3 papers
cs.LG2026
Entropic Confinement and Mode Connectivity in Overparameterized Neural Networks
Luca Di Carlo, Chase Goddard, David J. Schwab
Modern neural networks exhibit a striking property: basins of attraction in the loss landscape are often connected by low-loss paths, yet optimization dynamics generally remain con…
cs.LG2025
When can in-context learning generalize out of task distribution?
Chase Goddard, Lindsay M. Smith, Vudtiwat Ngampruetikorn +1
In-context learning (ICL) is a remarkable capability of pretrained transformers that allows models to generalize to unseen tasks after seeing only a few examples. We investigate em…
q-bio.QM2025
Optimization and variability can coexist
Marianne Bauer, William Bialek, Chase Goddard +6
Many biological systems perform close to their physical limits, but promoting this optimality to a general principle seems to require implausibly fine tuning of parameters. Using e…