Derives Schwinger-Dyson equations and Ward identities in NN-FT to study anomalies in QFTs via a conserved parameter-space current, yielding a new perspective on symmetries.
Beyond the Permutation Symmetry of Transformers: The Role of Rotation for Model Fusion
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A bidirectional optimization method using parameterized transformations enables near-zero loss barriers for linear mode connectivity in medium-scale language models and small barriers in billion-parameter transformers.
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Anomalies in Neural Network Field Theory
Derives Schwinger-Dyson equations and Ward identities in NN-FT to study anomalies in QFTs via a conserved parameter-space current, yielding a new perspective on symmetries.
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Scaling Linear Mode Connectivity and Merging to Billion Parameter Pretrained Transformers
A bidirectional optimization method using parameterized transformations enables near-zero loss barriers for linear mode connectivity in medium-scale language models and small barriers in billion-parameter transformers.