Results 11 to 20 of about 29,428 (264)

Tensor-Network Decoding Beyond 2D

PRX Quantum
Decoding algorithms based on approximate tensor-network (TN) contraction have proven tremendously successful in decoding two-dimensional (2D) local quantum codes such as toric or surface codes and color codes, effectively achieving optimal decoding ...
Christophe Piveteau, Christopher T. Chubb, Joseph M. Renes   +2 more
doaj   +2 more sources

Proper account of auto-correlations improves decoding performances of state-space (semi) Markov models [PDF]

Peer Community Journal
State-space models are widely used in ecology to infer hidden behaviors. This study develops an extensive numerical simulation-estimation experiment to evaluate the state decoding accuracy of four simple state-space models.
Bez, Nicolas, Gloaguen, Pierre, Etienne, Marie-Pierre, Lanco, Sophie, Joo, Rocío, Rivot, Etienne, Walker, Emily, Woillez, Mathieu, Mahévas, Stéphanie   +8 more
doaj   +3 more sources

Decoding bladder state from pudendal intraneural signals in pigs [PDF]

, 2023
: Neuroprosthetic devices used for the treatment of lower urinary tract dysfunction, such as incontinence or urinary retention, apply a pre-set continuous, open-loop stimulation paradigm, which can cause voiding dysfunctions due to neural adaptation.
Bernini, F, S. Lacour, Lenzi, C., Del Popolo, G, Strauss, I., C. Lenzi, F. Vallone, Pirone, A, A. Giannotti, Musco, S., Giannessi, E., Gabisonia, K, Paggi, V., Moccia, S., Paggi, Valentina, Giannotti, A, Strauss, I, Vallone, F, F. Bernini, K. Gabisonia, S. Micera, Vallone, Fabio, L. Pollina, Lenzi, C, S. Moccia, Strauss, Ivo, Vallone, F., S. Lo Vecchio, L. Carlucci, Del Popolo, Giulio, Giannotti, A., Paggi, V, Micera, S, Miragliotta, Vincenzo, Giannessi, Elisabetta, Pollina, Leonardo, Pirone, A., Lacour, S, Pirone, Andrea, Pollina, L, V. Miragliotta, Carlucci, Lucia, Moccia, S, Bernini, Fabio, Miragliotta, V, Moccia, Sara, Musco, Stefania, Lo Vecchio, Sara, Lacour, Stéphanie, Pollina, L., G. Del Popolo, Micera, Silvestro, S. Musco, A. Pirone, Carlucci, L, E. Giannessi, Lo Vecchio, S, V. Paggi, I. Strauss, Del Popolo, G., Giannessi, E, Bernini, F., Lacour, S., Lo Vecchio, S., Gabisonia, Khatia, Giannotti, Alice, Miragliotta, V., Carlucci, L., Gabisonia, K., Micera, S., Musco, S   +70 more
core   +1 more source

Number of latent factors vs. decoding accuracy.

, 2022
The decoding accuracy as a function of the number of factors generated by GPFA used (by incremental addition from slowest to fastest factors and vice versa) is shown in (A) and (B) for Subjects 1 and 2, respectively.
Paolo Gabriel (13215400), Isaac Shamie (8828390), Vikash Gilja (6320741), David Gonda (13215406), Patricia Dugan (10577361), Daniel Friedman (3324960), Xi Jiang (194239), Werner Doyle (12041857), Orin Devinsky (13215403), Sonya Wang (13215412), Thomas Thesen (356633), Shifteh Sattar (13215409), Eric Halgren (226713), Lucia Melloni (347320), Abdulwahab Alasfour (13215397)   +14 more
core   +1 more source

Spoken term detection using fast phonetic decoding [PDF]

, 2009
While spoken term detection (STD) systems based on word indices provide good accuracy, there are several practical applications where it is infeasible or too costly to employ an LVCSR engine. An STD system is presented, which is designed to incorporate a
Roy Wallace, Wallace, Roy G., Sridha Sridharan, Sridharan, Sridha, Vogt, Robbie, Robbie Vogt   +5 more
core   +1 more source

Faster Phrase-Based Decoding by Refining Feature State [PDF]

, 2014
We contribute a faster decoding algorithm for phrase-based machine translation. Translation hypotheses keep track of state, such as context for the language model and coverage of words in the source sentence.
Kayser, Michael, Michael Kayser, Kenneth Heafield, Heafield, Kenneth, Manning, Christopher D., Christopher D. Manning   +5 more
core   +1 more source

Deep Learning for Real-Time Neural Decoding of Grasp [PDF]

, 2023
Neural decoding involves correlating signals acquired from the brain to variables in the physical world like limb movement or robot control in Brain Machine Interfaces.
Bergamasco, Letizia, Scionti, Alberto, Valentina Agostini, Olivier Terzo, Elios Ghinato, Terzo, Olivier, Andres Agudelo-Toro, Alberto Scionti, Chiara Vercellino, Hansjörg Scherberger, Vitali, Giacomo, Marco Ghislieri, Gesmundo, Ilaria, Ghislieri, Marco, Vercellino, Chiara, Giacomo Vitali, Letizia Bergamasco, Ghinato, Elios, Scherberger, Hansjörg, Paolo Viviani, Agudelo-Toro, Andres, Ilaria Gesmundo, Viviani, Paolo, Agostini, Valentina   +23 more
core   +1 more source

Markov chain Monte Carlo methods for state-space models with point process observations [PDF]

, 2012
This letter considers how a number of modern Markov chain Monte Carlo (MCMC) methods can be applied for parameter estimation and inference in state-space models with point process observations.
Niranjan, Mahesan, Girolami, Mark, Yuan, Ke   +2 more
core   +1 more source

REAL-TIME ONE-PASS DECODING WITH RECURRENT NEURAL NETWORK LANGUAGE MODEL FOR SPEECH RECOGNITION [PDF]

, 2014
This paper proposes an efficient one-pass decoding method for realtime speech recognition employing a recurrent neural network language model (RNNLM).
Yotaro Kubo, Takaaki Hori, Atsushi Nakamura   +2 more
core   +1 more source

TinyML for EEG Decoding on Microcontrollers

, 2023
The accurate decoding of ElectroEncephaloGraphy (EEG) signals would bring us closer to understanding brain functionality, opening new pathways to fixing brain impairments and devising new Brain-Computer Interface (BCI)-related applications.
Massoud, Yehia Mahmoud, Tragoudaras, Antonios, Antoniadis, Charalampos   +2 more
core   +1 more source