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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 301–350 of 1915 papers

TitleStatusHype
DC-BENCH: Dataset Condensation BenchmarkCode1
GeNAS: Neural Architecture Search with Better GeneralizationCode1
DARTS: Differentiable Architecture SearchCode1
Adjoined Networks: A Training Paradigm with Applications to Network CompressionCode1
Meta-Learning of Neural Architectures for Few-Shot LearningCode1
Meta-prediction Model for Distillation-Aware NAS on Unseen DatasetsCode1
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-ResolutionCode1
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage ModelsCode1
Minimizing the Accumulated Trajectory Error to Improve Dataset DistillationCode1
MixPath: A Unified Approach for One-shot Neural Architecture SearchCode1
deepstruct -- linking deep learning and graph theoryCode1
BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image SegmentationCode1
Adversarial Branch Architecture Search for Unsupervised Domain AdaptationCode1
Block-Wisely Supervised Neural Architecture Search With Knowledge DistillationCode1
AutoSpeech: Neural Architecture Search for Speaker RecognitionCode1
DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter OptimizationCode1
BM-NAS: Bilevel Multimodal Neural Architecture SearchCode1
BN-NAS: Neural Architecture Search with Batch NormalizationCode1
CATE: Computation-aware Neural Architecture Encoding with TransformersCode1
Discretization-Aware Architecture SearchCode1
FR-NAS: Forward-and-Reverse Graph Predictor for Efficient Neural Architecture SearchCode1
Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture SearchCode1
Multi-objective Optimization by Learning Space PartitionsCode1
Multi-Prior Learning via Neural Architecture Search for Blind Face RestorationCode1
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture SearchCode1
Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed SpacesCode1
Differentiable Model Scaling using Differentiable TopkCode1
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS BenchmarksCode1
NAS-Bench-Graph: Benchmarking Graph Neural Architecture SearchCode1
Differentiable Neural Architecture Learning for Efficient Neural Network DesignCode1
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum SearchCode1
Differential Evolution for Neural Architecture SearchCode1
NAS-BNN: Neural Architecture Search for Binary Neural NetworksCode1
NAS-DIP: Learning Deep Image Prior with Neural Architecture SearchCode1
AIO-P: Expanding Neural Performance Predictors Beyond Image ClassificationCode1
Generalizable Lightweight Proxy for Robust NAS against Diverse PerturbationsCode1
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network QuantizationCode1
DrNAS: Dirichlet Neural Architecture SearchCode1
Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture SearchCode1
NAS-VAD: Neural Architecture Search for Voice Activity DetectionCode1
NAT: Neural Architecture Transformer for Accurate and Compact ArchitecturesCode1
NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and SizeCode1
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D NetworksCode1
AtomNAS: Fine-Grained End-to-End Neural Architecture SearchCode1
Can GPT-4 Perform Neural Architecture Search?Code1
DSNAS: Direct Neural Architecture Search without Parameter RetrainingCode1
Canvas: End-to-End Kernel Architecture Search in Neural NetworksCode1
Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance AssessmentCode1
Neural Architecture Search for Compressed Sensing Magnetic Resonance Image ReconstructionCode1
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image PriorCode1
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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