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Generalized Linear Mode Connectivity for Transformers

arXiv — stat.ML•Friday, November 14, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The exploration of linear mode connectivity (LMC) in neural networks, particularly in Transformers, is crucial for understanding optimization and generalization in deep learning. The recent article on LMC introduces a unified framework that captures various symmetry classes, which is essential for analyzing the deeper structures in loss landscapes. This is complemented by related research on quantization techniques for Vision Transformers, which aim to optimize model performance while reducing computational demands. Additionally, a unified geometric field theory framework for Transformers further emphasizes the importance of these architectures in various applications, showcasing the interconnectedness of these advancements in AI.
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