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High-Performance Tensor Formulation of the Viterbi Algorithm for Hidden Semi-Markov Models
Hidden Semi-Markov Models (HSMMs) are fundamental probabilistic models widely adopted across diverse domains, from computational biology to finance and signal processing. The Viterbi algorithm decodes the most likely state sequence given an HSMM and can be applied iteratively for ab initio model learning. However, existing Viterbi implementations remain sequential, and GPU-accelerated solutions are entirely absent, making HSMM decoding impractical for large-scale workloads. We present a tensor-based formulation of the Viterbi algorithm for HSMMs, restructuring the inner loops into tensor opera
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T01:45:34.000Z
First collected: 2026-09-20T09:01:24.920Z. This is not the publication date.