Full Bayesian Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery

2018 ◽  
Author(s):  
Thomas Glarner ◽  
Patrick Hanebrink ◽  
Janek Ebbers ◽  
Reinhold Haeb-Umbach
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Variational Autoencoder

scholarly journals RaptGen: A variational autoencoder with profile hidden Markov model for generative aptamer discovery

2021 ◽  
Author(s):  
Natsuki Iwano ◽  
Tatsuo Adachi ◽  
Kazuteru Aoki ◽  
Yoshikazu Nakamura ◽  
Michiaki Hamada
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
High Throughput Sequencing ◽  
Sequence Data ◽  
Hidden Markov ◽  
Bayesian Optimization ◽  
Sequencing Data ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Profile Hidden Markov Model

AbstractNucleic acid aptamers are generated by an in vitro molecular evolution method known as systematic evolution of ligands by exponential enrichment (SELEX). A variety of candidates is limited by actual sequencing data from an experiment. Here, we developed RaptGen, which is a variational autoencoder for in silico aptamer generation. RaptGen exploits a profile hidden Markov model decoder to represent motif sequences effectively. We showed that RaptGen embedded simulation sequence data into low-dimension latent space dependent on motif information. We also performed sequence embedding using two independent SELEX datasets. RaptGen successfully generated aptamers from the latent space even though they were not included in high-throughput sequencing. RaptGen could also generate a truncated aptamer with a short learning model. We demonstrated that RaptGen could be applied to activity-guided aptamer generation according to Bayesian optimization. We concluded that a generative method by RaptGen and latent representation are useful for aptamer discovery. Codes are available at https://github.com/hmdlab/raptgen.

Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery

2017 ◽  
Author(s):  
Janek Ebbers ◽  
Jahn Heymann ◽  
Lukas Drude ◽  
Thomas Glarner ◽  
Reinhold Haeb-Umbach ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Variational Autoencoder

scholarly journals A Transformer-Based Hierarchical Variational Autoencoder Combined Hidden Markov Model for Long Text Generation

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1277
Author(s):  
Kun Zhao ◽  
Hongwei Ding ◽  
Kai Ye ◽  
Xiaohui Cui
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Latent Variables ◽  
Latent Variable ◽  
Hidden Markov ◽  
Variable Structure ◽  
Text Generation ◽  
Short Text ◽  
Variational Autoencoder ◽  
Generation Network

The Variational AutoEncoder (VAE) has made significant progress in text generation, but it focused on short text (always a sentence). Long texts consist of multiple sentences. There is a particular relationship between each sentence, especially between the latent variables that control the generation of the sentences. The relationships between these latent variables help in generating continuous and logically connected long texts. There exist very few studies on the relationships between these latent variables. We proposed a method for combining the Transformer-Based Hierarchical Variational Autoencoder and Hidden Markov Model (HT-HVAE) to learn multiple hierarchical latent variables and their relationships. This application improves long text generation. We use a hierarchical Transformer encoder to encode the long texts in order to obtain better hierarchical information of the long text. HT-HVAE’s generation network uses HMM to learn the relationship between latent variables. We also proposed a method for calculating the perplexity for the multiple hierarchical latent variable structure. The experimental results show that our model is more effective in the dataset with strong logic, alleviates the notorious posterior collapse problem, and generates more continuous and logically connected long text.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

Engaging Voluntary Contributions in Online Communities: A Hidden Markov Model

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Online Communities ◽  
Hidden Markov ◽  
Voluntary Contributions
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