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 701–750 of 1915 papers

TitleStatusHype
Search Space Adaptation for Differentiable Neural Architecture Search in Image Classification—0
A Survey on Computationally Efficient Neural Architecture Search—0
Supernet Training for Federated Image Classification under System HeterogeneityCode0
Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural ReparameterizationCode1
Multi-Complexity-Loss DNAS for Energy-Efficient and Memory-Constrained Deep Neural NetworksCode0
Automatic Relation-aware Graph Network ProliferationCode1
Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning—0
Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture—0
Concurrent Neural Tree and Data Preprocessing AutoML for Image Classification—0
MAPLE-X: Latency Prediction with Explicit Microprocessor Prior Knowledge—0
FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid?—0
A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities—0
Incremental Learning with Differentiable Architecture and Forgetting Search—0
AutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System—0
A Classification of G-invariant Shallow Neural NetworksCode0
Heat Source Layout Optimization Using Automatic Deep Learning Surrogate and Multimodal Neighborhood Search Algorithm—0
Proxyless Neural Architecture Adaptation for Supervised Learning and Self-Supervised Learning—0
Warm-starting DARTS using meta-learning—0
Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained AnalysisCode0
AutoLC: Search Lightweight and Top-Performing Architecture for Remote Sensing Image Land-Cover ClassificationCode0
AutoKE: An automatic knowledge embedding framework for scientific machine learningCode1
Efficient Automated Deep Learning for Time Series ForecastingCode4
Neural Architecture Search using Property Guided Synthesis—0
A Collaboration Strategy in the Mining Pool for Proof-of-Neural-Architecture Consensus—0
Gait Recognition in the Wild: A Large-scale Benchmark and NAS-based BaselineCode0
UnrealNAS: Can We Search Neural Architectures with Unreal Data?—0
Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction—0
Self-Programming Artificial Intelligence Using Code-Generating Language Models—0
Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering—0
PRE-NAS: Predictor-assisted Evolutionary Neural Architecture Search—0
MAPLE-Edge: A Runtime Latency Predictor for Edge Devices—0
GPUNet: Searching the Deployable Convolution Neural Networks for GPUs—0
Adaptive hybrid activation function for deep neural networksCode0
PVNAS: 3D Neural Architecture Search with Point-Voxel Convolution—0
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications—0
Towards Privacy-Preserving Neural Architecture Search—0
SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via Train-Free MetricsCode0
DeepCore: A Comprehensive Library for Coreset Selection in Deep LearningCode2
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular DatasetsCode0
Efficient Architecture Search for Diverse TasksCode1
HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images—0
Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy—0
Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture SearchCode0
When NAS Meets Trees: An Efficient Algorithm for Neural Architecture SearchCode0
SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems—0
Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs—0
A Survey of Supernet Optimization and its Applications: Spatial and Temporal Optimization for Neural Architecture Search—0
ShiftNAS: Towards Automatic Generation of Advanced Mulitplication-Less Neural Networks—0
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020Code0
Novelty Driven Evolutionary Neural Architecture SearchCode0
Show:102550
← PrevPage 15 of 39Next →
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