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 1551–1600 of 1915 papers

TitleStatusHype
Knapsack Pruning with Inner DistillationCode1
Neural Architecture Search For Fault Diagnosis—0
How to 0wn NAS in Your Spare TimeCode0
Federated Neural Architecture Search—0
The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments—0
Training Large Neural Networks with Constant Memory using a New Execution AlgorithmCode1
Stabilizing Differentiable Architecture Search via Perturbation-based RegularizationCode1
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator—0
To Share or Not To Share: A Comprehensive Appraisal of Weight-SharingCode0
Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks—0
Variational Depth Search in ResNetsCode0
NASS: Optimizing Secure Inference via Neural Architecture Search—0
Bayesian Neural Architecture Search using A Training-Free Performance MetricCode1
NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture SearchCode1
Learning the Hypotheses Space from data: Learning Space and U-curve Property—0
Multi-objective Neural Architecture Search via Non-stationary Policy Gradient—0
BNAS:An Efficient Neural Architecture Search Approach Using Broad Scalable Architecture—0
Evolutionary Neural Architecture Search for Retinal Vessel Segmentation—0
Building high accuracy emulators for scientific simulations with deep neural architecture search—0
Latency-Aware Differentiable Neural Architecture SearchCode0
MixPath: A Unified Approach for One-shot Neural Architecture SearchCode1
Neural Architecture Search for Deep Image PriorCode0
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture SearchCode0
Performance-Oriented Neural Architecture Search—0
Fast Neural Network Adaptation via Parameter Remapping and Architecture SearchCode0
Deeper Insights into Weight Sharing in Neural Architecture SearchCode0
EcoNAS: Finding Proxies for Economical Neural Architecture Search—0
NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture SearchCode1
Neural Architecture Search in a Proxy Validation Loss Landscape—0
ISBNet: Instance-aware Selective Branching Networks—0
Scalable NAS with Factorizable Architectural Parameters—0
Modeling Neural Architecture Search Methods for Deep Networks—0
Searching for Stage-wise Neural Graphs In the Limit—0
Neural Architecture Search on Acoustic Scene Classification—0
NAS evaluation is frustratingly hardCode0
BETANAS: BalancEd TrAining and selective drop for Neural Architecture Search—0
Computation Reallocation for Object Detection—0
Progressive DARTS: Bridging the Optimization Gap for NAS in the WildCode0
TextNAS: A Neural Architecture Search Space tailored for Text Representation—0
FasterSeg: Searching for Faster Real-time Semantic SegmentationCode0
AtomNAS: Fine-Grained End-to-End Neural Architecture SearchCode1
C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation—0
Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud—0
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training DataCode0
UNAS: Differentiable Architecture Search Meets Reinforcement LearningCode0
PreVIous: A Methodology for Prediction of Visual Inference Performance on IoT DevicesCode0
Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS—0
Leveraging End-to-End Speech Recognition with Neural Architecture Search—0
A Variational-Sequential Graph Autoencoder for Neural Architecture Performance PredictionCode0
SpineNet: Learning Scale-Permuted Backbone for Recognition and LocalizationCode0
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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