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Due to the high arithmetic complexity and scalability challenges of deep learning, there is a critical need to shift research focus towards energy efficiency. Tsetlin Machines (TMs) are a recent approach to machine learning (ML) that has demonstrated significantly reduced energy compared to neural networks alike, while providing comparable accuracy on several benchmarks. However, TMs rely heavily...
Learning Automata (LA) can be reckoned to be the founding algorithms on which the field of Reinforcement Learning has been built. Among the families of LA, Estimator Algorithms (EAs) are certainly the fastest, and of these, the family of discretized algorithms are proven to converge even faster than their continuous counterparts. However, it has recently been reported that the previous proofs for...
We consider the problem of a user operating within a Cognitive Radio Network (CRN) which involves N channels each associated with a Primary User (PU). The problem consists of allocating a channel which, at any given time instant is not being used by a PU, to a Secondary User (SU). Within our study, we assume that a SU is allowed to perform “channel switching”, i.e., to choose an alternate channel...
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