This equivalence is particularly useful in situations where a function we term the expected likelihood is efficiently computable. In such situations, we give a ...
This equivalence is particularly useful in situations where a function we term the expected likelihood is efficiently computable. In such situations, we give a ...
In this work, we propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation. The resulting optimisation problem can ...
Equivalence of ML decoding to a continuous optimization problem. Srinivasavaradhan, S., Fragouli, C., & Diggavi, S. N. IEEE Symposium on Information Theory, ...
We introduce a novel decoding framework (§ 3) that relaxes this discrete optimisation problem into a continuous optimisation problem. This is akin to linear ...
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C.3.5: Equivalence of ML decoding to a continuous optimization problem · Click here to download the manuscript · Click here to view the Virtual Presentation.
Sep 6, 2024 · Dive into the mathematical principles that strengthen ML and DL, focusing on symmetric, reflexive, transitive, and equivalence relations.
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Aug 22, 2024 · We present a method, called matching synthesis, for decoding quantum codes that produces an enhanced assignment of errors from an ensemble of decoders.
Maximum-likelihood decoding is the optimum decoding that minimizes the probability of a decoding error, if the messages are all equally likely.
The maximum likelihood decoding problem for linear binary (n,k) codes is reformulated as a continuous optimization problem in a k -dimensional solid cube.