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 1151–1175 of 1915 papers

TitleStatusHype
Batch Group Normalization—0
BARS: Joint Search of Cell Topology and Layout for Accurate and Efficient Binary ARchitectures—0
Visionary: Vision architecture discovery for robot learning—0
BANANAS: Bayesian Optimization with Neural Networks for Neural Architecture Search—0
MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation—0
Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework—0
MSTAR: Multi-Scale Backbone Architecture Search for Timeseries Classification—0
BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule—0
MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records—0
Multi-Agent Automated Machine Learning—0
Accelerate Intermittent Deep Inference—0
Balancing Accuracy and Latency in Multipath Neural Networks—0
Bag of Tricks for Neural Architecture Search—0
Symbolic Regression on FPGAs for Fast Machine Learning Inference—0
Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search—0
Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search—0
Syno: Structured Synthesis for Neural Operators—0
aw_nas: A Modularized and Extensible NAS framework—0
A Web-Based Solution for Federated Learning with LLM-Based Automation—0
Multi-Objective Evolutionary for Object Detection Mobile Architectures Search—0
A Deeper Look at Zero-Cost Proxies for Lightweight NAS—0
Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness—0
Multi-Objective Meta Learning—0
Multi-objective Neural Architecture Search with Almost No Training—0
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS—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