4 papers
Optimizer-Induced Mode Connectivity: From AdamW to Muon
Fangzhao Zhang, Sungyoon Kim, Erica Zhang +2
Mode connectivity has been widely studied, yet the role of the optimizer remains underexplored. We revisit it through optimizer-induced implicit regularization, asking how connecti…
A ghost mechanism: An analytical model of abrupt learning in recurrent networks
Fatih Dinc, Ege Cirakman, Bariscan Kurtkaya +4
Abrupt learning is a common phenomenon in recurrent neural networks (RNNs) trained on working memory tasks. In such cases, the networks develop transient slow regions in state spac…
Latent computing by biological neural networks: A dynamical systems framework
Fatih Dinc, Marta Blanco-Pozo, David Klindt +8
Although individual neurons and neural populations exhibit the phenomenon of representational drift, perceptual and behavioral outputs of many neural circuits can remain stable acr…
Understanding and controlling the geometry of memory organization in RNNs
Udith Haputhanthri, Liam Storan, Yiqi Jiang +7
Training recurrent neural networks (RNNs) is a high-dimensional process that requires updating numerous parameters. Therefore, it is often difficult to pinpoint the underlying lear…