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 1351–1400 of 1915 papers

TitleStatusHype
NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and SizeCode1
Graph Neural Network Architecture Search for Molecular Property Prediction—0
NAS-DIP: Learning Deep Image Prior with Neural Architecture SearchCode1
Simplifying Architecture Search for Graph Neural NetworkCode1
NASirt: AutoML based learning with instance-level complexity information—0
Learned Transferable Architectures Can Surpass Hand-Designed Architectures for Large Scale Speech Recognition—0
A Survey on Evolutionary Neural Architecture Search—0
LC-NAS: Latency Constrained Neural Architecture Search for Point Cloud Networks—0
Automated Search for Resource-Efficient Branched Multi-Task NetworksCode1
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS BenchmarksCode1
Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture RecognitionCode1
Enhanced MRI Reconstruction Network using Neural Architecture Search—0
NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule NetworksCode0
Discovering Multi-Hardware Mobile Models via Architecture Search—0
NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture SearchCode0
Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation—0
AutoPose: Searching Multi-Scale Branch Aggregation for Pose EstimationCode0
Finding Fast Transformers: One-Shot Neural Architecture Search by Component Composition—0
Efficient hyperparameter optimization by way of PAC-Bayes bound minimizationCode0
Can weight sharing outperform random architecture search? An investigation with TuNAS—0
Network Architecture Search for Domain Adaptation—0
Evolutionary Algorithm Enhanced Neural Architecture Search for Text-Independent Speaker Verification—0
TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture SearchCode1
NASB: Neural Architecture Search for Binary Convolutional Neural Networks—0
Evaluating Efficient Performance Estimators of Neural Architectures—0
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap—0
Anti-Bandit Neural Architecture Search for Model Defense—0
Shape Adaptor: A Learnable Resizing ModuleCode1
Evolving Multi-Resolution Pooling CNN for Monaural Singing Voice Separation—0
Differentiable Feature Aggregation Search for Knowledge Distillation—0
S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search—0
Neural Architecture Search in Graph Neural NetworksCode1
Searching Efficient 3D Architectures with Sparse Point-Voxel ConvolutionCode2
HMCNAS: Neural Architecture Search using Hidden Markov Chains and Bayesian Optimization—0
Neural Architecture Search as Sparse Supernet—0
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound—0
Efficient OCT Image Segmentation Using Neural Architecture Search—0
SOTERIA: In Search of Efficient Neural Networks for Private InferenceCode0
What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning—0
Representation Sharing for Fast Object Detector Search and BeyondCode1
NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture SearchCode1
MCUNet: Tiny Deep Learning on IoT DevicesCode1
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search—0
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture SearchCode1
Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks—0
Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot StartCode0
BRP-NAS: Prediction-based NAS using GCNs—0
On Adversarial Robustness: A Neural Architecture Search perspectiveCode0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation—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
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