00:00
Tasks: BIG-bench Machine Learning, Distributed Computing, graph partitioning

Distributed computing excels at processing large scale data, but the
communication cost for synchronizing the shared parameters may slow down the
overall performance. Fortunately, the interactions between parameter and data
in many problems are sparse, which admits efficient partition in order to
reduce the communication overhead.
  In this paper, we formulate data placement as a graph partitioning problem.
We propose a distributed partitioning algorithm. We give both theoretical
guarantees and a highly efficient implementation. We also provide a highly
efficient implementation of the algorithm and demonstrate its promising results
on both text datasets and social networks. We show that the proposed algorithm
leads to 1.6x speedup of a state-of-the-start distributed machine learning
system by eliminating 90\% of the network communication.
Simple regret for infinitely many armed bandits
Authors: Alexandra Carpentier, Michal Valko
Date: 2015-05-18 00:00:00

We consider a stochastic bandit problem with infinitely many arms. In this
setting, the learner has no chance of trying all the arms even once and has to
dedicate its limited number of samples only to a certain number of arms. All
previous algorithms for this setting were designed for minimizing the
cumulative regret of the learner. In this paper, we propose an algorithm aiming
at minimizing the simple regret. As in the cumulative regret setting of
infinitely many armed bandits, the rate of the simple regret will depend on a
parameter $\beta$ characterizing the distribution of the near-optimal arms. We
prove that depending on $\beta$, our algorithm is minimax optimal either up to
a multiplicative constant or up to a $\log(n)$ factor. We also provide
extensions to several important cases: when $\beta$ is unknown, in a natural
setting where the near-optimal arms have a small variance, and in the case of
unknown time horizon.
Fractally-organized Connectionist Networks: Conjectures and Preliminary Results
Authors: Vincenzo De Florio
Date: 2015-05-18 00:00:00
Tasks: Attribute

A strict interpretation of connectionism mandates complex networks of simple
components. The question here is, is this simplicity to be interpreted in
absolute terms? I conjecture that absolute simplicity might not be an essential
attribute of connectionism, and that it may be effectively exchanged with a
requirement for relative simplicity, namely simplicity with respect to the
current organizational level. In this paper I provide some elements to the
analysis of the above question. In particular I conjecture that fractally
organized connectionist networks may provide a convenient means to achive what
Leibniz calls an "art of complication", namely an effective way to encapsulate
complexity and practically extend the applicability of connectionism to domains
such as sociotechnical system modeling and design. Preliminary evidence to my
claim is brought by considering the design of the software architecture
designed for the telemonitoring service of Flemish project "Little Sister".
Joint Representation Classification for Collective Face Recognition
Authors: Liping Wang, Songcan Chen
Date: 2015-05-18 00:00:00
Tasks: Classification, Face Recognition, General Classification, Sparse Representation-based Classification

Sparse representation based classification (SRC) is popularly used in many
applications such as face recognition, and implemented in two steps:
representation coding and classification. For a given set of testing images,
SRC codes every image over the base images as a sparse representation then
classifies it to the class with the least representation error. This scheme
utilizes an individual representation rather than the collective one to
classify such a set of images, doing so obviously ignores the correlation among
the given images. In this paper, a joint representation classification (JRC)
for collective face recognition is proposed. JRC takes the correlation of
multiple images as well as a single representation into account. Under the
assumption that the given face images are generally related to each other, JRC
codes all the testing images over the base images simultaneously to facilitate
recognition. To this end, the testing inputs are aligned into a matrix and the
joint representation coding is formulated to a generalized
$l_{2,q}-l_{2,p}$-minimization problem. To uniformly solve the induced
optimization problems for any $q\in[1,2]$ and $p\in (0,2]$, an iterative
quadratic method (IQM) is developed. IQM is proved to be a strict descent
algorithm with convergence to the optimal solution. Moreover, a more practical
IQM is proposed for large-scale case. Experimental results on three public
databases show that the JRC with practical IQM no only saves much computational
cost but also achieves better performance in collective face recognition than
the state-of-the-arts.
Learning Discriminative Stein Kernel for SPD Matrices and Its Applications
Authors: Jianjia Zhang, Lei Wang, Luping Zhou, Wanqing Li
Date: 2014-07-08 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Stein kernel has recently shown promising performance on classifying images
represented by symmetric positive definite (SPD) matrices. It evaluates the
similarity between two SPD matrices through their eigenvalues. In this paper,
we argue that directly using the original eigenvalues may be problematic
because: i) Eigenvalue estimation becomes biased when the number of samples is
inadequate, which may lead to unreliable kernel evaluation; ii) More
importantly, eigenvalues only reflect the property of an individual SPD matrix.
They are not necessarily optimal for computing Stein kernel when the goal is to
discriminate different sets of SPD matrices. To address the two issues in one
shot, we propose a discriminative Stein kernel, in which an extra parameter
vector is defined to adjust the eigenvalues of the input SPD matrices. The
optimal parameter values are sought by optimizing a proxy of classification
performance. To show the generality of the proposed method, three different
kernel learning criteria that are commonly used in the literature are employed
respectively as a proxy. A comprehensive experimental study is conducted on a
variety of image classification tasks to compare our proposed discriminative
Stein kernel with the original Stein kernel and other commonly used methods for
evaluating the similarity between SPD matrices. The experimental results
demonstrate that, the discriminative Stein kernel can attain greater
discrimination and better align with classification tasks by altering the
eigenvalues. This makes it produce higher classification performance than the
original Stein kernel and other commonly used methods.
U-Net: Convolutional Networks for Biomedical Image Segmentation
Authors: Olaf Ronneberger, Philipp Fischer, Thomas Brox
Date: 2015-05-18 00:00:00
Tasks: Cell Segmentation, Cell Tracking, Colorectal Gland Segmentation:, Crack Segmentation, Dichotomous Image Segmentation, GPU, Image Segmentation, Lesion Segmentation, Medical Image Segmentation, Multi-tissue Nucleus Segmentation, Optic Disc Segmentation, Pancreas Segmentation, Retinal Vessel Segmentation, Segmentation, Semantic Segmentation, Skin Cancer Segmentation, Thermal Image Segmentation, Video Polyp Segmentation

There is large consent that successful training of deep networks requires
many thousand annotated training samples. In this paper, we present a network
and training strategy that relies on the strong use of data augmentation to use
the available annotated samples more efficiently. The architecture consists of
a contracting path to capture context and a symmetric expanding path that
enables precise localization. We show that such a network can be trained
end-to-end from very few images and outperforms the prior best method (a
sliding-window convolutional network) on the ISBI challenge for segmentation of
neuronal structures in electron microscopic stacks. Using the same network
trained on transmitted light microscopy images (phase contrast and DIC) we won
the ISBI cell tracking challenge 2015 in these categories by a large margin.
Moreover, the network is fast. Segmentation of a 512x512 image takes less than
a second on a recent GPU. The full implementation (based on Caffe) and the
trained networks are available at
http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .
Global Variational Method for Fingerprint Segmentation by Three-part Decomposition
Authors: Duy Hoang Thai, Carsten Gottschlich
Date: 2015-05-18 00:00:00
Tasks: Segmentation

Verifying an identity claim by fingerprint recognition is a commonplace
experience for millions of people in their daily life, e.g. for unlocking a
tablet computer or smartphone. The first processing step after fingerprint
image acquisition is segmentation, i.e. dividing a fingerprint image into a
foreground region which contains the relevant features for the comparison
algorithm, and a background region. We propose a novel segmentation method by
global three-part decomposition (G3PD). Based on global variational analysis,
the G3PD method decomposes a fingerprint image into cartoon, texture and noise
parts. After decomposition, the foreground region is obtained from the non-zero
coefficients in the texture image using morphological processing. The
segmentation performance of the G3PD method is compared to five
state-of-the-art methods on a benchmark which comprises manually marked ground
truth segmentation for 10560 images. Performance evaluations show that the G3PD
method consistently outperforms existing methods in terms of segmentation
accuracy.
Advances in Artificial Intelligence: Deep Intentions, Shallow Achievements
Authors: Emanuel Diamant
Date: 2015-05-18 00:00:00
Tasks: Deep Learning, Philosophy

Over the past decade, AI has made a remarkable progress due to recently
revived Deep Learning technology. Deep Learning enables to process large
amounts of data using simplified neuron networks that simulate the way in which
the brain works. At the same time, there is another point of view that posits
that brain is processing information, not data. This duality hampered AI
progress for years. To provide a remedy for this situation, I propose a new
definition of information that considers it as a coupling between two separate
entities - physical information (that implies data processing) and semantic
information (that provides physical information interpretation). In such a
case, intelligence arises as a result of information processing. The paper
points on the consequences of this turn for the AI design philosophy.
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation
Authors: Jifeng Dai, Kaiming He, Jian Sun
Date: 2015-03-05 00:00:00
Proceeding: ICCV 2015 12
Tasks: Segmentation, Semantic Segmentation

Recent leading approaches to semantic segmentation rely on deep convolutional
networks trained with human-annotated, pixel-level segmentation masks. Such
pixel-accurate supervision demands expensive labeling effort and limits the
performance of deep networks that usually benefit from more training data. In
this paper, we propose a method that achieves competitive accuracy but only
requires easily obtained bounding box annotations. The basic idea is to iterate
between automatically generating region proposals and training convolutional
networks. These two steps gradually recover segmentation masks for improving
the networks, and vise versa. Our method, called BoxSup, produces competitive
results supervised by boxes only, on par with strong baselines fully supervised
by masks under the same setting. By leveraging a large amount of bounding
boxes, BoxSup further unleashes the power of deep convolutional networks and
yields state-of-the-art results on PASCAL VOC 2012 and PASCAL-CONTEXT.
Place Recognition with Event-based Cameras and a Neural Implementation of SeqSLAM
Authors: Michael Milford, Hanme Kim, Michael Mangan, Stefan Leutenegger, Tom Stone, Barbara Webb, Andrew Davison
Date: 2015-05-18 00:00:00

Event-based cameras offer much potential to the fields of robotics and
computer vision, in part due to their large dynamic range and extremely high
"frame rates". These attributes make them, at least in theory, particularly
suitable for enabling tasks like navigation and mapping on high speed robotic
platforms under challenging lighting conditions, a task which has been
particularly challenging for traditional algorithms and camera sensors. Before
these tasks become feasible however, progress must be made towards adapting and
innovating current RGB-camera-based algorithms to work with event-based
cameras. In this paper we present ongoing research investigating two distinct
approaches to incorporating event-based cameras for robotic navigation: the
investigation of suitable place recognition / loop closure techniques, and the
development of efficient neural implementations of place recognition techniques
that enable the possibility of place recognition using event-based cameras at
very high frame rates using neuromorphic computing hardware.
Scalable Parallel Numerical Constraint Solver Using Global Load Balancing
Authors: Daisuke Ishii, Kazuki Yoshizoe, Toyotaro Suzumura
Date: 2015-05-18 00:00:00

We present a scalable parallel solver for numerical constraint satisfaction
problems (NCSPs). Our parallelization scheme consists of homogeneous worker
solvers, each of which runs on an available core and communicates with others
via the global load balancing (GLB) method. The parallel solver is implemented
with X10 that provides an implementation of GLB as a library. In experiments,
several NCSPs from the literature were solved and attained up to 516-fold
speedup using 600 cores of the TSUBAME2.5 supercomputer.
Emergence-focused design in complex system simulation
Authors: Chris Marriott, Jobran Chebib
Date: 2015-05-18 00:00:00

Emergence is a phenomenon taken for granted in science but also still not
well understood. We have developed a model of artificial genetic evolution
intended to allow for emergence on genetic, population and social levels. We
present the details of the current state of our environment, agent, and
reproductive models. In developing our models we have relied on a principle of
using non-linear systems to model as many systems as possible including
mutation and recombination, gene-environment interaction, agent metabolism,
agent survival, resource gathering and sexual reproduction. In this paper we
review the genetic dynamics that have emerged in our system including
genotype-phenotype divergence, genetic drift, pseudogenes, and gene
duplication. We conclude that emergence-focused design in complex system
simulation is necessary to reproduce the multilevel emergence seen in the
natural world.
Reproducible Evaluation of Pan-Tilt-Zoom Tracking
Authors: Gengjie Chen, Pierre-Luc St-Charles, Wassim Bouachir, Thomas Joeisseint, Guillaume-Alexandre Bilodeau, Robert Bergevin
Date: 2015-05-18 00:00:00

Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in
computer vision for many years. However, it is very difficult to assess the
progress that has been made on this topic because there is no standard
evaluation methodology. The difficulty in evaluating PTZ tracking algorithms
arises from their dynamic nature. In contrast to other forms of tracking, PTZ
tracking involves both locating the target in the image and controlling the
motors of the camera to aim it so that the target stays in its field of view.
This type of tracking can only be performed online. In this paper, we propose a
new evaluation framework based on a virtual PTZ camera. With this framework,
tracking scenarios do not change for each experiment and we are able to
replicate online PTZ camera control and behavior including camera positioning
delays, tracker processing delays, and numerical zoom. We tested our evaluation
framework with the Camshift tracker to show its viability and to establish
baseline results.
A Definition of Happiness for Reinforcement Learning Agents
Authors: Mayank Daswani, Jan Leike
Date: 2015-05-18 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

What is happiness for reinforcement learning agents? We seek a formal
definition satisfying a list of desiderata. Our proposed definition of
happiness is the temporal difference error, i.e. the difference between the
value of the obtained reward and observation and the agent's expectation of
this value. This definition satisfies most of our desiderata and is compatible
with empirical research on humans. We state several implications and discuss
examples.
HEp-2 Cell Image Classification with Deep Convolutional Neural Networks
Authors: Zhimin Gao, Lei Wang, Luping Zhou, Jianjia Zhang
Date: 2015-04-10 00:00:00
Tasks: Classification, Data Augmentation, General Classification, image-classification, Image Classification

Efficient Human Epithelial-2 (HEp-2) cell image classification can facilitate
the diagnosis of many autoimmune diseases. This paper presents an automatic
framework for this classification task, by utilizing the deep convolutional
neural networks (CNNs) which have recently attracted intensive attention in
visual recognition. This paper elaborates the important components of this
framework, discusses multiple key factors that impact the efficiency of
training a deep CNN, and systematically compares this framework with the
well-established image classification models in the literature. Experiments on
benchmark datasets show that i) the proposed framework can effectively
outperform existing models by properly applying data augmentation; ii) our
CNN-based framework demonstrates excellent adaptability across different
datasets, which is highly desirable for classification under varying laboratory
settings. Our system is ranked high in the cell image classification
competition hosted by ICPR 2014.
Visual Semantic Role Labeling
Authors: Saurabh Gupta, Jitendra Malik
Date: 2015-05-17 00:00:00
Tasks: 16k, Action Classification, Action Recognition, Semantic Role Labeling, Temporal Action Localization

In this paper we introduce the problem of Visual Semantic Role Labeling:
given an image we want to detect people doing actions and localize the objects
of interaction. Classical approaches to action recognition either study the
task of action classification at the image or video clip level or at best
produce a bounding box around the person doing the action. We believe such an
output is inadequate and a complete understanding can only come when we are
able to associate objects in the scene to the different semantic roles of the
action. To enable progress towards this goal, we annotate a dataset of 16K
people instances in 10K images with actions they are doing and associate
objects in the scene with different semantic roles for each action. Finally, we
provide a set of baseline algorithms for this task and analyze error modes
providing directions for future work.
Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment
Authors: John S. Breese, David Heckerman
Date: 2013-02-13 00:00:00

We develop and extend existing decision-theoretic methods for troubleshooting
a nonfunctioning device. Traditionally, diagnosis with Bayesian networks has
focused on belief updating---determining the probabilities of various faults
given current observations. In this paper, we extend this paradigm to include
taking actions. In particular, we consider three classes of actions: (1) we can
make observations regarding the behavior of a device and infer likely faults as
in traditional diagnosis, (2) we can repair a component and then observe the
behavior of the device to infer likely faults, and (3) we can change the
configuration of the device, observe its new behavior, and infer the likelihood
of faults. Analysis of latter two classes of troubleshooting actions requires
incorporating notions of persistence into the belief-network formalism used for
probabilistic inference.