Variational Autoencoders (VAEs) belong to a family of autoencoders with probabilistic properties, making them well suited for generating data by producing a smooth and continuous latent space. Despite being introduced over a decade ago, the method continues to be widely adopted in both research and industry for diverse applications.
arXiv:2501. 09876v3 Announce Type: replace-cross Abstract: Generative modeling aims to generate new data samples that resemble a given dataset.
By Wonjun Lee, Riley C. W. O'Neill, Dongmian Zou, Jeff Calder, Gilad Lerman
arXiv:2609.37435v1 Announce Type: new
Abstract: Neural Koopman autoencoder models have been shown to successfully build a latent embedding with linear dynamics for arbitrary dynamical systems, enabli...
By Anthony Frion, Lucas Drumetz, Guillaume Tochon, Mauro Dalla Mura, Ali Can Bekar, Abdeldjalil A\"issa El Bey
arXiv:2604. 00669v2 Announce Type: replace Abstract: This study examines the challenges of modeling complex and noisy data related to socioeconomic factors over time, with a focus on data from various districts in Odisha, India.
By Sandeep Kumar Samota, Reema Gupta, Snehashish Chakraverty
The paper introduces a deep generative model using conditional variational autoencoders to augment vital sign data from healthy individuals so that it mimics patterns of specific clinical conditions. Trained on a publicly available ICU dataset, the model learns the underlying dynamics of ICU data and reshapes healthy data to align with target clinical labels. A proposed distance metric demonstrates that the generated samples are more aligned with intended clinical labels than baseline methods.
By Rafael Pina, Varuna De Silva, Mindula Illeperuma