MHVAE: a Human-Inspired Deep Hierarchical Generative Model for Multimodal Representation Learning

4 June 2020

Papers citing "MHVAE: a Human-Inspired Deep Hierarchical Generative Model for Multimodal Representation Learning"

6 / 6 papers shown

Title
Generative AI for RF Sensing in IoT systems Li Wang Chao Zhang Qiyang Zhao Hang Zou S. Lasaulce Giuseppe Valenzise Zhuo He Mérouane Debbah 143 11 0 10 Jul 2024
Bridging Language, Vision and Action: Multimodal VAEs in Robotic Manipulation TasksIEEE/RJS International Conference on Intelligent RObots and Systems (IROS), 2024 G. Sejnova M. Vavrecka Karla Stepanova 272 2 0 02 Apr 2024
Learning Missing Modal Electronic Health Records with Unified Multi-modal Data Embedding and Modality-Aware AttentionMachine Learning in Health Care (MLHC), 2023 Kwanhyung Lee Soojeong Lee Sangchul Hahn Heejung Hyun Edward Choi Byungeun Ahn Joohyung Lee 158 25 0 04 May 2023
Benchmarking Multimodal Variational Autoencoders: CdSprites+ Dataset and Toolkit G. Sejnova M. Vavrecka Karla Stepanova VGen 148 2 0 07 Sep 2022
A unified software/hardware scalable architecture for brain-inspired computing based on self-organizing neural modelsFrontiers in Neuroscience (Front. Neurosci.), 2022 Artem R. Muliukov Laurent Rodriguez Benoit Miramond Lyes Khacef Joachim Schmidt Quentin Berthet A. Upegui 156 6 0 06 Jan 2022
Composable Generative Models Johan Leduc Nicolas Grislain SyDa 126 5 0 18 Feb 2021