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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

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Showing 651–700 of 1915 papers

TitleStatusHype
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanismCode0
BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture SearchCode0
Efficient Incorporation of Multiple Latency Targets in the Once-For-All NetworkCode0
Behaviour DistillationCode0
Efficient hyperparameter optimization by way of PAC-Bayes bound minimizationCode0
Efficient Global Neural Architecture SearchCode0
How to 0wn the NAS in Your Spare TimeCode0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
Homogeneous Architecture Augmentation for Neural PredictorCode0
MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained DevicesCode0
HiveNAS: Neural Architecture Search using Artificial Bee Colony OptimizationCode0
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass LensCode0
Efficient Decoupled Neural Architecture Search by Structure and Operation SamplingCode0
Hierarchical Representations for Efficient Architecture SearchCode0
Are Neural Architecture Search Benchmarks Well Designed? A Deeper Look Into Operation ImportanceCode0
How does topology of neural architectures impact gradient propagation and model performance?Code0
Bayesian Neural Network Language Modeling for Speech RecognitionCode0
Heterogeneous Graph Neural Architecture Search with GPT-4Code0
Efficient Architecture Search by Network TransformationCode0
Meta Architecture SearchCode0
Efficient and Explainable Graph Neural Architecture Search via Monte-Carlo Tree SearchCode0
Efficacy of Neural Prediction-Based Zero-Shot NASCode0
How to 0wn NAS in Your Spare TimeCode0
Improving Ranking Correlation of Supernet with Candidates Enhancement and Progressive TrainingCode0
Learning Implicitly Recurrent CNNs Through Parameter SharingCode0
Budgeted Training: Rethinking Deep Neural Network Training Under Resource ConstraintsCode0
Evolutionary Architecture Search for Graph Neural NetworksCode0
Guided Evolution for Neural Architecture SearchCode0
Bayesian Learning of Neural Network ArchitecturesCode0
Building Optimal Neural Architectures using Interpretable KnowledgeCode0
Autoequivariant Network Search via Group DecompositionCode0
BayesFT: Bayesian Optimization for Fault Tolerant Neural Network ArchitectureCode0
BATS: Binary ArchitecTure SearchCode0
ECToNAS: Evolutionary Cross-Topology Neural Architecture SearchCode0
GraphPAS: Parallel Architecture Search for Graph Neural NetworksCode0
GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient NASCode0
GradSign: Model Performance Inference with Theoretical InsightsCode0
BatchQuant: Quantized-for-all Architecture Search with Robust QuantizerCode0
NAAP-440 Dataset and Baseline for Neural Architecture Accuracy PredictionCode0
GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generatorsCode0
EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale 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
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
GraphNAS: Graph Neural Architecture Search with Reinforcement LearningCode0
Hardware Aware Neural Network Architectures using FbNetCode0
Band-gap regression with architecture-optimized message-passing neural networksCode0
DVOLVER: Efficient Pareto-Optimal Neural Network Architecture SearchCode0
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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-SDARTS-RSAccuracy (Test)46.71—Unverified
4β-RDARTS-L2Accuracy (Test)46.71—Unverified
5NARAccuracy (Test)46.66—Unverified
6ASE-NAS+Accuracy (Val)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