1,450 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
Numerical Data Imputation for Multimodal Data Sets: A Probabilistic Nearest-Neighbor Kernel Density Approach
Numerical data imputation algorithms replace missing values by estimates to
leverage incomplete data sets. Current imputation methods seek to minimize the
error between the unobserved ground truth and the imputed values. But this
strategy can create artifacts leading to poor imputation in the presence of
multimodal or complex distributions. To tackle this problem, we introduce the
NNKDE algorithm: a data imputation method combining nearest neighbor
estimation (NN) and density estimation with Gaussian kernels (KDE). We
compare our method with previous data imputation methods using artificial and
real-world data with different data missing scenarios and various data missing
rates, and show that our method can cope with complex original data structure,
yields lower data imputation errors, and provides probabilistic estimates with
higher likelihood than current methods. We release the code in open-source for
the community: https://github.com/DeltaFloflo/knnxkdeComment: 30 pages, 8 figures, accepted in TMLR (Reproducibility certification
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