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 951–1000 of 1915 papers

TitleStatusHype
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers—0
Spatial-Temporal Search for Spiking Neural Networks—0
An Experimental Study of Weight Initialization and Weight Inheritance Effects on Neuroevolution—0
Speeding up NAS with Adaptive Subset Selection—0
Improving One-shot NAS by Suppressing the Posterior Fading—0
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness—0
SPIDER: Searching Personalized Neural Architecture for Federated Learning—0
Improving Routability Prediction via NAS Using a Smooth One-shot Augmented Predictor—0
CLASH: Complementary Learning with Neural Architecture Search for Gait Recognition—0
SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-based Autonomous Agents—0
Improving the Energy Efficiency and Robustness of tinyML Computer Vision using Log-Gradient Input Images—0
Uniform-Precision Neural Network Quantization via Neural Channel Expansion—0
Improving Zero-Shot Neural Architecture Search with Parameters Scoring—0
iNAS: Integral NAS for Device-Aware Salient Object Detection—0
Chimera: A Block-Based Neural Architecture Search Framework for Event-Based Object Detection—0
Incremental Learning with Differentiable Architecture and Forgetting Search—0
Inductive Transfer for Neural Architecture Optimization—0
Inference Latency Prediction at the Edge—0
SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems—0
ZenDet: Revisiting Efficient Object Detection Backbones from Zero-Shot Neural Architecture Search—0
Accelerator-aware Neural Network Design using AutoML—0
InstaTune: Instantaneous Neural Architecture Search During Fine-Tuning—0
Instructing the Architecture Search for Spatial-temporal Sequence Forecasting with LLM—0
Channel Planting for Deep Neural Networks using Knowledge Distillation—0
Inter-choice dependent super-network weights—0
InterGridNet: An Electric Network Frequency Approach for Audio Source Location Classification Using Convolutional Neural Networks—0
SQuantizer: Simultaneous Learning for Both Sparse and Low-precision Neural Networks—0
Interleaving Learning, with Application to Neural Architecture Search—0
Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition—0
Intra-layer Neural Architecture Search—0
Intriguing Properties of Adversarial Examples—0
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond—0
UnrealNAS: Can We Search Neural Architectures with Unreal Data?—0
SSS3D: Fast Neural Architecture Search For Efficient Three-Dimensional Semantic Segmentation—0
Zen-NAS: A Zero-Shot NAS for High-Performance Image Recognition—0
ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search—0
Chain-structured neural architecture search for financial time series forecasting—0
Dynamic Routing Networks—0
ISBNet: Instance-aware Selective Branching Networks—0
Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search—0
CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework—0
Causal-aware Graph Neural Architecture Search under Distribution Shifts—0
Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision—0
JMSNAS: Joint Model Split and Neural Architecture Search for Learning over Mobile Edge Networks—0
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation—0
Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition—0
Joint Neural Architecture Search and Quantization—0
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search—0
Katib: A Distributed General AutoML Platform on Kubernetes—0
Kernel-Level Energy-Efficient Neural Architecture Search for Tabular Dataset—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