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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 901–950 of 1915 papers

TitleStatusHype
BINAS: Bilinear Interpretable Neural Architecture SearchCode0
Towards a Robust Differentiable Architecture Search under Label Noise—0
Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image ClassificationCode1
ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies—0
DARTS for Inverse Problems: a Study on Stability—0
NAS-HPO-Bench-II: A Benchmark Dataset on Joint Optimization of Convolutional Neural Network Architecture and Training HyperparametersCode1
Growing Representation Learning—0
NeuralArTS: Structuring Neural Architecture Search with Type Theory—0
GradSign: Model Performance Inference with Theoretical InsightsCode0
DPNAS: Neural Architecture Search for Deep Learning with Differential PrivacyCode0
CONetV2: Efficient Auto-Channel Size Optimization for CNNsCode0
Improving the sample-efficiency of neural architecture search with reinforcement learningCode0
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse TasksCode1
On the Security Risks of AutoMLCode0
Across-Task Neural Architecture Search via Meta Learning—0
Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo—0
ZARTS: On Zero-order Optimization for Neural Architecture Search—0
SuperShaper: Task-Agnostic Super Pre-training of BERT Models with Variable Hidden Dimensions—0
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking—0
Accelerating Multi-Objective Neural Architecture Search by Random-Weight Evaluation—0
Dataset Condensation with Distribution MatchingCode1
Conceptual Expansion Neural Architecture Search (CENAS)—0
Multi-objective Optimization by Learning Space PartitionsCode1
Max and Coincidence Neurons in Neural Networks—0
An Analysis of Super-Net Heuristics in Weight-Sharing NAS—0
Inexact bilevel stochastic gradient methods for constrained and unconstrained lower-level problemsCode0
DAAS: Differentiable Architecture and Augmentation Policy Search—0
What to expect of hardware metric predictors in NAS—0
Ranking Convolutional Architectures by their Feature Extraction Capabilities—0
Personalized Neural Architecture Search for Federated Learning—0
Tr-NAS: Memory-Efficient Neural Architecture Search with Transferred Blocks—0
NASPY: Automated Extraction of Automated Machine Learning Models—0
NAS-Bench-Zero: A Large Scale Dataset for Understanding Zero-Shot Neural Architecture Search—0
Multi-objective optimization for Hardware-aware Neural Architecture Search—0
A Novel Watermarking Framework for Ownership Verification of DNN Architectures—0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning—0
ZenDet: Revisiting Efficient Object Detection Backbones from Zero-Shot Neural Architecture Search—0
ConCoDE: Hard-constrained Differentiable Co-Exploration Method for Neural Architectures and Hardware Accelerators—0
FedNAS: Federated Deep Learning via Neural Architecture Search—0
A Transferable General-Purpose Predictor for Neural Architecture Search—0
Multi-objective Optimization by Learning Space Partition—0
Neural Architecture Search via Ensemble-based Knowledge Distillation—0
Hardware-Aware Network Transformation—0
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—0
Picking up the pieces: separately evaluating supernet training and architecture selection—0
AutoCoG: A Unified Data-Modal Co-Search Framework for Graph Neural Networks—0
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search—0
NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet TrainingCode1
L^2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning—0
Profiling Neural Blocks and Design Spaces for Mobile Neural Architecture SearchCode0
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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