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

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Showing 351–400 of 1915 papers

TitleStatusHype
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge DistillationCode0
MicroNAS: Memory and Latency Constrained Hardware-Aware Neural Architecture Search for Time Series Classification on Microcontrollers—0
Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image ClassificationCode0
An Approach for Efficient Neural Architecture Search Space Definition—0
LLM Performance Predictors are good initializers for Architecture SearchCode0
Cascaded Multi-task Adaptive Learning Based on Neural Architecture Search—0
MGAS: Multi-Granularity Architecture Search for Trade-Off Between Model Effectiveness and Efficiency—0
Fairer and More Accurate Tabular Models Through NAS—0
ASP: Automatic Selection of Proxy dataset for efficient AutoML—0
Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT DevicesCode1
Entropic Score metric: Decoupling Topology and Size in Training-free NAS—0
Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular DataCode0
Brain development dictates energy constraints on neural architecture search: cross-disciplinary insights on optimization strategies—0
Evolutionary Neural Architecture Search for Transformer in Knowledge TracingCode1
Learnable Extended Activation Function (LEAF) for Deep Neural NetworksCode0
Graph Neural Architecture Search with GPT-4—0
Order-Preserving GFlowNetsCode0
DataDAM: Efficient Dataset Distillation with Attention MatchingCode1
DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks—0
ZiCo-BC: A Bias Corrected Zero-Shot NAS for Vision Tasks—0
NAS-NeRF: Generative Neural Architecture Search for Neural Radiance Fields—0
Grassroots Operator Search for Model Edge Adaptation—0
iHAS: Instance-wise Hierarchical Architecture Search for Deep Learning Recommendation Models—0
Harmonic-NAS: Hardware-Aware Multimodal Neural Architecture Search on Resource-constrained DevicesCode1
Band-gap regression with architecture-optimized message-passing neural networksCode0
SSHNN: Semi-Supervised Hybrid NAS Network for Echocardiographic Image SegmentationCode0
DBsurf: A Discrepancy Based Method for Discrete Stochastic Gradient Estimation—0
Efficacy of Neural Prediction-Based Zero-Shot NASCode0
Efficient and Explainable Graph Neural Architecture Search via Monte-Carlo Tree SearchCode0
InstaTune: Instantaneous Neural Architecture Search During Fine-Tuning—0
Generalizable Learning Reconstruction for Accelerating MR Imaging via Federated Neural Architecture SearchCode0
HNAS-reg: hierarchical neural architecture search for deformable medical image registration—0
A Benchmark Study on Calibration—0
EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning—0
ResBuilder: Automated Learning of Depth with Residual Structures—0
Asynchronous Evolution of Deep Neural Network Architectures—0
AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic ClassificationCode1
Efficient Model Adaptation for Continual Learning at the Edge—0
Shrink-Perturb Improves Architecture Mixing during Population Based Training for Neural Architecture SearchCode0
YOLOBench: Benchmarking Efficient Object Detectors on Embedded SystemsCode0
FedAutoMRI: Federated Neural Architecture Search for MR Image Reconstruction—0
Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture SearchCode1
A Survey on Multi-Objective Neural Architecture Search—0
ShiftNAS: Improving One-shot NAS via Probability ShiftCode0
A Survey of Techniques for Optimizing Transformer Inference—0
MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for Heterogeneous MPSoC Deployment—0
GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generatorsCode0
DDNAS: Discretized Differentiable Neural Architecture Search for Text ClassificationCode0
Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture SearchCode1
Search-time Efficient Device Constraints-Aware Neural Architecture Search—0
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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