WebOur HCVAE model leverages user and item side information as part of the optimisation objective to help the model construct more expressive latent representations and to better capture attributes of the users and items (such as demographic, category preferences) within the personalised item probability distributions.

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http://www.42maru.ai/?trk=public_post-text WebInfo-HCVAE/vae/models.py Go to file Cannot retrieve contributors at this time 794 lines (614 sloc) 31.3 KB Raw Blame import numpy as np import torch import torch. nn as nn import … fiber wire in headphones

Generating Diverse and Consistent QA pairs from Contexts with ...

WebInfo-HCVAE (Lee et al.,2024) achieved good per- formance by designing the relationship between document, query, and answer as a probability dis- tribution and learning the latent vectors based on an auto-encoder. Answers and documents are used as inputs when creating queries. Web11 jun. 2024 · The proposed hierarchical conditional variational autoencoder (HCVAE) is an extension of VAE. In VAE, we have no control on the latent space representation. Hence, we propose HCVAE which models latent variable based on available information as input conditions. Fig. 2 represents that condition Ci and Cij are included with input vector x. http://eprints.gla.ac.uk/219367/ fiberwire ligament reconstruction