QATS is a new polylog-time approximate decoding procedure for HMMs that builds admissible state sequences by locally maximizing likelihoods over paths with at most three segments via adaptive ternary segmentation and cumulative sum storage.
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A new ratio-based interval sectioning method for unimodal function minimization is reported to outperform bisection and golden section searches by factors of 2.26x and 1.72x in passive form across tests on twenty functions.
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Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models
QATS is a new polylog-time approximate decoding procedure for HMMs that builds admissible state sequences by locally maximizing likelihoods over paths with at most three segments via adaptive ternary segmentation and cumulative sum storage.
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In ratio section method and algorithms for minimizing unimodal functions
A new ratio-based interval sectioning method for unimodal function minimization is reported to outperform bisection and golden section searches by factors of 2.26x and 1.72x in passive form across tests on twenty functions.