2,554 research outputs found
Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission
By deploying machine-learning algorithms at the network edge, edge learning
can leverage the enormous real-time data generated by billions of mobile
devices to train AI models, which enable intelligent mobile applications. In
this emerging research area, one key direction is to efficiently utilize radio
resources for wireless data acquisition to minimize the latency of executing a
learning task at an edge server. Along this direction, we consider the specific
problem of retransmission decision in each communication round to ensure both
reliability and quantity of those training data for accelerating model
convergence. To solve the problem, a new retransmission protocol called
data-importance aware automatic-repeat-request (importance ARQ) is proposed.
Unlike the classic ARQ focusing merely on reliability, importance ARQ
selectively retransmits a data sample based on its uncertainty which helps
learning and can be measured using the model under training. Underpinning the
proposed protocol is a derived elegant communication-learning relation between
two corresponding metrics, i.e., signal-to-noise ratio (SNR) and data
uncertainty. This relation facilitates the design of a simple threshold based
policy for importance ARQ. The policy is first derived based on the classic
classifier model of support vector machine (SVM), where the uncertainty of a
data sample is measured by its distance to the decision boundary. The policy is
then extended to the more complex model of convolutional neural networks (CNN)
where data uncertainty is measured by entropy. Extensive experiments have been
conducted for both the SVM and CNN using real datasets with balanced and
imbalanced distributions. Experimental results demonstrate that importance ARQ
effectively copes with channel fading and noise in wireless data acquisition to
achieve faster model convergence than the conventional channel-aware ARQ.Comment: This is an updated version: 1) extension to general classifiers; 2)
consideration of imbalanced classification in the experiments. Submitted to
IEEE Journal for possible publicatio
Towards Data-centric Graph Machine Learning: Review and Outlook
Data-centric AI, with its primary focus on the collection, management, and
utilization of data to drive AI models and applications, has attracted
increasing attention in recent years. In this article, we conduct an in-depth
and comprehensive review, offering a forward-looking outlook on the current
efforts in data-centric AI pertaining to graph data-the fundamental data
structure for representing and capturing intricate dependencies among massive
and diverse real-life entities. We introduce a systematic framework,
Data-centric Graph Machine Learning (DC-GML), that encompasses all stages of
the graph data lifecycle, including graph data collection, exploration,
improvement, exploitation, and maintenance. A thorough taxonomy of each stage
is presented to answer three critical graph-centric questions: (1) how to
enhance graph data availability and quality; (2) how to learn from graph data
with limited-availability and low-quality; (3) how to build graph MLOps systems
from the graph data-centric view. Lastly, we pinpoint the future prospects of
the DC-GML domain, providing insights to navigate its advancements and
applications.Comment: 42 pages, 9 figure
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning
Do we need active learning? The rise of strong deep semi-supervised methods
raises doubt about the usability of active learning in limited labeled data
settings. This is caused by results showing that combining semi-supervised
learning (SSL) methods with a random selection for labeling can outperform
existing active learning (AL) techniques. However, these results are obtained
from experiments on well-established benchmark datasets that can overestimate
the external validity. However, the literature lacks sufficient research on the
performance of active semi-supervised learning methods in realistic data
scenarios, leaving a notable gap in our understanding. Therefore we present
three data challenges common in real-world applications: between-class
imbalance, within-class imbalance, and between-class similarity. These
challenges can hurt SSL performance due to confirmation bias. We conduct
experiments with SSL and AL on simulated data challenges and find that random
sampling does not mitigate confirmation bias and, in some cases, leads to worse
performance than supervised learning. In contrast, we demonstrate that AL can
overcome confirmation bias in SSL in these realistic settings. Our results
provide insights into the potential of combining active and semi-supervised
learning in the presence of common real-world challenges, which is a promising
direction for robust methods when learning with limited labeled data in
real-world applications.Comment: Accepted @ ECML PKDD 2023. This is the author's version of the work.
The definitive Version of Record will be published in the Proceedings of ECML
PKDD 202
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