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 1601–1650 of 1915 papers

TitleStatusHype
The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments—0
To Share or Not To Share: A Comprehensive Appraisal of Weight-SharingCode0
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator—0
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
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
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
ISBNet: Instance-aware Selective Branching Networks—0
Neural Architecture Search in a Proxy Validation Loss Landscape—0
Modeling Neural Architecture Search Methods for Deep Networks—0
Scalable NAS with Factorizable Architectural Parameters—0
Searching for Stage-wise Neural Graphs In the Limit—0
Neural Architecture Search on Acoustic Scene Classification—0
NAS evaluation is frustratingly hardCode0
Computation Reallocation for Object Detection—0
BETANAS: BalancEd TrAining and selective drop for Neural Architecture Search—0
FasterSeg: Searching for Faster Real-time Semantic SegmentationCode0
TextNAS: A Neural Architecture Search Space tailored for Text Representation—0
Progressive DARTS: Bridging the Optimization Gap for NAS in the WildCode0
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
Efficient Differentiable Neural Architecture Search with Meta Kernels—0
SpineNet: Learning Scale-Permuted Backbone for Recognition and LocalizationCode0
Ultrafast Photorealistic Style Transfer via Neural Architecture Search—0
AdversarialNAS: Adversarial Neural Architecture Search for GANsCode0
EDAS: Efficient and Differentiable Architecture Search—0
DEGAS: Differentiable Efficient Generator Search—0
Neural Predictor for Neural Architecture SearchCode0
ExperienceThinking: Constrained Hyperparameter Optimization based on Knowledge and Pruning—0
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural NetworksCode0
Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection—0
MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification—0
DATA: Differentiable ArchiTecture ApproximationCode0
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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