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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 701–750 of 1915 papers

TitleStatusHype
Efficacy of Neural Prediction-Based Zero-Shot NASCode0
Network Pruning via Transformable Architecture SearchCode0
ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network ArchitecturesCode0
Improving Ranking Correlation of Supernet with Candidates Enhancement and Progressive TrainingCode0
ISyNet: Convolutional Neural Networks design for AI acceleratorCode0
How to 0wn NAS in Your Spare TimeCode0
Bayesian Learning of Neural Network ArchitecturesCode0
How to 0wn the NAS in Your Spare TimeCode0
Exploring Randomly Wired Neural Networks for Image RecognitionCode0
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass LensCode0
How does topology of neural architectures impact gradient propagation and model performance?Code0
HiveNAS: Neural Architecture Search using Artificial Bee Colony OptimizationCode0
Neural Architecture Search for Sentence Classification with BERTCode0
BayesFT: Bayesian Optimization for Fault Tolerant Neural Network ArchitectureCode0
BATS: Binary ArchitecTure SearchCode0
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural NetworksCode0
ECToNAS: Evolutionary Cross-Topology Neural Architecture SearchCode0
Homogeneous Architecture Augmentation for Neural PredictorCode0
Heterogeneous Graph Neural Architecture Search with GPT-4Code0
BatchQuant: Quantized-for-all Architecture Search with Robust QuantizerCode0
EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture SearchCode0
FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture SearchCode0
EASNet: Searching Elastic and Accurate Network Architecture for Stereo MatchingCode0
BASQ: Branch-wise Activation-clipping Search Quantization for Sub-4-bit Neural NetworksCode0
Fast, Accurate and Lightweight Super-Resolution with Neural Architecture SearchCode0
Fast and Practical Neural Architecture SearchCode0
Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS "Cold-Start"Code0
Architecture-Aware Minimization (A^2M): How to Find Flat Minima in Neural Architecture SearchCode0
CHASE: Robust Visual Tracking via Cell-Level Differentiable Neural Architecture SearchCode0
Hierarchical Representations for Efficient Architecture SearchCode0
Band-gap regression with architecture-optimized message-passing neural networksCode0
DVOLVER: Efficient Pareto-Optimal Neural Network Architecture SearchCode0
D-VAE: A Variational Autoencoder for Directed Acyclic GraphsCode0
Neural Predictor for Neural Architecture SearchCode0
DropNAS: Grouped Operation Dropout for Differentiable Architecture SearchCode0
BAM: Bottleneck Attention ModuleCode0
Autoequivariant Network Search via Group DecompositionCode0
Fast Neural Network Adaptation via Parameter Remapping and Architecture SearchCode0
Guided Evolution for Neural Architecture SearchCode0
Hardware Aware Neural Network Architectures using FbNetCode0
DPNAS: Neural Architecture Search for Deep Learning with Differential PrivacyCode0
FBNetV3: Joint Architecture-Recipe Search using Predictor PretrainingCode0
ABC-Di: Approximate Bayesian Computation for Discrete DataCode0
Do Not Train It: A Linear Neural Architecture Search of Graph Neural NetworksCode0
GraphPAS: Parallel Architecture Search for Graph Neural NetworksCode0
GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient NASCode0
GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generatorsCode0
Balanced Mixture of SuperNets for Learning the CNN Pooling ArchitectureCode0
Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture SearchCode0
GradSign: Model Performance Inference with Theoretical InsightsCode0
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All datasetsImageNetNAS-Bench-201, ImageNet-16-120CIFAR-10NAS-Bench-201, CIFAR-100NAS-Bench-201, CIFAR-10CIFAR-10 Image ClassificationCIFAR-100NATS-Bench Topology, CIFAR-10NATS-Bench Topology, CIFAR-100NATS-Bench Topology, ImageNet16-120Food-101NAS-Bench-101

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-RDARTS-L2Accuracy (Test)46.71—Unverified
4β-SDARTS-RSAccuracy (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
7NN-MASS- CIFAR-ATop-1 Error Rate3—Unverified
8DARTS (first order)Top-1 Error Rate3—Unverified
9NASGEPTop-1 Error Rate2.82—Unverified
10AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82—Unverified