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Models/CR-LSO

CR-LSO

Reported on 12 benchmarks across 2 tasks · 1 paper · 2 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology12 results

Neural Architecture SearchonNAS-Bench-201, ImageNet-16-120
Accuracy (Test)· 2022-11-11
46.98
SOTA
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks arXiv:2211.05950
AutoMLonNAS-Bench-201, ImageNet-16-120
Accuracy (Test)· 2022-11-11
46.98
SOTA
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks arXiv:2211.05950
Neural Architecture SearchonNAS-Bench-201, ImageNet-16-120
Accuracy (Val)· 2022-11-11
46.51
best: 46.73 (AG-Net)
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks arXiv:2211.05950
Neural Architecture SearchonNAS-Bench-201, CIFAR-10
Accuracy (Test)· 2022-11-11
94.35
best: 94.37 (DiNAS)
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks arXiv:2211.05950
Neural Architecture SearchonNAS-Bench-201, CIFAR-10
Accuracy (Val)· 2022-11-11
91.54
best: 91.61 (DiNAS)
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks arXiv:2211.05950
Neural Architecture SearchonNAS-Bench-201, CIFAR-100
Accuracy (Test)· 2022-11-11
73.47
best: 73.51 (DiNAS)
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks arXiv:2211.05950
Neural Architecture SearchonNAS-Bench-201, CIFAR-100
Accuracy (Val)· 2022-11-11
73.44
best: 73.49 (DiNAS)
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks arXiv:2211.05950
AutoMLonNAS-Bench-201, ImageNet-16-120
Accuracy (Val)· 2022-11-11
46.51
best: 46.73 (AG-Net)
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks arXiv:2211.05950
AutoMLonNAS-Bench-201, CIFAR-10
Accuracy (Test)· 2022-11-11
94.35
best: 94.37 (DiNAS)
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks arXiv:2211.05950
AutoMLonNAS-Bench-201, CIFAR-10
Accuracy (Val)· 2022-11-11
91.54
best: 91.61 (DiNAS)
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks arXiv:2211.05950
AutoMLonNAS-Bench-201, CIFAR-100
Accuracy (Test)· 2022-11-11
73.47
best: 73.51 (DiNAS)
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks arXiv:2211.05950
AutoMLonNAS-Bench-201, CIFAR-100
Accuracy (Val)· 2022-11-11
73.44
best: 73.49 (DiNAS)
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks arXiv:2211.05950