Search Results for author: Arjun Krishnakumar

Found 3 papers, 2 papers with code

Weight-Entanglement Meets Gradient-Based Neural Architecture Search

no code implementations16 Dec 2023 Rhea Sanjay Sukthanker, Arjun Krishnakumar, Mahmoud Safari, Frank Hutter

%Due to the inherent differences in the structure of these search spaces, these Since weight-entanglement poses compatibility challenges for gradient-based NAS methods, these two paradigms have largely developed independently in parallel sub-communities.

Neural Architecture Search

NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies

1 code implementation6 Oct 2022 Arjun Krishnakumar, Colin White, Arber Zela, Renbo Tu, Mahmoud Safari, Frank Hutter

Zero-cost proxies (ZC proxies) are a recent architecture performance prediction technique aiming to significantly speed up algorithms for neural architecture search (NAS).

Neural Architecture Search

NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy

1 code implementation ICLR 2022 Yash Mehta, Colin White, Arber Zela, Arjun Krishnakumar, Guri Zabergja, Shakiba Moradian, Mahmoud Safari, Kaicheng Yu, Frank Hutter

The release of tabular benchmarks, such as NAS-Bench-101 and NAS-Bench-201, has significantly lowered the computational overhead for conducting scientific research in neural architecture search (NAS).

Image Classification Neural Architecture Search +4

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