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CAT-GS

model1 events
papersSEP 11 04:00 UTC

CAT-GS Framework Targets Instability in Multimodal Neural Network Training

A new arXiv paper introduces CAT-GS, a training approach that combines calibrated gating with a "fusion surgery" technique for multimodal neural networks. The authors identify three linked failure modes in end-to-end multimodal training, including one modality dominating optimization and unstable dynamics. The method aims to balance learning across modalities and stabilize training.

multimodal/training/arxiv/neural-networks