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 176–200 of 1915 papers

TitleStatusHype
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
Accelerating Evolutionary Neural Architecture Search via Multi-Fidelity EvaluationCode1
AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic ClassificationCode1
EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture SearchCode1
Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT DevicesCode1
Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed SpacesCode1
Equivalence in Deep Neural Networks via Conjugate Matrix EnsemblesCode1
Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree SearchCode1
Evolutionary Neural Cascade Search across SupernetworksCode1
EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation using Accelerated Neuroevolution with Weight TransferCode1
Exploring Relational Context for Multi-Task Dense PredictionCode1
Adaptive Cross-Layer Attention for Image RestorationCode1
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture SearchCode1
ChamNet: Towards Efficient Network Design through Platform-Aware Model AdaptationCode1
Automatic Relation-aware Graph Network ProliferationCode1
b-DARTS: Beta-Decay Regularization for Differentiable Architecture SearchCode1
AdvRush: Searching for Adversarially Robust Neural ArchitecturesCode1
FOX-NAS: Fast, On-device and Explainable Neural Architecture SearchCode1
FreeREA: Training-Free Evolution-based Architecture SearchCode1
β-DARTS++: Bi-level Regularization for Proxy-robust Differentiable Architecture SearchCode1
FuSeConv: Fully Separable Convolutions for Fast Inference on Systolic ArraysCode1
Bayesian Neural Architecture Search using A Training-Free Performance MetricCode1
AutoGL: A Library for Automated Graph LearningCode1
AOWS: Adaptive and optimal network width search with latency constraintsCode1
β-DARTS: Beta-Decay Regularization for Differentiable Architecture SearchCode1
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Benchmark Results

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