ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2207.12696
  4. Cited By
Advanced Conditional Variational Autoencoders (A-CVAE): Towards
  interpreting open-domain conversation generation via disentangling latent
  feature representation

Advanced Conditional Variational Autoencoders (A-CVAE): Towards interpreting open-domain conversation generation via disentangling latent feature representation

26 July 2022
Ye Wang
Jing Liao
Hong-ye Yu
Guoyin Wang
Xiaoxia Zhang
Li Liu
    DRL
ArXivPDFHTML

Papers citing "Advanced Conditional Variational Autoencoders (A-CVAE): Towards interpreting open-domain conversation generation via disentangling latent feature representation"

2 / 2 papers shown
Title
Disentangling Genotype and Environment Specific Latent Features for
  Improved Trait Prediction using a Compositional Autoencoder
Disentangling Genotype and Environment Specific Latent Features for Improved Trait Prediction using a Compositional Autoencoder
Anirudha Powadi
Talukder Zaki Jubery
Michael C. Tross
James C. Schnable
Baskar Ganapathysubramanian
CML
CoGe
28
0
0
25 Oct 2024
An Empirical Bayes Framework for Open-Domain Dialogue Generation
An Empirical Bayes Framework for Open-Domain Dialogue Generation
Jing Yang Lee
Kong Aik Lee
Woon-Seng Gan
BDL
19
0
0
18 Nov 2023
1