35 citations · 95 across the 5 of their papers we have counts for
5 papers
When Does Re-initialization Work?
Sheheryar Zaidi, Tudor Berariu, Hyunjik Kim +4
Re-initializing a neural network during training has been observed to improve generalization in recent works. Yet it is neither widely adopted in deep learning practice nor is it o…
Pre-training via Denoising for Molecular Property Prediction
Sheheryar Zaidi, Michael Schaarschmidt, James Martens +6
Many important problems involving molecular property prediction from 3D structures have limited data, posing a generalization challenge for neural networks. In this paper, we descr…
Provably Strict Generalisation Benefit for Equivariant Models
Bryn Elesedy, Sheheryar Zaidi
It is widely believed that engineering a model to be invariant/equivariant improves generalisation. Despite the growing popularity of this approach, a precise characterisation of t…
LieTransformer: Equivariant self-attention for Lie Groups
Michael Hutchinson, Charline Le Lan, Sheheryar Zaidi +3
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 th…
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift
Sheheryar Zaidi, Arber Zela, Thomas Elsken +3
Ensembles of neural networks achieve superior performance compared to stand-alone networks in terms of accuracy, uncertainty calibration and robustness to dataset shift. \emph{Deep…