Search Results for author:
Found 5 papers, 2 papers with code
A General-Purpose Transferable Predictor for Neural Architecture Search
In this paper, we propose a general-purpose neural predictor for NAS that can transfer across search spaces, by representing any given candidate Convolutional Neural Network (CNN) with a Computation Graph (CG) that consists of primitive operators.
GENNAPE: Towards Generalized Neural Architecture Performance Estimators
In this paper, we propose GENNAPE, a Generalized Neural Architecture Performance Estimator, which is pretrained on open neural architecture benchmarks, and aims to generalize to completely unseen architectures through combined innovations in network representation, contrastive pretraining, and fuzzy clustering-based predictor ensemble.
A Transferable General-Purpose Predictor for Neural Architecture Search
Understanding and modelling the performance of neural architectures is key to Neural Architecture Search (NAS).
Profiling Neural Blocks and Design Spaces for Mobile Neural Architecture Search
Neural architecture search automates neural network design and has achieved state-of-the-art results in many deep learning applications.
Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann Machines
We argue that this relates to the fact that we are training a quantum rather than classical Boltzmann distribution in this case.
