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Neural network-based predictive modeling with high-dimensional structured Gaussian targets requires an efficient and numerically stable, yet expressive approximation of the covariance matrix. We propose SCORE: a scalable framework, combining scoring rule training with an expressive covariance approximation learned in s...
This work quantifies functional degeneracy through the behavioral recovery rank, defined as the number of leading behavioral-Hessian eigendirections required to recover a trained model's performance, and finds that structural and magnitude pruning retain more degrees of freedom, even after the task is saturated.
An analytical framework is developed to compare fully-connected ReLU ANNs and integrate-and-fire SNNs for time-series data with respect to their theoretical energy efficiency at matched expressive capacity, and derives an expressivity-normalized efficiency ratio and explicit thresholds in network width, spike sparsity,...
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