SOTAVerified

Neural Architecture Search

Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS essentially takes the process of a human manually tweaking a neural network and learning what works well, and automates this task to discover more complex architectures.

Image Credit : NAS with Reinforcement Learning

Papers

Showing 14761500 of 1915 papers

TitleStatusHype
Provably Convergent Federated Trilevel Learning0
A Pairwise Comparison Relation-assisted Multi-objective Evolutionary Neural Architecture Search Method with Multi-population Mechanism0
ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies0
Anytime Neural Architecture Search on Tabular Data0
Proxyless Neural Architecture Adaptation for Supervised Learning and Self-Supervised Learning0
Anti-Bandit Neural Architecture Search for Model Defense0
Pruning Compact ConvNets for Efficient Inference0
A Novel Watermarking Framework for Ownership Verification of DNN Architectures0
Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights0
PSE-Net: Channel Pruning for Convolutional Neural Networks with Parallel-subnets Estimator0
Across-Task Neural Architecture Search via Meta Learning0
Pushing Joint Image Denoising and Classification to the Edge0
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs0
PVNAS: 3D Neural Architecture Search with Point-Voxel Convolution0
PV-NAS: Practical Neural Architecture Search for Video Recognition0
Pyramid Architecture Search for Real-Time Image Deblurring0
A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trick0
QuadraNet: Improving High-Order Neural Interaction Efficiency with Hardware-Aware Quadratic Neural Networks0
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles0
A Quantile-based Approach for Hyperparameter Transfer Learning0
Quantum Architecture Search with Unsupervised Representation Learning0
Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization0
Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation0
Qubit-Wise Architecture Search Method for Variational Quantum Circuits0
QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SPOS (ProxylessNAS (GPU) latency)Accuracy75.3Unverified
2SPOS (FBNet-C latency)Accuracy75.1Unverified
3SPOS (block search + channel search)Accuracy74.7Unverified
4MUXNet-xsTop-1 Error Rate33.3Unverified
5FBNetV2-F1Top-1 Error Rate31.7Unverified
6LayerNAS-60MTop-1 Error Rate31Unverified
7NASGEPTop-1 Error Rate29.51Unverified
8MUXNet-sTop-1 Error Rate28.4Unverified
9NN-MASS-ATop-1 Error Rate27.1Unverified
10FBNetV2-F3Top-1 Error Rate26.8Unverified
#ModelMetricClaimedVerifiedStatus
1CR-LSOAccuracy (Test)46.98Unverified
2Shapley-NASAccuracy (Test)46.85Unverified
3β-RDARTS-L2Accuracy (Test)46.71Unverified
4β-SDARTS-RSAccuracy (Test)46.71Unverified
5ASE-NAS+Accuracy (Val)46.66Unverified
6NARAccuracy (Test)46.66Unverified
7BaLeNAS-TFAccuracy (Test)46.54Unverified
8AG-NetAccuracy (Test)46.42Unverified
9Local searchAccuracy (Test)46.38Unverified
10NASBOTAccuracy (Test)46.37Unverified
#ModelMetricClaimedVerifiedStatus
1Balanced MixtureAccuracy (% )91.55Unverified
2GDASTop-1 Error Rate3.4Unverified
3Bonsai-NetTop-1 Error Rate3.35Unverified
4Net2 (2)Top-1 Error Rate3.3Unverified
5μDARTSTop-1 Error Rate3.28Unverified
6NN-MASS- CIFAR-CTop-1 Error Rate3.18Unverified
7NN-MASS- CIFAR-ATop-1 Error Rate3Unverified
8DARTS (first order)Top-1 Error Rate3Unverified
9NASGEPTop-1 Error Rate2.82Unverified
10AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82Unverified