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 1851–1900 of 1915 papers

TitleStatusHype
FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search—0
FLASH: Fast Neural Architecture Search with Hardware Optimization—0
ZiCo-BC: A Bias Corrected Zero-Shot NAS for Vision Tasks—0
FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid?—0
Flexible Channel Dimensions for Differentiable Architecture Search—0
Flexiffusion: Segment-wise Neural Architecture Search for Flexible Denoising Schedule—0
Flexiffusion: Training-Free Segment-Wise Neural Architecture Search for Efficient Diffusion Models—0
FL-NAS: Towards Fairness of NAS for Resource Constrained Devices via Large Language Models—0
DASViT: Differentiable Architecture Search for Vision Transformer—0
FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation—0
DAS: Neural Architecture Search via Distinguishing Activation Score—0
FNAS: Uncertainty-Aware Fast Neural Architecture Search—0
FocusFormer: Focusing on What We Need via Architecture Sampler—0
Forecasting of COVID-19 Cases, Using an Evolutionary Neural Architecture Search Approach—0
DARTS without a Validation Set: Optimizing the Marginal Likelihood—0
FP-NAS: Fast Probabilistic Neural Architecture Search—0
FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames—0
DARTS for Inverse Problems: a Study on Stability—0
Is Differentiable Architecture Search truly a One-Shot Method?—0
From Hand-Crafted Metrics to Evolved Training-Free Performance Predictors for Neural Architecture Search via Genetic Programming—0
DARTFormer: Finding The Best Type Of Attention—0
Sheaf HyperNetworks for Personalized Federated Learning—0
Fruit Classification System with Deep Learning and Neural Architecture Search—0
FSD: Fully-Specialized Detector via Neural Architecture Search—0
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary—0
FTSO: Effective NAS via First Topology Second Operator—0
Shears: Unstructured Sparsity with Neural Low-rank Adapter Search—0
Full-attention based Neural Architecture Search using Context Auto-regression—0
Full Stack Optimization of Transformer Inference: a Survey—0
DARC: Differentiable ARchitecture Compression—0
ShiftAddAug: Augment Multiplication-Free Tiny Neural Network with Hybrid Computation—0
Uncertainty Modeling of Emerging Device-based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search—0
DANCE: Differentiable Accelerator/Network Co-Exploration—0
DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search—0
DAAS: Differentiable Architecture and Augmentation Policy Search—0
ShiftNAS: Towards Automatic Generation of Advanced Mulitplication-Less Neural Networks—0
CrossNAS: A Cross-Layer Neural Architecture Search Framework for PIM Systems—0
Generalization Guarantees for Neural Architecture Search with Train-Validation Split—0
Generalization Properties of NAS under Activation and Skip Connection Search—0
Generalized Hadamard-Product Fusion Operators for Visual Question Answering—0
Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning—0
CP-NAS: Child-Parent Neural Architecture Search for Binary Neural Networks—0
Generative Adversarial Neural Architecture Search with Importance Sampling—0
Generative Adversarial Neural Architecture Search—0
Generative flow induced neural architecture search: Towards discovering optimal architecture in wavelet neural operator—0
ShrinkNAS : Single-Path One-Shot Operator Exploratory Training for Transformer with Dynamic Space Shrinking—0
CP-CNN: Core-Periphery Principle Guided Convolutional Neural Network—0
A Collaboration Strategy in the Mining Pool for Proof-of-Neural-Architecture Consensus—0
Genetic Neural Architecture Search for automatic assessment of human sperm images—0
ShuffleNASNets: Efficient CNN models through modified Efficient Neural Architecture Search—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