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 1351–1400 of 1915 papers

TitleStatusHype
Zen-NAS: A Zero-Shot NAS for High-Performance Image Recognition—0
ZeroLM: Data-Free Transformer Architecture Search for Language Models—0
Zero-Shot NAS via the Suppression of Local Entropy Decrease—0
ZiCo-BC: A Bias Corrected Zero-Shot NAS for Vision Tasks—0
Relational Graph Neural Network Design via Progressive Neural Architecture Search—0
HOTNAS: Hierarchical Optimal Transport for Neural Architecture Search—0
How Does Supernet Help in Neural Architecture Search?—0
How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor—0
How Powerful are Performance Predictors in Neural Architecture Search?—0
Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search—0
How to Train Your Super-Net: An Analysis of Training Heuristics in Weight-Sharing NAS—0
HQNAS: Auto CNN deployment framework for joint quantization and architecture search—0
HSCoNAS: Hardware-Software Co-Design of Efficient DNNs via Neural Architecture Search—0
HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark—0
HW-SW Optimization of DNNs for Privacy-preserving People Counting on Low-resolution Infrared Arrays—0
Hybrid Composition with IdleBlock: More Efficient Networks for Image Recognition—0
Hybrid-Quantum Neural Architecture Search for The Proximal Policy Optimization Algorithm—0
Hyperparameter Optimization in Machine Learning—0
Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery—0
HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet—0
HyT-NAS: Hybrid Transformers Neural Architecture Search for Edge Devices—0
iDARTS: Improving DARTS by Node Normalization and Decorrelation Discretization—0
Identifying phase transitions in physical systems with neural networks: a neural architecture search perspective—0
iHAS: Instance-wise Hierarchical Architecture Search for Deep Learning Recommendation Models—0
ILASH: A Predictive Neural Architecture Search Framework for Multi-Task Applications—0
IMB-NAS: Neural Architecture Search for Imbalanced Datasets—0
ImmuNeCS: Neural Committee Search by an Artificial Immune System—0
Improved Conformer-based End-to-End Speech Recognition Using Neural Architecture Search—0
Improving Differentiable Architecture Search with a Generative Model—0
Improving Differentiable Architecture Search via Self-Distillation—0
Improving One-shot NAS by Suppressing the Posterior Fading—0
Improving Routability Prediction via NAS Using a Smooth One-shot Augmented Predictor—0
Improving the Energy Efficiency and Robustness of tinyML Computer Vision using Log-Gradient Input Images—0
Improving Zero-Shot Neural Architecture Search with Parameters Scoring—0
iNAS: Integral NAS for Device-Aware Salient Object Detection—0
Incremental Learning with Differentiable Architecture and Forgetting Search—0
Inductive Transfer for Neural Architecture Optimization—0
Inference Latency Prediction at the Edge—0
InstaTune: Instantaneous Neural Architecture Search During Fine-Tuning—0
Instructing the Architecture Search for Spatial-temporal Sequence Forecasting with LLM—0
Inter-choice dependent super-network weights—0
InterGridNet: An Electric Network Frequency Approach for Audio Source Location Classification Using Convolutional Neural Networks—0
Interleaving Learning, with Application to Neural Architecture Search—0
Intra-layer Neural Architecture Search—0
Intriguing Properties of Adversarial Examples—0
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond—0
Dynamic Routing Networks—0
ISBNet: Instance-aware Selective Branching Networks—0
JMSNAS: Joint Model Split and Neural Architecture Search for Learning over Mobile Edge Networks—0
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation—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-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