SGD on neural network weights induces a BBP phase transition that detaches signal eigenvalues from the random bulk, yielding an analytically solvable phase diagram for trainability in a linear teacher-student model.
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2 Pith papers cite this work, alongside 32 external citations. Polarity classification is still indexing.
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Establishes correspondence between anomaly detection and functional renormalization group flow of non-equilibrium field theories, benchmarked on 2D Ising model identifying critical thresholds with <4% error.
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Spectral phase transitions and trainability in neural network learning dynamics
SGD on neural network weights induces a BBP phase transition that detaches signal eigenvalues from the random bulk, yielding an analytically solvable phase diagram for trainability in a linear teacher-student model.
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Field Theory of Data: Anomaly Detection via the Functional Renormalization Group. The 2D Ising Model as a Benchmark
Establishes correspondence between anomaly detection and functional renormalization group flow of non-equilibrium field theories, benchmarked on 2D Ising model identifying critical thresholds with <4% error.