Patent ID: 11893498
Assignee: INSILICO MEDICINE IP LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 12:
13. The computer system of claim 1, wherein the operations comprise training a variational autoencoder with a learnable prior to obtain the trained conditional generative mode, the training comprising:
providing a computing system with deep neural network having a conditional generative model configured as a variational autoencoder with a learnable prior, wherein the learnable prior is a complex distribution parameterized by a tensor train decomposition, wherein the conditional generative model is configured for solving a subset conditioning problem by estimating a joint distribution of latent codes and conditions for a respective object in a tensor train format;
providing a dataset to the conditional generative model, the dataset having object data for a plurality of objects and property data for a property of a first portion of the plurality of objects and without the property data for a second potion of the plurality of objects, wherein the property is a defined object property;
processing the object data of the dataset with an object encoder of the conditional generative model to obtain latent object data from the object data mapped onto the prior distribution, wherein the latent object data includes a distribution of latent variables having a mean and a variance, wherein the latent object data is based at least partially on the property data;
sampling one or more latent variables from the obtained distribution of latent variables with the conditional generative model, wherein the sampled one or more latent variables are based at least partially on the property data;
processing the sampled one or more latent variables with the tensor train decomposition of the conditional generative model, wherein the processing is performed with the defined object property in order to compute a probability of the samples having the defined object property;
processing the sampled one or more latent variables through an object decoder of the conditional generative model to obtain a reconstructed object;
determining a reconstruction loss of the reconstructed object from an original object from the object data with the conditional generative model;
computing a Kullback-Leibler divergence of the distribution of the latent variables from a distribution of the tensor train decomposition with the conditional generative model;
using the determined reconstruction loss and computed Kullback-Leibler divergence to compute a loss with the conditional generative model from the data set;
performing a gradient descent with the conditional generative model until the reconstructed object is sufficiently representative of the original object and has the defined object property;
obtaining a trained model configured as a trained variational autoencoder with the learnable prior, wherein the learnable prior parameterized by the tensor train decomposition; and
providing the trained conditional generative model.