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
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking—0
Grassroots Operator Search for Model Edge Adaptation—0
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond—0
Dynamic Routing Networks—0
A Quantile-based Approach for Hyperparameter Transfer Learning—0
GreenFactory: Ensembling Zero-Cost Proxies to Estimate Performance of Neural Networks—0
DARTS for Inverse Problems: a Study on Stability—0
Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS—0
EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning—0
Intra-layer Neural Architecture Search—0
Efficient Visual Fault Detection for Freight Train via Neural Architecture Search with Data Volume Robustness—0
Guided Evolutionary Neural Architecture Search With Efficient Performance Estimation—0
Efficient Transfer Learning via Joint Adaptation of Network Architecture and Weight—0
Binarized Neural Architecture Search for Efficient Object Recognition—0
Efficient Traffic Classification using HW-NAS: Advanced Analysis and Optimization for Cybersecurity on Resource-Constrained Devices—0
Half Search Space is All You Need—0
A Study of the Learning Progress in Neural Architecture Search Techniques—0
LANA: Latency Aware Network Acceleration—0
HAO: Hardware-aware neural Architecture Optimization for Efficient Inference—0
Happy People -- Image Synthesis as Black-Box Optimization Problem in the Discrete Latent Space of Deep Generative Models—0
Interleaving Learning, with Application to Neural Architecture Search—0
Intriguing Properties of Adversarial Examples—0
ISBNet: Instance-aware Selective Branching Networks—0
Hardware-Aware Graph Neural Network Automated Design for Edge Computing Platforms—0
Efficient Search of Multiple Neural Architectures with Different Complexities via Importance Sampling—0
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor—0
Binarized Neural Architecture Search—0
Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography—0
Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation—0
Efficient Sampling for Predictor-Based Neural Architecture Search—0
ASP: Automatic Selection of Proxy dataset for efficient AutoML—0
HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images—0
A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS—0
Efficient Progressive Neural Architecture Search—0
Efficient OCT Image Segmentation Using Neural Architecture Search—0
Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search—0
Efficient Novelty-Driven Neural Architecture Search—0
Heed the Noise in Performance Evaluations in Neural Architecture Search—0
SpeedLimit: Neural Architecture Search for Quantized Transformer Models—0
ASFD: Automatic and Scalable Face Detector—0
Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification—0
Heterogeneous Learning Rate Scheduling for Neural Architecture Search on Long-Tailed Datasets—0
Heterogeneous Model Transfer between Different Neural Networks—0
HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devices—0
Inter-choice dependent super-network weights—0
Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection—0
Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection—0
Automatic Mixed-Precision Quantization Search of BERT—0
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances—0
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures—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