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Natural Humanoid Robot Locomotion with Generative Motion Prior
Natural and lifelike locomotion remains a fundamental challenge for humanoid robots to interact with human society.
Diffusion-Driven Generative Framework for Molecular Conformation Prediction
The task of deducing three-dimensional molecular configurations from their two-dimensional graph representations holds paramount importance in the fields of computational chemistry and pharmaceutical development.
Domain Adaptive Graph Classification
Moreover, our approach incorporates adaptive perturbations into the dual branches, which align the source and target distribution to address domain discrepancies.
Dynamic Spiking Framework for Graph Neural Networks
Current work often uses SNNs instead of Recurrent Neural Networks (RNNs) by using binary features instead of continuous ones for efficient training, which would overlooks graph structure information and leads to the loss of details during propagation.
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