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Found 4 papers, 3 papers with code
Beyond neural scaling laws: beating power law scaling via data pruning
Widely observed neural scaling laws, in which error falls off as a power of the training set size, model size, or both, have driven substantial performance improvements in deep learning.
A theory of learning with constrained weight-distribution
Our theory predicts that the reduction in capacity due to the constrained weight-distribution is related to the Wasserstein distance between the imposed distribution and that of the standard normal distribution.
Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks
Medial entorhinal cortex (MEC) supports a wide range of navigational and memory related behaviors. Well-known experimental results have revealed specialized cell types in MEC --- e. g. grid, border, and head-direction cells --- whose highly stereotypical response profiles are suggestive of the role they might play in supporting MEC functionality.
A unified theory for the origin of grid cells through the lens of pattern formation
This theory provides insight into the optimal solutions of diverse formulations of the normative task, and shows that symmetries in the representation of space correctly predict the structure of learned firing fields in trained neural networks.
