1,691 research outputs found
Content-Aware User Clustering and Caching in Wireless Small Cell Networks
In this paper, the problem of content-aware user clustering and content
caching in wireless small cell networks is studied. In particular, a service
delay minimization problem is formulated, aiming at optimally caching contents
at the small cell base stations (SCBSs). To solve the optimization problem, we
decouple it into two interrelated subproblems. First, a clustering algorithm is
proposed grouping users with similar content popularity to associate similar
users to the same SCBS, when possible. Second, a reinforcement learning
algorithm is proposed to enable each SCBS to learn the popularity distribution
of contents requested by its group of users and optimize its caching strategy
accordingly. Simulation results show that by correlating the different
popularity patterns of different users, the proposed scheme is able to minimize
the service delay by 42% and 27%, while achieving a higher offloading gain of
up to 280% and 90%, respectively, compared to random caching and unclustered
learning schemes.Comment: In the IEEE 11th International Symposium on Wireless Communication
Systems (ISWCS) 201
Towards Explainable and Language-Agnostic LLMs: Symbolic Reverse Engineering of Language at Scale
Large language models (LLMs) have achieved a milestone that undenia-bly
changed many held beliefs in artificial intelligence (AI). However, there
remains many limitations of these LLMs when it comes to true language
understanding, limitations that are a byproduct of the under-lying architecture
of deep neural networks. Moreover, and due to their subsymbolic nature,
whatever knowledge these models acquire about how language works will always be
buried in billions of microfeatures (weights), none of which is meaningful on
its own, making such models hopelessly unexplainable. To address these
limitations, we suggest com-bining the strength of symbolic representations
with what we believe to be the key to the success of LLMs, namely a successful
bottom-up re-verse engineering of language at scale. As such we argue for a
bottom-up reverse engineering of language in a symbolic setting. Hints on what
this project amounts to have been suggested by several authors, and we discuss
in some detail here how this project could be accomplished.Comment: Draft, preprin
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