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Found 5 papers, 3 papers with code
When Does Re-initialization Work?
However, when deployed alongside other carefully tuned regularization techniques, re-initialization methods offer little to no added benefit for generalization, although optimal generalization performance becomes less sensitive to the choice of learning rate and weight decay hyperparameters.
Pre-training via Denoising for Molecular Property Prediction
Many important problems involving molecular property prediction from 3D structures have limited data, posing a generalization challenge for neural networks.
Provably Strict Generalisation Benefit for Equivariant Models
It is widely believed that engineering a model to be invariant/equivariant improves generalisation.
LieTransformer: Equivariant self-attention for Lie Groups
Group equivariant neural networks are used as building blocks of group invariant neural networks, which have been shown to improve generalisation performance and data efficiency through principled parameter sharing.
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift
On a variety of classification tasks and modern architecture search spaces, we show that the resulting ensembles outperform deep ensembles not only in terms of accuracy but also uncertainty calibration and robustness to dataset shift.
