pwc-archive/papers-with-abstracts huggingface dataset:
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
Authors: Adith Swaminathan, Thorsten Joachims
Date: 2015-02-09 00:00:00
Tasks: counterfactual, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

We develop a learning principle and an efficient algorithm for batch learning
from logged bandit feedback. This learning setting is ubiquitous in online
systems (e.g., ad placement, web search, recommendation), where an algorithm
makes a prediction (e.g., ad ranking) for a given input (e.g., query) and
observes bandit feedback (e.g., user clicks on presented ads). We first address
the counterfactual nature of the learning problem through propensity scoring.
Next, we prove generalization error bounds that account for the variance of the
propensity-weighted empirical risk estimator. These constructive bounds give
rise to the Counterfactual Risk Minimization (CRM) principle. We show how CRM
can be used to derive a new learning method -- called Policy Optimizer for
Exponential Models (POEM) -- for learning stochastic linear rules for
structured output prediction. We present a decomposition of the POEM objective
that enables efficient stochastic gradient optimization. POEM is evaluated on
several multi-label classification problems showing substantially improved
robustness and generalization performance compared to the state-of-the-art.
Harmonic Exponential Families on Manifolds
Authors: Taco S. Cohen, Max Welling
Date: 2015-05-17 00:00:00
Tasks: Motion Estimation

In a range of fields including the geosciences, molecular biology, robotics
and computer vision, one encounters problems that involve random variables on
manifolds. Currently, there is a lack of flexible probabilistic models on
manifolds that are fast and easy to train. We define an extremely flexible
class of exponential family distributions on manifolds such as the torus,
sphere, and rotation groups, and show that for these distributions the gradient
of the log-likelihood can be computed efficiently using a non-commutative
generalization of the Fast Fourier Transform (FFT). We discuss applications to
Bayesian camera motion estimation (where harmonic exponential families serve as
conjugate priors), and modelling of the spatial distribution of earthquakes on
the surface of the earth. Our experimental results show that harmonic densities
yield a significantly higher likelihood than the best competing method, while
being orders of magnitude faster to train.
TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking
Authors: Pingmei Xu, Krista A. Ehinger, yinda zhang, Adam Finkelstein, Sanjeev R. Kulkarni, Jianxiong Xiao
Date: 2015-04-25 00:00:00
Tasks: Saliency Prediction

Traditional eye tracking requires specialized hardware, which means
collecting gaze data from many observers is expensive, tedious and slow.
Therefore, existing saliency prediction datasets are order-of-magnitudes
smaller than typical datasets for other vision recognition tasks. The small
size of these datasets limits the potential for training data intensive
algorithms, and causes overfitting in benchmark evaluation. To address this
deficiency, this paper introduces a webcam-based gaze tracking system that
supports large-scale, crowdsourced eye tracking deployed on Amazon Mechanical
Turk (AMTurk). By a combination of careful algorithm and gaming protocol
design, our system obtains eye tracking data for saliency prediction comparable
to data gathered in a traditional lab setting, with relatively lower cost and
less effort on the part of the researchers. Using this tool, we build a
saliency dataset for a large number of natural images. We will open-source our
tool and provide a web server where researchers can upload their images to get
eye tracking results from AMTurk.
Kinect Range Sensing: Structured-Light versus Time-of-Flight Kinect
Authors: Hamed Sarbolandi, Damien Lefloch, Andreas Kolb
Date: 2015-05-20 00:00:00

Recently, the new Kinect One has been issued by Microsoft, providing the next
generation of real-time range sensing devices based on the Time-of-Flight (ToF)
principle. As the first Kinect version was using a structured light approach,
one would expect various differences in the characteristics of the range data
delivered by both devices. This paper presents a detailed and in-depth
comparison between both devices. In order to conduct the comparison, we propose
a framework of seven different experimental setups, which is a generic basis
for evaluating range cameras such as Kinect. The experiments have been designed
with the goal to capture individual effects of the Kinect devices as isolatedly
as possible and in a way, that they can also be adopted, in order to apply them
to any other range sensing device. The overall goal of this paper is to provide
a solid insight into the pros and cons of either device. Thus, scientists that
are interested in using Kinect range sensing cameras in their specific
application scenario can directly assess the expected, specific benefits and
potential problem of either device.
DRAW: A Recurrent Neural Network For Image Generation
Authors: Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra
Date: 2015-02-16 00:00:00
Tasks: Foveation, Image Generation

This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye.
Knowledge, Justification, and Adequate Reasons
Authors: Paul Égré, Paul Marty, Bryan Renne
Date: 2014-12-04 00:00:00

Is knowledge definable as justified true belief ("JTB")? We argue that one can legitimately answer positively or negatively, depending on whether or not one's true belief is justified by what we call adequate reasons. To facilitate our argument we introduce a simple propositional logic of reason-based belief, and give an axiomatic characterization of the notion of adequacy for reasons. We show that this logic is sufficiently flexible to accommodate various useful features, including quantification over reasons. We use our framework to contrast two notions of JTB: one internalist, the other externalist. We argue that Gettier cases essentially challenge the internalist notion but not the externalist one. Our approach commits us to a form of infallibilism about knowledge, but it also leaves us with a puzzle, namely whether knowledge involves the possession of only adequate reasons, or leaves room for some inadequate reasons. We favor the latter position, which reflects a milder and more realistic version of infallibilism.
Towards Large-scale Inconsistency Measurement
Authors: Matthias Thimm
Date: 2015-05-20 00:00:00

We investigate the problem of inconsistency measurement on large knowledge
bases by considering stream-based inconsistency measurement, i.e., we
investigate inconsistency measures that cannot consider a knowledge base as a
whole but process it within a stream. For that, we present, first, a novel
inconsistency measure that is apt to be applied to the streaming case and,
second, stream-based approximations for the new and some existing inconsistency
measures. We conduct an extensive empirical analysis on the behavior of these
inconsistency measures on large knowledge bases, in terms of runtime, accuracy,
and scalability. We conclude that for two of these measures, the approximation
of the new inconsistency measure and an approximation of the contension
inconsistency measure, large-scale inconsistency measurement is feasible.
Towards a Simulation-Based Programming Paradigm for AI applications
Authors: Jörg Pührer
Date: 2015-05-20 00:00:00

We present initial ideas for a programming paradigm based on simulation that
is targeted towards applications of artificial intelligence (AI). The approach
aims at integrating techniques from different areas of AI and is based on the
idea that simulated entities may freely exchange data and behavioural patterns.
We define basic notions of a simulation-based programming paradigm and show how
it can be used for implementing AI applications.
Towards Efficient Evolving Multi-Context Systems (Preliminary Report)
Authors: Ricardo Gonçalves, Matthias Knorr, João Leite
Date: 2015-05-20 00:00:00

Managed Multi-Context Systems (mMCSs) provide a general framework for
integrating knowledge represented in heterogeneous KR formalisms. Recently,
evolving Multi-Context Systems (eMCSs) have been introduced as an extension of
mMCSs that add the ability to both react to, and reason in the presence of
commonly temporary dynamic observations, and evolve by incorporating new
knowledge. However, the general complexity of such an expressive formalism may
simply be too high in cases where huge amounts of information have to be
processed within a limited short amount of time, or even instantaneously. In
this paper, we investigate under which conditions eMCSs may scale in such
situations and we show that such polynomial eMCSs can be applied in a practical
use case.
On Minimal Change in Evolving Multi-Context Systems (Preliminary Report)
Authors: Ricardo Gonçalves, Matthias Knorr, João Leite
Date: 2015-05-20 00:00:00

Managed Multi-Context Systems (mMCSs) provide a general framework for
integrating knowledge represented in heterogeneous KR formalisms. However,
mMCSs are essentially static as they were not designed to run in a dynamic
scenario. Some recent approaches, among them evolving Multi-Context Systems
(eMCSs), extend mMCSs by allowing not only the ability to integrate knowledge
represented in heterogeneous KR formalisms, but at the same time to both react
to, and reason in the presence of commonly temporary dynamic observations, and
evolve by incorporating new knowledge. The notion of minimal change is a
central notion in dynamic scenarios, specially in those that admit several
possible alternative evolutions. Since eMCSs combine heterogeneous KR
formalisms, each of which may require different notions of minimal change, the
study of minimal change in eMCSs is an interesting and highly non-trivial
problem. In this paper, we study the notion of minimal change in eMCSs, and
discuss some alternative minimal change criteria.
Asynchronous Multi-Context Systems
Authors: Stefan Ellmauthaler, Jörg Pührer
Date: 2015-05-20 00:00:00

In this work, we present asynchronous multi-context systems (aMCSs), which
provide a framework for loosely coupling different knowledge representation
formalisms that allows for online reasoning in a dynamic environment. Systems
of this kind may interact with the outside world via input and output streams
and may therefore react to a continuous flow of external information. In
contrast to recent proposals, contexts in an aMCS communicate with each other
in an asynchronous way which fits the needs of many application domains and is
beneficial for scalability. The federal semantics of aMCSs renders our
framework an integration approach rather than a knowledge representation
formalism itself. We illustrate the introduced concepts by means of an example
scenario dealing with rescue services. In addition, we compare aMCSs to
reactive multi-context systems and describe how to simulate the latter with our
novel approach.
Multi-Context Systems for Reactive Reasoning in Dynamic Environments
Authors: Gerhard Brewka, Stefan Ellmauthaler, Jörg Pührer
Date: 2015-05-20 00:00:00

We show in this paper how managed multi-context systems (mMCSs) can be turned
into a reactive formalism suitable for continuous reasoning in dynamic
environments. We extend mMCSs with (abstract) sensors and define the notion of
a run of the extended systems. We then show how typical problems arising in
online reasoning can be addressed: handling potentially inconsistent sensor
input, modeling intelligent forms of forgetting, selective integration of
knowledge, and controlling the reasoning effort spent by contexts, like setting
contexts to an idle mode. We also investigate the complexity of some important
related decision problems and discuss different design choices which are given
to the knowledge engineer.
Towards Ideal Semantics for Analyzing Stream Reasoning
Authors: Harald Beck, Minh Dao-Tran, Thomas Eiter, Michael Fink
Date: 2015-05-20 00:00:00
Tasks: Logical Reasoning

The rise of smart applications has drawn interest to logical reasoning over
data streams. Recently, different query languages and stream
processing/reasoning engines were proposed in different communities. However,
due to a lack of theoretical foundations, the expressivity and semantics of
these diverse approaches are given only informally. Towards clear
specifications and means for analytic study, a formal framework is needed to
define their semantics in precise terms. To this end, we present a first step
towards an ideal semantics that allows for exact descriptions and comparisons
of stream reasoning systems.
Reactive Reasoning with the Event Calculus
Authors: Alexander Artikis, Marek Sergot, Georgios Paliouras
Date: 2015-05-20 00:00:00

Systems for symbolic event recognition accept as input a stream of
time-stamped events from sensors and other computational devices, and seek to
identify high-level composite events, collections of events that satisfy some
pattern. RTEC is an Event Calculus dialect with novel implementation and
'windowing' techniques that allow for efficient event recognition, scalable to
large data streams. RTEC can deal with applications where event data arrive
with a (variable) delay from, and are revised by, the underlying sources. RTEC
can update already recognised events and recognise new events when data arrive
with a delay or following data revision. Our evaluation shows that RTEC can
support real-time event recognition and is capable of meeting the performance
requirements identified in a recent survey of event processing use cases.
DropSample: A New Training Method to Enhance Deep Convolutional Neural Networks for Large-Scale Unconstrained Handwritten Chinese Character Recognition
Authors: Weixin Yang, Lianwen Jin, DaCheng Tao, Zecheng Xie, Ziyong Feng
Date: 2015-05-20 00:00:00

Inspired by the theory of Leitners learning box from the field of psychology,
we propose DropSample, a new method for training deep convolutional neural
networks (DCNNs), and apply it to large-scale online handwritten Chinese
character recognition (HCCR). According to the principle of DropSample, each
training sample is associated with a quota function that is dynamically
adjusted on the basis of the classification confidence given by the DCNN
softmax output. After a learning iteration, samples with low confidence will
have a higher probability of being selected as training data in the next
iteration; in contrast, well-trained and well-recognized samples with very high
confidence will have a lower probability of being involved in the next training
iteration and can be gradually eliminated. As a result, the learning process
becomes more efficient as it progresses. Furthermore, we investigate the use of
domain-specific knowledge to enhance the performance of DCNN by adding a domain
knowledge layer before the traditional CNN. By adopting DropSample together
with different types of domain-specific knowledge, the accuracy of HCCR can be
improved efficiently. Experiments on the CASIA-OLHDWB 1.0, CASIA-OLHWDB 1.1,
and ICDAR 2013 online HCCR competition datasets yield outstanding recognition
rates of 97.33%, 97.06%, and 97.51% respectively, all of which are
significantly better than the previous best results reported in the literature.
Algorithmic Analysis of Edge Ranking and Profiling for MTF Determination of an Imaging System
Authors: Poorna Banerjee Dasgupta
Date: 2015-05-20 00:00:00
Tasks: Edge Detection

Edge detection is one of the most principal techniques for detecting
discontinuities in the gray levels of image pixels. The Modulation Transfer
Function (MTF) is one of the main criteria for assessing imaging quality and is
a parameter frequently used for measuring the sharpness of an imaging system.
In order to determine the MTF, it is essential to determine the best edge from
the target image so that an edge profile can be developed and then the line
spread function and hence the MTF, can be computed accordingly. For regular
image sizes, the human visual system is adept enough to identify suitable edges
from the image. But considering huge image datasets, such as those obtained
from satellites, the image size may range in few gigabytes and in such a case,
manual inspection of images for determination of the best suitable edge is not
plausible and hence, edge profiling tasks have to be automated. This paper
presents a novel, yet simple, algorithm for edge ranking and detection from
image data-sets for MTF computation, which is ideal for automation on
vectorised graphical processing units.
Measuring Visibility using Atmospheric Transmission and Digital Surface Model
Authors: Jean-Philippe Andreu, Stefan Mayer, Karlheinz Gutjahr, Harald Ganster
Date: 2015-05-20 00:00:00

Reliable and exact assessment of visibility is essential for safe air
traffic. In order to overcome the drawbacks of the currently subjective reports
from human observers, we present an approach to automatically derive visibility
measures by means of image processing. It first exploits image based estimation
of the atmospheric transmission describing the portion of the light that is not
scattered by atmospheric phenomena (e.g., haze, fog, smoke) and reaches the
camera. Once the atmospheric transmission is estimated, a 3D representation of
the vicinity (digital surface model: DMS) is used to compute depth measurements
for the haze-free pixels and then derive a global visibility estimation for the
airport. Results on foggy images demonstrate the validity of the proposed
method.
Biometric Matching and Fusion System for Fingerprints from Non-Distal Phalanges
Authors: Mehmet Kayaoglu, Berkay Topcu, Umut Uludag
Date: 2015-05-15 00:00:00

Market research indicates that fingerprints are still the most popular
biometric modality for personal authentication. Even with the onset of new
modalities (e.g. vein matching), many applications within different domains
(e-ID, banking, border control...) and geographies rely on fingerprints
obtained from the distal phalanges (a.k.a. sections, digits) of the human hand
structure. Motivated by the problem of poor quality distal fingerprint images
affecting a non-trivial portion of the population (which decreases associated
authentication accuracy), we designed and tested a multifinger, multiphalanx
fusion scheme, that combines minutiae matching scores originating from
non-distal (ie. middle and proximal) phalanges based on (i) simple sum fusion,
(ii) NFIQ image-quality-based fusion, and (iii) phalanx-type-based fusion.
Utilizing a medium-size (50 individuals, 400 unique fingers, 1600 distinct
images) database collected in our laboratory with a commercial optical
fingerprint sensor, and a commercial minutiae extractor & matcher (without any
modification), allowed us to simulate a real-world fingerprint authentication
setting. Detailed analyses including ROC curves with statistical confidence
intervals show that the proposed system can be a viable alternative for cases
where (i) distal phalanx images are not usable (e.g. due to missing digits, or
low quality finger surface due to manual labor), and (ii) switching to a new
biometric modality (e.g. iris) is not possible due to economical or
infrastructure limits. Further, we show that when distal phalanx images are in
fact usable, combining them with images from other phalanges increases accuracy
as well.
Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side Information
Authors: Martin Kasparick, Renato L. G. Cavalcante, Stefan Valentin, Slawomir Stanczak, Masahiro Yukawa
Date: 2014-04-03 00:00:00

In this paper, we address the problem of reconstructing coverage maps from path-loss measurements in cellular networks. We propose and evaluate two kernel-based adaptive online algorithms as an alternative to typical offline methods. The proposed algorithms are application-tailored extensions of powerful iterative methods such as the adaptive projected subgradient method and a state-of-the-art adaptive multikernel method. Assuming that the moving trajectories of users are available, it is shown how side information can be incorporated in the algorithms to improve their convergence performance and the quality of the estimation. The complexity is significantly reduced by imposing sparsity-awareness in the sense that the algorithms exploit the compressibility of the measurement data to reduce the amount of data which is saved and processed. Finally, we present extensive simulations based on realistic data to show that our algorithms provide fast, robust estimates of coverage maps in real-world scenarios. Envisioned applications include path-loss prediction along trajectories of mobile users as a building block for anticipatory buffering or traffic offloading.
Live Video Synopsis for Multiple Cameras
Authors: Yedid Hoshen, Shmuel Peleg
Date: 2015-05-20 00:00:00
Tasks: Decision Making, Video Synopsis

Video surveillance cameras generate most of recorded video, and there is far
more recorded video than operators can watch. Much progress has recently been
made using summarization of recorded video, but such techniques do not have
much impact on live video surveillance.
  We assume a camera hierarchy where a Master camera observes the
decision-critical region, and one or more Slave cameras observe regions where
past activity is important for making the current decision. We propose that
when people appear in the live Master camera, the Slave cameras will display
their past activities, and the operator could use past information for
real-time decision making.
  The basic units of our method are action tubes, representing objects and
their trajectories over time. Our object-based method has advantages over frame
based methods, as it can handle multiple people, multiple activities for each
person, and can address re-identification uncertainty.
Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representation on Sequence Labelling Tasks
Authors: Lizhen Qu, Gabriela Ferraro, Liyuan Zhou, Weiwei Hou, Nathan Schneider, Timothy Baldwin
Date: 2015-04-21 00:00:00
Tasks: Chunking, NER, POS, POS Tagging, Small Data Image Classification, Word Embeddings

Word embeddings -- distributed word representations that can be learned from
unlabelled data -- have been shown to have high utility in many natural
language processing applications. In this paper, we perform an extrinsic
evaluation of five popular word embedding methods in the context of four
sequence labelling tasks: POS-tagging, syntactic chunking, NER and MWE
identification. A particular focus of the paper is analysing the effects of
task-based updating of word representations. We show that when using word
embeddings as features, as few as several hundred training instances are
sufficient to achieve competitive results, and that word embeddings lead to
improvements over OOV words and out of domain. Perhaps more surprisingly, our
results indicate there is little difference between the different word
embedding methods, and that simple Brown clusters are often competitive with
word embeddings across all tasks we consider.