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 201225 of 1915 papers

TitleStatusHype
Robust 3D Face Alignment with Multi-Path Neural Architecture Search0
Heterogeneous Learning Rate Scheduling for Neural Architecture Search on Long-Tailed Datasets0
Reinforced Compressive Neural Architecture Search for Versatile Adversarial Robustness0
Multi-Objective Neural Architecture Search for In-Memory Computing0
Large Language Model Assisted Adversarial Robustness Neural Architecture SearchCode0
Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach0
BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems0
Combinatorial Optimization with Automated Graph Neural NetworksCode2
CAP: A Context-Aware Neural Predictor for NASCode0
Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS0
Fruit Classification System with Deep Learning and Neural Architecture Search0
Towards Neural Architecture Search for Transfer Learning in 6G Networks0
CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework0
Pretrained Hybrids with MAD Skills0
Multi-Objective Neural Architecture Search by Learning Search Space Partitions0
einspace: Searching for Neural Architectures from Fundamental OperationsCode1
GI-NAS: Boosting Gradient Inversion Attacks through Adaptive Neural Architecture Search0
Sheaf HyperNetworks for Personalized Federated Learning0
LLaMA-NAS: Efficient Neural Architecture Search for Large Language Models0
Efficient Visual Fault Detection for Freight Train via Neural Architecture Search with Data Volume Robustness0
Causal-aware Graph Neural Architecture Search under Distribution Shifts0
The devil is in discretization discrepancy. Robustifying Differentiable NAS with Single-Stage Searching Protocol0
HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language ModelsCode1
Moreau Envelope for Nonconvex Bi-Level Optimization: A Single-loop and Hessian-free Solution Strategy0
Differentiable Model Scaling using Differentiable TopkCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SPOS (ProxylessNAS (GPU) latency)Accuracy75.3Unverified
2SPOS (FBNet-C latency)Accuracy75.1Unverified
3SPOS (block search + channel search)Accuracy74.7Unverified
4MUXNet-xsTop-1 Error Rate33.3Unverified
5FBNetV2-F1Top-1 Error Rate31.7Unverified
6LayerNAS-60MTop-1 Error Rate31Unverified
7NASGEPTop-1 Error Rate29.51Unverified
8MUXNet-sTop-1 Error Rate28.4Unverified
9NN-MASS-ATop-1 Error Rate27.1Unverified
10FBNetV2-F3Top-1 Error Rate26.8Unverified
#ModelMetricClaimedVerifiedStatus
1CR-LSOAccuracy (Test)46.98Unverified
2Shapley-NASAccuracy (Test)46.85Unverified
3β-SDARTS-RSAccuracy (Test)46.71Unverified
4β-RDARTS-L2Accuracy (Test)46.71Unverified
5NARAccuracy (Test)46.66Unverified
6ASE-NAS+Accuracy (Val)46.66Unverified
7BaLeNAS-TFAccuracy (Test)46.54Unverified
8AG-NetAccuracy (Test)46.42Unverified
9Local searchAccuracy (Test)46.38Unverified
10NASBOTAccuracy (Test)46.37Unverified
#ModelMetricClaimedVerifiedStatus
1Balanced MixtureAccuracy (% )91.55Unverified
2GDASTop-1 Error Rate3.4Unverified
3Bonsai-NetTop-1 Error Rate3.35Unverified
4Net2 (2)Top-1 Error Rate3.3Unverified
5μDARTSTop-1 Error Rate3.28Unverified
6NN-MASS- CIFAR-CTop-1 Error Rate3.18Unverified
7DARTS (first order)Top-1 Error Rate3Unverified
8NN-MASS- CIFAR-ATop-1 Error Rate3Unverified
9AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82Unverified
10NASGEPTop-1 Error Rate2.82Unverified