Multi-conditioned Graph Diffusion for Neural Architecture Search
Asthana R, Conrad J, Dawoud Y, Ortmanns M, Belagiannis V (2024)
Publication Language: English
Publication Type: Journal article, Online publication
Publication year: 2024
Journal
URI: https://openreview.net/forum?id=5VotySkajV
Open Access Link: https://openreview.net/forum?id=5VotySkajV
Abstract
Neural architecture search automates the design of neural network architectures usually by exploring a large and thus complex architecture search space. To advance the architecture search, we present a graph diffusion-based NAS approach that uses discrete conditional graph diffusion processes to generate high-performing neural network architectures. We then propose a multi-conditioned classifier-free guidance approach applied to graph diffusion networks to jointly impose constraints such as high accuracy and low hardware latency. Unlike the related work, our method is completely differentiable and requires only a single model training. In our evaluations, we show promising results on six standard benchmarks, yielding novel and unique architectures at a fast speed, i.e. less than 0.2 seconds per architecture. Furthermore, we demonstrate the generalisability and efficiency of our method through experiments on ImageNet dataset.
Authors with CRIS profile
Rohan Asthana
Professur für Maschinelles Lernen in der Signalverarbeitung
Youssef Dawoud
Professur für Maschinelles Lernen in der Signalverarbeitung
Vasileios Belagiannis
Professur für Maschinelles Lernen in der Signalverarbeitung
Involved external institutions
Germany (DE)How to cite
APA:
Asthana, R., Conrad, J., Dawoud, Y., Ortmanns, M., & Belagiannis, V. (2024). Multi-conditioned Graph Diffusion for Neural Architecture Search. Transactions on Machine Learning Research.
MLA:
Asthana, Rohan, et al. "Multi-conditioned Graph Diffusion for Neural Architecture Search." Transactions on Machine Learning Research (2024).
BibTeX: Download