Results 51 to 60 of about 63,948 (252)
MathematicsRegime switching models have been widely studied for their ability to capture the dynamic behavior of time series data and are widely used in economic and financial data analysis.
Zhenni Tan, Yuehua Wu
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Log-Viterbi algorithm applied on second-order hidden Markov model for human activity recognition
International Journal of Distributed Sensor Networks, 2018 Recognition of human activities is getting into the limelight among researchers in the field of pervasive computing, ambient intelligence, robotic, and monitoring such as assistive living, elderly care, and health care.
Yang Sung-Hyun +3 more
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Automatic Harmonization Using a Hidden Semi-Markov Model
Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2013 Hidden Markov Models have been used frequently in the audio domain to identify underlying musical structure. Much less work has been done in the purely symbolic realm. Recently, a substantial amount of expert-labelled symbolic musical data has been injected into the research community.
Ryan Groves
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Structure and Randomness of Continuous-Time Discrete-Event Processes
, 2017 Loosely speaking, the Shannon entropy rate is used to gauge a stochastic process' intrinsic randomness; the statistical complexity gives the cost of predicting the process.
Crutchfield, J. P., Marzen, S. E.
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Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM [PDF]
, 2013 We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known.
Almeida +39 more
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S3RL: Enhancing Spatial Single‐Cell Transcriptomics With Separable Representation Learning
Advanced Science, EarlyView.Separable Spatial Representation Learning (S3RL) is introduced to enhance the reconstruction of spatial transcriptomic landscapes by disentangling spatial structure and gene expression semantics. By integrating multimodal inputs with graph‐based representation learning and hyperspherical prototype modeling, S3RL enables high‐fidelity spatial domain ...
Laiyi Fu +6 more
wiley +1 more source
Peer Community JournalState-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 +8 more
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Infinite Structured Hidden Semi-Markov Models
, 2014 23 pages, 10 ...
Huggins, Jonathan H., Wood, Frank
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Advanced Intelligent Systems, EarlyView.This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi +3 more
wiley +1 more source
Structured Inference for Recurrent Hidden Semi-markov Model [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Segmentation and labeling for high dimensional time series is an important yet challenging task in a number of applications, such as behavior understanding and medical diagnosis. Recent advances to model the nonlinear dynamics in such time series data, has suggested to involve recurrent neural networks into Hidden Markov Models.
Hao Liu +5 more
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