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An exactly solvable model for emergence and scaling laws
Deep learning models can exhibit what appears to be a sudden ability to solve a new problem as training time ($T$), training data ($D$), or model size ($N$) increases, a phenomenon known as emergence.
Probing optimisation in physics-informed neural networks
A novel comparison is presented of the effect of optimiser choice on the accuracy of physics-informed neural networks (PINNs).
Generalizing similarity in noisy setups: the DIBS phenomenon
This work uncovers an interplay among data density, noise, and the generalization ability in similarity learning.
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