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 151–200 of 1915 papers

TitleStatusHype
deepstruct -- linking deep learning and graph theoryCode1
DU-DARTS: Decreasing the Uncertainty of Differentiable Architecture SearchCode1
AutoSpeech: Neural Architecture Search for Speaker RecognitionCode1
EEEA-Net: An Early Exit Evolutionary Neural Architecture SearchCode1
AutoSTR: Efficient Backbone Search for Scene Text RecognitionCode1
A Study on Encodings for Neural Architecture SearchCode1
Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture SearchCode1
Automated Machine Learning on Graphs: A SurveyCode1
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture SearchCode1
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter OptimizationCode1
Adjoined Networks: A Training Paradigm with Applications to Network CompressionCode1
EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture SearchCode1
Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT ScansCode1
emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion RecognitionCode1
Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT DevicesCode1
EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture SearchCode1
AIO-P: Expanding Neural Performance Predictors Beyond Image ClassificationCode1
Bayesian Neural Architecture Search using A Training-Free Performance MetricCode1
Automated Search for Resource-Efficient Branched Multi-Task NetworksCode1
Evolutionary Neural AutoML for Deep LearningCode1
AtomNAS: Fine-Grained End-to-End Neural Architecture SearchCode1
b-DARTS: Beta-Decay Regularization for Differentiable Architecture SearchCode1
EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation using Accelerated Neuroevolution with Weight TransferCode1
Exploring Relational Context for Multi-Task Dense PredictionCode1
AttentiveNAS: Improving Neural Architecture Search via Attentive SamplingCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
Accelerating Evolutionary Neural Architecture Search via Multi-Fidelity EvaluationCode1
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage ModelsCode1
Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and DirectionsCode1
BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image SegmentationCode1
AlphaNet: Improved Training of Supernets with Alpha-DivergenceCode1
Block-Wisely Supervised Neural Architecture Search With Knowledge DistillationCode1
AutoML: A Survey of the State-of-the-ArtCode1
BN-NAS: Neural Architecture Search with Batch NormalizationCode1
FlowNAS: Neural Architecture Search for Optical Flow EstimationCode1
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture SearchCode1
Deep Multimodal Neural Architecture SearchCode1
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture SearchCode1
Designing the Topology of Graph Neural Networks: A Novel Feature Fusion PerspectiveCode1
Automated Concatenation of Embeddings for Structured PredictionCode1
AdvRush: Searching for Adversarially Robust Neural ArchitecturesCode1
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network QuantizationCode1
Dataset Condensation with Distribution MatchingCode1
Generic Neural Architecture Search via RegressionCode1
Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image ClassificationCode1
Can GPT-4 Perform Neural Architecture Search?Code1
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D NetworksCode1
AutoGL: A Library for Automated Graph LearningCode1
AOWS: Adaptive and optimal network width search with latency constraintsCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Show:102550
← PrevPage 4 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