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Implementation of GPLVM and Bayesian GPLVM in pytorch/gpytorch

GPLVM 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# GPLVM: Core Models

Implementation of Gaussian Process Latent Variable models in pytorch/gpytorch

Key References:

1) [Gaussian process latent variable models for visualisation of high dimensional data](https://papers.nips.cc/paper/2003/file/9657c1fffd38824e5ab0472e022e577e-Paper.pdf)
2) [Bayesian GPLVM](http://proceedings.mlr.press/v9/titsias10a/titsias10a.pdf)
3) [Local distance preservation in the GPLVM](https://dl.acm.org/doi/abs/10.1145/1143844.1143909?casa_token=xk93fApyEQoAAAAA%3AacOknmr9fwAp7G2neUwxlDZakjcPDQyq5bbvtYvlNxFAgDB46nFRCtcA5d3F_dPgIF3mgbc_2fI)
4) [Stochastic Variational Inference for back-constrained GPLVM](https://www.blackboxworkshop.org/pdf/gplvm_blackbox_final.pdf)
5) [Gaussian Processes for Big data](https://arxiv.org/abs/1309.6835)

Models

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1) GPLVM 
2) B-GPLVM
3) Back-constrained GPLVM
4) SVI GPLVM (with and without back-constraints)

Inference

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1) ML-II Optimisation 
2) Variational Inference with the collapsed bound
3) Stochastic Variational Inference with the uncollapsed bound

Code Layout

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data/*  has all the data loading utilities  
models/* model classes

Usage

------------

See demo.py

GPLVM

GPLVM 최신버젼 다운로드

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GPLVM 최신버젼 다운로드

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