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 851–900 of 1915 papers

TitleStatusHype
BOMP-NAS: Bayesian Optimization Mixed Precision NAS—0
SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-Device Inference—0
GP-NAS-ensemble: a model for NAS Performance Prediction—0
Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration—0
Efficient Training Under Limited ResourcesCode0
Neural Architecture Search: Insights from 1000 PapersCode0
RNAS-CL: Robust Neural Architecture Search by Cross-Layer Knowledge Distillation—0
Accurate Detection of Paroxysmal Atrial Fibrillation with Certified-GAN and Neural Architecture Search—0
DQNAS: Neural Architecture Search using Reinforcement Learning—0
Efficient Evaluation Methods for Neural Architecture Search: A Survey—0
Adaptive Neural Networks Using Residual Fitting—0
Pruning Compact ConvNets for Efficient Inference—0
On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis—0
Unleashing the Power of Gradient Signal-to-Noise Ratio for Zero-Shot NASCode0
Continual Segment: Towards a Single, Unified and Non-forgetting Continual Segmentation Model of 143 Whole-body Organs in CT Scans—0
TripLe: Revisiting Pretrained Model Reuse and Progressive Learning for Efficient Vision Transformer Scaling and Searching—0
HOTNAS: Hierarchical Optimal Transport for Neural Architecture Search—0
Extensible and Efficient Proxy for Neural Architecture Search—0
EMT-NAS:Transferring Architectural Knowledge Between Tasks From Different DatasetsCode0
Pseudo-Inverted Bottleneck Convolution for DARTS Search SpaceCode0
Breaking the Architecture Barrier: A Method for Efficient Knowledge Transfer Across Networks—0
DAS: Neural Architecture Search via Distinguishing Activation Score—0
Automatic Network Adaptation for Ultra-Low Uniform-Precision Quantization—0
From Xception to NEXcepTion: New Design Decisions and Neural Architecture SearchCode0
Colab NAS: Obtaining lightweight task-specific convolutional neural networks following Occam's razorCode0
A Study on the Intersection of GPU Utilization and CNN Inference—0
Single Cell Training on Architecture Search for Image Denoising—0
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification—0
BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture SearchCode0
GENNAPE: Towards Generalized Neural Architecture Performance EstimatorsCode0
GraphPNAS: Learning Distribution of Good Neural Architectures via Deep Graph Generative Models—0
Entropy-Driven Mixed-Precision Quantization for Deep Network Design—0
PIDS: Joint Point Interaction-Dimension Search for 3D Point CloudCode0
Differentiable Meta Multigraph Search with Partial Message Propagation on Heterogeneous Information Networks—0
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach—0
α DARTS Once More: Enhancing Differentiable Architecture Search by Masked Image Modeling—0
HiveNAS: Neural Architecture Search using Artificial Bee Colony OptimizationCode0
Revisiting Training-free NAS Metrics: An Efficient Training-based MethodCode0
Development of a Neural Network-Based Mathematical Operation Protocol for Embedded Hexadecimal Digits Using Neural Architecture Search (NAS)—0
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural NetworksCode0
Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search—0
Multi-Objective Evolutionary for Object Detection Mobile Architectures Search—0
Deep neural network based on F-neurons and its learningCode0
Speeding up NAS with Adaptive Subset Selection—0
Bridge the Gap Between Architecture Spaces via A Cross-Domain PredictorCode0
Saliency-Aware Neural Architecture Search—0
Automated Dominative Subspace Mining for Efficient Neural Architecture SearchCode0
Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search—0
PredNAS: A Universal and Sample Efficient Neural Architecture Search Framework—0
NAS-PRNet: Neural Architecture Search generated Phase Retrieval Net for Off-axis Quantitative Phase Imaging—0
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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-SDARTS-RSAccuracy (Test)46.71—Unverified
4β-RDARTS-L2Accuracy (Test)46.71—Unverified
5NARAccuracy (Test)46.66—Unverified
6ASE-NAS+Accuracy (Val)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
7DARTS (first order)Top-1 Error Rate3—Unverified
8NN-MASS- CIFAR-ATop-1 Error Rate3—Unverified
9AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82—Unverified
10NASGEPTop-1 Error Rate2.82—Unverified