arXiv Machine Learning
Biologically Inspired Mechanisms for Facilitating Grokking in Multilayer Perceptrons
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arXiv Machine Learning
Jul 14
How to Tame Grokking: Representation Geometry as a Control Signal
arXiv:2607. 11666v1 Announce Type: new Abstract: Grokking is a phenomenon in which neural networks initially memorize training data and only later exhibit strong generalization after prolonged optimization.
arXiv AI
Jun 2
Rare Events, Real Signals: Functional Ensembles as Units of Computation in Deep Spiking Networks
arXiv:2606. 00073v1 Announce Type: cross Abstract: We investigate how internal representations emerge across hierarchical processing systems by introducing a neuroscience-inspired framework for analyzing deep spiking neural networks (SNN) through the lens of functional connectivity.
arXiv Machine Learning
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Exploring napping paradigm for Recurrent Spiking Neural Networks
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arXiv AI
Jun 16
Harnessing cortical geometry, wiring, and function as inductive biases for recurrent neural networks
arXiv:2606. 14975v1 Announce Type: cross Abstract: How the wiring and functional organization of cortex shape recurrent computation remains a central question in both neuroscience and machine learning.
arXiv Machine Learning
Jun 30
SGD Provably Prioritizes a Shortcut Spurious Feature in the XOR Model
arXiv:2606. 30444v1 Announce Type: cross Abstract: Neural networks are known to be susceptible to over-reliance on spurious correlations.
arXiv Machine Learning
Jun 3
Quadratic integrate-and-fire neurons exhibit less fragmented loss landscapes and outperform leaky integrate-and-fire neurons in spike-based gradient descent
arXiv:2606. 03935v1 Announce Type: cross Abstract: The ability to train spiking neural networks is essential for modeling biological neural networks as well as for neuromorphic computing.