Abstract: We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (nonnegativity-constrained autoencoder), that learns features that show part-based ...
For three decades, functional neuroimaging (fMRI) has been shaping the understanding of the human brain. A major obstacle for ...
Current state-of-the-art object-centric models use slots and attention-based routing for binding. However, this class of models has several conceptual limitations: the number of slots is hardwired; ...
Abstract: This paper introduces V2Coder, a non-autoregressive vocoder based on hierarchical variational autoencoders (VAEs). The hierarchical VAE with hierarchically extended prior and approximate ...
Fluid–structure interaction (FSI) governs how flowing water and air interact with marine structures—from wind turbines to ...
MAESTRO: Masked Autoencoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data
We introduce MAESTRO, a tailored adaptation of the Masked Autoencoder (MAE) framework that effectively orchestrates the use of multimodal, multitemporal, and multispectral Earth Observation (EO) data.
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