2 papers
cs.LG2026
Omni-modal decomposition autoencoders learn full-stack wearable disentangled representations
Ioannis Ziogas, Ensieh Khazaei, Bilal Taha +4
Learning disentangled representations is a key requirement for developing versatile, general-purpose, and sustainable models in multi-modal wearable computing. However, existing ap…
cs.LG2026
Variational decomposition autoencoding improves disentanglement of latent representations
Ioannis Ziogas, Aamna Al Shehhi, Ahsan H. Khandoker +1
Understanding the structure of complex, nonstationary, high-dimensional time-evolving signals is a central challenge in scientific data analysis. In many domains, such as speech an…