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

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Showing 251–300 of 1915 papers

TitleStatusHype
Differential Evolution for Neural Architecture SearchCode1
Revisiting Parameter Sharing for Automatic Neural Channel Number SearchCode1
CLEARER: Multi-Scale Neural Architecture Search for Image RestorationCode1
Evolving Search Space for Neural Architecture SearchCode1
AttentiveNAS: Improving Neural Architecture Search via Attentive SamplingCode1
EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation using Accelerated Neuroevolution with Weight TransferCode1
Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through GradientsCode1
Adaptive Linear Span Network for Object Skeleton DetectionCode1
VEGA: Towards an End-to-End Configurable AutoML PipelineCode1
μNAS: Constrained Neural Architecture Search for MicrocontrollersCode1
Neural Architecture Search of SPD Manifold NetworksCode1
Hierarchical Neural Architecture Search for Deep Stereo MatchingCode1
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity MicrocontrollersCode1
Optimal Subarchitecture Extraction For BERTCode1
ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse CodingCode1
Automated Concatenation of Embeddings for Structured PredictionCode1
Smooth Variational Graph Embeddings for Efficient Neural Architecture SearchCode1
Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture SearchCode1
DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural NetworksCode1
RelativeNAS: Relative Neural Architecture Search via Slow-Fast LearningCode1
S3NAS: Fast NPU-aware Neural Architecture Search MethodologyCode1
DARTS-: Robustly Stepping out of Performance Collapse Without IndicatorsCode1
Understanding the wiring evolution in differentiable neural architecture searchCode1
NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and SizeCode1
NAS-DIP: Learning Deep Image Prior with Neural Architecture SearchCode1
Simplifying Architecture Search for Graph Neural NetworkCode1
Automated Search for Resource-Efficient Branched Multi-Task NetworksCode1
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS BenchmarksCode1
Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture RecognitionCode1
TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture SearchCode1
Shape Adaptor: A Learnable Resizing ModuleCode1
Neural Architecture Search in Graph Neural NetworksCode1
Representation Sharing for Fast Object Detector Search and BeyondCode1
NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture SearchCode1
MCUNet: Tiny Deep Learning on IoT DevicesCode1
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture SearchCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
Accuracy Prediction with Non-neural Model for Neural Architecture SearchCode1
A Study on Encodings for Neural Architecture SearchCode1
Journey Towards Tiny Perceptual Super-ResolutionCode1
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum SearchCode1
Discretization-Aware Architecture SearchCode1
GOLD-NAS: Gradual, One-Level, DifferentiableCode1
Rethinking Bottleneck Structure for Efficient Mobile Network DesignCode1
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree SearchCode1
Neural Architecture Design for GPU-Efficient NetworksCode1
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture SearchCode1
Cyclic Differentiable Architecture SearchCode1
DrNAS: Dirichlet Neural Architecture SearchCode1
Neural Ensemble Search for Uncertainty Estimation and Dataset ShiftCode1
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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