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GraViT-E: Gradient-based Vision Transformer Search with Entangled Weights
Rhea Sukthanker · Arjun Krishnakumar · sharat patil · Frank Hutter
Event URL: https://openreview.net/forum?id=dm8WcWiuvd »

Differentiable one-shot neural architecture search methods have recently become popular since they can exploit weight-sharing to efficiently search in large architectural search spaces. These methods traditionally perform a continuous relaxation of the discrete search space to search for an optimal architecture. However, they suffer from large memory requirements, making their application to parameter-heavy architectures like transformers difficult. Recently, single-path one-shot methods have been introduced which often use weight entanglement to alleviate this issue by sampling the weights of the sub-networks from the largest model, which is itself the supernet. In this work, we propose a continuous relaxation of weight entanglement-based architectural representation. Our Gradient-based Vision Transformer Search with Entangled Weights (GraViT-E) combines the best properties of both differentiable one-shot NAS and weight entanglement. We observe that our method imparts much better regularization properties and memory efficiency to the trained supernet. We study three one-shot optimizers on the Vision Transformer search space and observe that our method outperforms existing baselines on multiple datasets while being upto 35% more parameter efficient on ImageNet-1k.

Author Information

Rhea Sukthanker (University of Freiburg, Albert-Ludwigs-Universität Freiburg)
Arjun Krishnakumar (University of Freiburg)
sharat patil
Frank Hutter (University of Freiburg & Bosch)

Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), where he previously was an assistant professor 2013-2017. Before that, he was at the University of British Columbia (UBC) for eight years, for his PhD and postdoc. Frank's main research interests lie in machine learning, artificial intelligence and automated algorithm design. For his 2009 PhD thesis on algorithm configuration, he received the CAIAC doctoral dissertation award for the best thesis in AI in Canada that year, and with his coauthors, he received several best paper awards and prizes in international competitions on machine learning, SAT solving, and AI planning. Since 2016 he holds an ERC Starting Grant for a project on automating deep learning based on Bayesian optimization, Bayesian neural networks, and deep reinforcement learning.

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