10,401 research outputs found
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval
In Online Learning to Rank (OLTR) the aim is to find an optimal ranking model
by interacting with users. When learning from user behavior, systems must
interact with users while simultaneously learning from those interactions.
Unlike other Learning to Rank (LTR) settings, existing research in this field
has been limited to linear models. This is due to the speed-quality tradeoff
that arises when selecting models: complex models are more expressive and can
find the best rankings but need more user interactions to do so, a requirement
that risks frustrating users during training. Conversely, simpler models can be
optimized on fewer interactions and thus provide a better user experience, but
they will converge towards suboptimal rankings. This tradeoff creates a
deadlock, since novel models will not be able to improve either the user
experience or the final convergence point, without sacrificing the other. Our
contribution is twofold. First, we introduce a fast OLTR model called Sim-MGD
that addresses the speed aspect of the speed-quality tradeoff. Sim-MGD ranks
documents based on similarities with reference documents. It converges rapidly
and, hence, gives a better user experience but it does not converge towards the
optimal rankings. Second, we contribute Cascading Multileave Gradient Descent
(C-MGD) for OLTR that directly addresses the speed-quality tradeoff by using a
cascade that enables combinations of the best of two worlds: fast learning and
high quality final convergence. C-MGD can provide the better user experience of
Sim-MGD while maintaining the same convergence as the state-of-the-art MGD
model. This opens the door for future work to design new models for OLTR
without having to deal with the speed-quality tradeoff.Comment: CIKM 2017, Proceedings of the 2017 ACM on Conference on Information
and Knowledge Managemen
Quasi-SLCA based Keyword Query Processing over Probabilistic XML Data
The probabilistic threshold query is one of the most common queries in
uncertain databases, where a result satisfying the query must be also with
probability meeting the threshold requirement. In this paper, we investigate
probabilistic threshold keyword queries (PrTKQ) over XML data, which is not
studied before. We first introduce the notion of quasi-SLCA and use it to
represent results for a PrTKQ with the consideration of possible world
semantics. Then we design a probabilistic inverted (PI) index that can be used
to quickly return the qualified answers and filter out the unqualified ones
based on our proposed lower/upper bounds. After that, we propose two efficient
and comparable algorithms: Baseline Algorithm and PI index-based Algorithm. To
accelerate the performance of algorithms, we also utilize probability density
function. An empirical study using real and synthetic data sets has verified
the effectiveness and the efficiency of our approaches
On the Modeling of Musical Solos as Complex Networks
Notes in a musical piece are building blocks employed in non-random ways to
create melodies. It is the "interaction" among a limited amount of notes that
allows constructing the variety of musical compositions that have been written
in centuries and within different cultures. Networks are a modeling tool that
is commonly employed to represent a set of entities interacting in some way.
Thus, notes composing a melody can be seen as nodes of a network that are
connected whenever these are played in sequence. The outcome of such a process
results in a directed graph. By using complex network theory, some main metrics
of musical graphs can be measured, which characterize the related musical
pieces. In this paper, we define a framework to represent melodies as networks.
Then, we provide an analysis on a set of guitar solos performed by main
musicians. Results of this study indicate that the presented model can have an
impact on audio and multimedia applications such as music classification,
identification, e-learning, automatic music generation, multimedia
entertainment.Comment: to appear in Information Science, Elsevier. Please cite the paper
including such information. arXiv admin note: text overlap with
arXiv:1603.0497
Fidelity-Weighted Learning
Training deep neural networks requires many training samples, but in practice
training labels are expensive to obtain and may be of varying quality, as some
may be from trusted expert labelers while others might be from heuristics or
other sources of weak supervision such as crowd-sourcing. This creates a
fundamental quality versus-quantity trade-off in the learning process. Do we
learn from the small amount of high-quality data or the potentially large
amount of weakly-labeled data? We argue that if the learner could somehow know
and take the label-quality into account when learning the data representation,
we could get the best of both worlds. To this end, we propose
"fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach
for training deep neural networks using weakly-labeled data. FWL modulates the
parameter updates to a student network (trained on the task we care about) on a
per-sample basis according to the posterior confidence of its label-quality
estimated by a teacher (who has access to the high-quality labels). Both
student and teacher are learned from the data. We evaluate FWL on two tasks in
information retrieval and natural language processing where we outperform
state-of-the-art alternative semi-supervised methods, indicating that our
approach makes better use of strong and weak labels, and leads to better
task-dependent data representations.Comment: Published as a conference paper at ICLR 201
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
- …