27 citations · 33 across the 12 of their papers we have counts for
4 papers · 1 filter
ONNX-Net: Towards Universal Representations and Instant Performance Prediction for Neural Architectures
Shiwen Qin, Alexander Auras, Shay B. Cohen +4
Neural architecture search (NAS) automates the design process of high-performing architectures, but remains bottlenecked by expensive performance evaluation. Most existing studies…
Transferrable Surrogates in Expressive Neural Architecture Search Spaces
Shiwen Qin, Gabriela Kadlecová, Martin Pilát +5
Neural architecture search (NAS) faces a challenge in balancing the exploration of expressive, broad search spaces that enable architectural innovation with the need for efficient…
einspace: Searching for Neural Architectures from Fundamental Operations
Linus Ericsson, Miguel Espinosa, Chenhongyi Yang +5
Neural architecture search (NAS) finds high performing networks for a given task. Yet the results of NAS are fairly prosaic; they did not e.g. create a shift from convolutional str…
Hyperparameter Selection in Continual Learning
Thomas L. Lee, Sigrid Passano Hellan, Linus Ericsson +2
In continual learning (CL) -- where a learner trains on a stream of data -- standard hyperparameter optimisation (HPO) cannot be applied, as a learner does not have access to all o…