In the high-dimensional limit the spherical Boltzmann machine admits exact equations for training dynamics, Bayesian evidence, and cascades of phase transitions tied to mode alignment with data, which connect to generative phenomena including double descent and out-of-equilibrium biases.
Dynamical decoupling of generalization and overfitting in large two-layer networks
5 Pith papers cite this work. Polarity classification is still indexing.
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Higher-variance classes are learned first in diffusion models; strong class imbalance reverses the order and imposes distinct delayed learning times on minority classes.
DYNAMITE is a high-performance solver for dynamical mean-field equations that reaches times up to 10^7 with linear runtime and sublinear memory scaling.
A two-level DMFT tracks bulk and outlier spectral dynamics in wide networks, predicting width-consistent outlier growth and hyperparameter transfer under muP scaling for deep linear nets while noting bulk restructuring for large-output tasks.
In overparameterized quadratic networks, one-pass SGD escapes generalization plateaus only modestly faster and selects the initialization-closest zero-loss solution due to a conserved quantity in the overlap ODEs.
citing papers explorer
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Spherical Boltzmann machines: a solvable theory of learning and generation in energy-based models
In the high-dimensional limit the spherical Boltzmann machine admits exact equations for training dynamics, Bayesian evidence, and cascades of phase transitions tied to mode alignment with data, which connect to generative phenomena including double descent and out-of-equilibrium biases.
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The Interplay of Data Structure and Imbalance in the Learning Dynamics of Diffusion Models
Higher-variance classes are learned first in diffusion models; strong class imbalance reverses the order and imposes distinct delayed learning times on minority classes.
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DYNAMITE: A high-performance framework for solving Dynamical Mean-Field Equations
DYNAMITE is a high-performance solver for dynamical mean-field equations that reaches times up to 10^7 with linear runtime and sublinear memory scaling.
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Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer
A two-level DMFT tracks bulk and outlier spectral dynamics in wide networks, predicting width-consistent outlier growth and hyperparameter transfer under muP scaling for deep linear nets while noting bulk restructuring for large-output tasks.
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Escape dynamics and implicit bias of one-pass SGD in overparameterized quadratic networks
In overparameterized quadratic networks, one-pass SGD escapes generalization plateaus only modestly faster and selects the initialization-closest zero-loss solution due to a conserved quantity in the overlap ODEs.