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Training Neural Networks to Approach the Optimum Bayes Estimator in Dense Multi-Emitter Localization
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Yi Sun, Mona Sharifi, Muzna Yumman

· 1 min read

ResearcharXiv cs.LG

Training Neural Networks to Approach the Optimum Bayes Estimator in Dense Multi-Emitter Localization

arXiv:2609.20465v1 Announce Type: new Abstract: We train neural networks on synthesized frames to approach the optimum Bayes estimator for dense emitter localization. The result justifies the future work on training neural networks to achieve high-throughput large-FOV super spatiotemporal resolution SMLM.

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This story was published by arXiv cs.LG and written by Yi Sun, Mona Sharifi, Muzna Yumman. SyncAI.news shows a preview; the complete article is on the publisher's site.

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