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cornellius-gp / gpytorch

A highly efficient implementation of Gaussian Processes in PyTorch
MIT License
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[Docs] GPLVM with SVI for missing data dimensions in output #2305

Open upasana27 opened 1 year ago

upasana27 commented 1 year ago

📚 Documentation/Examples

@vr308 I was trying to implement this paper https://arxiv.org/pdf/2202.12979v1.pdf using the example provided (Gaussian_Process_Latent_Variable_Models_with_Stochastic_Variational_Inference). As mentioned in the paper, the algorithm can handle missing data as well because the ELBO factorizes across data dimensions as well. However, there is no example towards this. I was wondering if you could give some idea how to integrate missing data dimensions in the example provided. https://docs.gpytorch.ai/en/v1.6.0/examples/045_GPLVM/Gaussian_Process_Latent_Variable_Models_with_Stochastic_Variational_Inference.html

rajveer43 commented 12 months ago

I would like to work on this!