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NASBench-101
The NASBench-101 dataset is a large-scale benchmark for neural architecture search. It contains nearly 423K unique convolutional neural network architectures with diverse... -
ArchExplorer
ArchExplorer is a visual analysis method for understanding a neural architecture space and summarizing design principles. -
NAS-Bench-301 and AlphaNet
The dataset used in the paper is NAS-Bench-301 and AlphaNet. NAS-Bench-301 is a surrogate NAS benchmark built via deep ensembles and modeling uncertainty, which provides... -
Tiny-NanoBench
The proposed Siamese-Predictor using EFM to fuse prior knowledge - Estimation Code. The proposed Estimation Code and accuracy of architectures are highly correlated based on our... -
NASBench-201
The proposed Siamese-Predictor using EFM to fuse prior knowledge - Estimation Code. The proposed Estimation Code and accuracy of architectures are highly correlated based on our... -
Siamese-Predictor for Neural Architecture Search
The proposed Siamese-Predictor using EFM to fuse prior knowledge - Estimation Code. The proposed Estimation Code and accuracy of architectures are highly correlated based on our... -
Re-parameterization Operations Search for Easy-to-Deploy Network
Structural re-parameterization technology provides a new idea to improve the performance of traditional convolutional networks. -
ProxylessNAS
The dataset used in the paper to test the proposed Decomposable Winograd Method (DWM) for convolution acceleration. -
NAS-Bench Event Sequences
The NAS-Bench Event Sequences dataset is used for Event Sequence Classification. -
SeqNAS: Neural Architecture Search for Event Sequence Classification
Event Sequences (EvS) with marker and timing information are very common in real-world applications such as medicine, biology, social medial analysis, fault diagnosis, churn... -
NAS-Bench-101
The dataset used in the paper is NAS-Bench-101, a benchmark for neural architecture search. -
ENAS Macro
The dataset used in the paper is ENAS Macro, a search space for neural architecture search. -
MnasNet: Platform-Aware Neural Architecture Search for Mobile
Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts... -
Multi-conditioned Graph Diffusion for Neural Architecture Search
Multi-conditioned Graph Diffusion for Neural Architecture Search -
Data-Free Neural Architecture Search via Recursive Label Calibration
This paper presents a novel framework for data-free neural architecture search (NAS) without access to the original data. -
Progressive Neural Architecture Search
Progressive Neural Architecture Search -
Designing Network Design Spaces
Designing Network Design Spaces
