GitHub

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.
IEEE

Autoencoder-Augmented Graph Neural Networks for Accurate and Scalable Structure Recognition in Analog/Mixed-Signal Schematics

Abstract: The increasing complexity of Analog/Mixed-Signal (AMS) schematics has been posing significant challenges in structure recognition, particularly in the intellectual property (IP) industry, ...
GitHub

AVF-MAE++ : Scaling Affective Video Facial Masked Autoencoders via Efficient Audio-Visual Self-Supervised Learning

Abstract: Affective Video Facial Analysis (AVFA) is important for advancing emotion-aware AI, yet the persistent data scarcity in AVFA presents challenges. Recently, the self-supervised learning (SSL) ...
IEEE

Variational Graph Convolutional Networks for Dynamic Graph Representation Learning

Abstract: The ubiquitous and ever-evolving nature of cyber threats demands innovative approaches that can adapt to the dynamic relationships and structures within network data. Traditional models ...