We lifted 4 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| abdelfattah-lab/flan_nas | canonical | 1 of 3 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| flatten_mixed_list | Ran | this paper's copy was not recorded; identical code first harvested from abdelfattah-lab/nasflat_latency pointer only · get_code("3c699604599de842") |
| transform_geno_to_nas101_format | Ran | abdelfattah-lab/flan_nas/nas_embedding_suite/nb123/nb301_arch_tuple_to_nb101.py pointer only (licence: NONE) · get_code("fb5b322121808a2b") |
| build_mat_encoding | Not yet run | abdelfattah-lab/flan_nas/nas_embedding_suite/nb123/nb301_arch_tuple_to_nb101.py pointer only (licence: NONE) · get_code("0a513424466d26bc") |
| transform_nas101_format_to_geno | Not yet run | abdelfattah-lab/flan_nas/nas_embedding_suite/nb123/nb301_arch_tuple_to_nb101.py pointer only (licence: NONE) · get_code("35fec8de282781d2") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Predictor-based methods have substantially enhanced Neural Architecture Search (NAS) optimization. The efficacy of these predictors is largely influenced by the method of encoding neural network architectures. While traditional encodings used an adjacency matrix describing the graph structure of a neural network, novel encodings embrace a variety of approaches from unsupervised pretraining of latent representations to vectors of zero-cost proxies. In this paper, we categorize and investigate neural encodings from three main types: structural, learned, and score-based. Furthermore, we extend these encodings and introduce \textit{unified encodings}, that extend NAS predictors to multiple search spaces. Our analysis draws from experiments conducted on over 1.5 million neural network architectures on NAS spaces such as NASBench-101 (NB101), NB201, NB301, Network Design Spaces (NDS), and TransNASBench-101. Building on our study, we present our predictor \textbf{FLAN}: \textbf{Fl}ow \textbf{A}ttention for \textbf{N}AS. FLAN integrates critical insights on predictor design, transfer learning, and \textit{unified encodings} to enable more than an order of magnitude cost reduction for training NAS accuracy predictors. Our implementation and encodings for all neural networks are open-sourced at \href{https://github.com/abdelfattah-lab/flan_nas}{https://github.com/abdelfattah-lab/flan\_nas}.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2403.02484")
get_code_for_paper("2403.02484")
have("2403.02484")
Connect an agent — have() is free.