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 551–575 of 1915 papers

TitleStatusHype
Improve Ranking Correlation of Super-net through Training Scheme from One-shot NAS to Few-shot NASCode0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactionsCode0
Latency-Aware Differentiable Neural Architecture SearchCode0
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanismCode0
CAP: A Context-Aware Neural Predictor for NASCode0
A Transformer-based Neural Architecture Search MethodCode0
How to 0wn the NAS in Your Spare TimeCode0
How to 0wn NAS in Your Spare TimeCode0
CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive AutoencodersCode0
Butterfly Transform: An Efficient FFT Based Neural Architecture DesignCode0
ATOM: Attention Mixer for Efficient Dataset DistillationCode0
Building Optimal Neural Architectures using Interpretable KnowledgeCode0
Homogeneous Architecture Augmentation for Neural PredictorCode0
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass LensCode0
Budgeted Training: Rethinking Deep Neural Network Training Under Resource ConstraintsCode0
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture SearchCode0
HiveNAS: Neural Architecture Search using Artificial Bee Colony OptimizationCode0
How does topology of neural architectures impact gradient propagation and model performance?Code0
Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image ClassificationCode0
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020Code0
Heterogeneous Graph Neural Architecture Search with GPT-4Code0
Bridge the Gap Between Architecture Spaces via A Cross-Domain PredictorCode0
Hardware Aware Neural Network Architectures using FbNetCode0
Hardware/Software Co-Exploration of Neural ArchitecturesCode0
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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