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 101–125 of 1915 papers

TitleStatusHype
SA-GNAS: Seed Architecture Expansion for Efficient Large-scale Graph Neural Architecture SearchCode0
ILASH: A Predictive Neural Architecture Search Framework for Multi-Task Applications—0
GradAlign for Training-free Model Performance Inference—0
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs—0
Knowledge-aware Evolutionary Graph Neural Architecture SearchCode0
A Graph Neural Architecture Search Approach for Identifying Bots in Social Media—0
Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction ModelsCode0
TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks—0
GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient NASCode0
Delta-NAS: Difference of Architecture Encoding for Predictor-based Evolutionary Neural Architecture Search—0
Improving Routability Prediction via NAS Using a Smooth One-shot Augmented Predictor—0
Data-to-Model Distillation: Data-Efficient Learning FrameworkCode0
Exploring the Manifold of Neural Networks Using Diffusion Geometry—0
Retinal Vessel Segmentation via Neuron Programming—0
RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively—0
SASE: A Searching Architecture for Squeeze and Excitation Operations—0
Zero-Shot NAS via the Suppression of Local Entropy Decrease—0
Learning Morphisms with Gauss-Newton Approximation for Growing Networks—0
Customized Subgraph Selection and Encoding for Drug-drug Interaction PredictionCode0
Differentiable architecture search with multi-dimensional attention for spiking neural networks—0
Syno: Structured Synthesis for Neural Operators—0
Hyperparameter Optimization in Machine Learning—0
Yoga Pose Classification Using Transfer Learning—0
Developing Convolutional Neural Networks using a Novel Lamarckian Co-Evolutionary Algorithm—0
Towards Robust Out-of-Distribution Generalization: Data Augmentation and Neural Architecture Search Approaches—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-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