Error bounds for convolutional codes and an asymptotically optimum decoding algorithm

By A. Viterbi. 1967

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Citations in the corpus

Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates (2018, methodology): “The most probable segmentation x ∗ for the input sentence X is then given by where S ( X ) is a set of segmentation candidates built from the input sentence X . x ∗ is obtained with the Viterbi algorithm (Viterbi, 1967).”
Frank-Wolfe Algorithms for Saddle Point Problems (2016, methodology): “We encoded the structure Y i of the i th word with a Markov model: its In this case, the oracle function is simply the Viterbi algorithm Viterbi (1967).”
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs (2016, methodology): “For OCR and CoNLL, the max oracle consists of the Viterbi algorithm (Viterbi, 1967); for HorseSeg – in graph cut (Boykov & Kolmogorov, 2004), for LSP – in belief propagation on a tree with messages passed by a generalized distance transform (Felzenszwalb & Huttenlocher, 2005).”
Deep Visual-Semantic Alignments for Generating Image Descriptions (2014, neutral): “We minimize the energy to find the best alignments a using dynamic programming [54].”
Neural Speech Synthesis with Transformer Network (2018, methodology): “Concatenative TTS systems (Hunt and Black 1996; Black and Taylor 1997) split original waves into small units, and stitch them by some algorithms such as Viterbi (Viterbi 1967) followed by signal process methods (Charpentier and Stella 1986; Verhelst and Roelands 1993) to generate new waves.”
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series (2020, methodology): “To estimate this ability, we ran the well-know Viterbi algorithm (Viterbi, 1967)”
Toward understanding natural language directions (2010, methodology): “We perform global inference using a Viterbi-style algorithm [19] that finds the most probable sequence of viewpoints corresponding to a given sequence of SDCs.”
The ECO data set and the performance of non-intrusive load monitoring algorithms (2014, methodology): “[27] relies on hidden Markov models (HMMs) and the Viterbi algorithm [30] to disaggregate the electricity consumption of a household.”
Learning a Latent Space of Multitrack Measures (2018, methodology): “For each segment, we infer chords at a frequency of 2 per measure using the Viterbi algorithm [39] over a heuristically-deﬁned probability distribution; as a byproduct we also infer the time-varying key of the sequence.”
Named Entity Recognition through Classifier Combination (2003, methodology): “The search methods employed by each classifier are different: the HMM, MaxEnt and RRM classifiers construct a model for each example and then rely on a sequence search such as the Viterbi algorithm (Viterbi, 1967) to identify the best overall sequence, while fnTBL starts with most frequent classific”
Polar Codes for Channel and Source Coding (2009, methodology): “We claim that in this case the message subset pair Gv = [9, 10], Gc = [16, 41] is good with strength β = 3 4 .”
SMURFLite: combining simplified Markov random fields with simulated evolution improves remote homology detection for beta-structural proteins into the twilight zone (2012, methodology): “This dynamic programming resembles the classic Viterbi algorithm (Viterbi, 1967) used on HMMER’s ‘plan7’ (Eddy, 1998) HMMs, except that some states are betastrand states, which are required to be match states, and which are paired with other beta-strand nodes in the model.”
PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. (2007, methodology): “1970) for training the model to maximize the likelihood of the observed data for each individual, and then used the Viterbi algorithm (Viterbi 1967) to infer the most likely path (state sequences for all SNPs along each chromosome).”
Constructing free-energy approximations and generalized belief propagation algorithms (2005, methodology): “They are well known by names like the forward–backward algorithm for hidden Markov models [3], the Viterbi algorithm [4], [5], Gallager’s sum–product algorithm for decoding lowdensity parity check codes [6], the “turbo-decoding” algorithm [7], [8], Pearl’s “belief propagation” algorithm for inferenc”