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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 601–650 of 1915 papers

TitleStatusHype
Improving Ranking Correlation of Supernet with Candidates Enhancement and Progressive TrainingCode0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
Improving the Efficient Neural Architecture Search via Rewarding ModificationsCode0
Improving Neural Architecture Search by Mixing a FireFly algorithm with a Training Free EvaluationCode0
ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation FusionCode0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
Improving the sample-efficiency of neural architecture search with reinforcement learningCode0
Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image ClassificationCode0
ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation LearningCode0
ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network ArchitecturesCode0
A Semi-Decoupled Approach to Fast and Optimal Hardware-Software Co-Design of Neural AcceleratorsCode0
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanismCode0
Implantable Adaptive Cells: differentiable architecture search to improve the performance of any trained U-shaped networkCode0
Homogeneous Architecture Augmentation for Neural PredictorCode0
Improved Differentiable Architecture Search for Language Modeling and Named Entity RecognitionCode0
Improve Ranking Correlation of Super-net through Training Scheme from One-shot NAS to Few-shot NASCode0
HiveNAS: Neural Architecture Search using Artificial Bee Colony OptimizationCode0
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass LensCode0
Inexact bilevel stochastic gradient methods for constrained and unconstrained lower-level problemsCode0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
Efficient Multiplayer Battle Game Optimizer for Adversarial Robust Neural Architecture SearchCode0
Benchmarking Deep Spiking Neural Networks on Neuromorphic HardwareCode0
BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture SearchCode0
Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT ClassificationCode0
Hierarchical Representations for Efficient Architecture SearchCode0
Insights from the Use of Previously Unseen Neural Architecture Search DatasetsCode0
How does topology of neural architectures impact gradient propagation and model performance?Code0
BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture SearchCode0
Efficient Incorporation of Multiple Latency Targets in the Once-For-All NetworkCode0
Behaviour DistillationCode0
Efficient hyperparameter optimization by way of PAC-Bayes bound minimizationCode0
Hardware Aware Neural Network Architectures using FbNetCode0
BLOX: Macro Neural Architecture Search Benchmark and AlgorithmsCode0
Efficient Global Neural Architecture SearchCode0
Hardware/Software Co-Exploration of Neural ArchitecturesCode0
Heterogeneous Graph Neural Architecture Search with GPT-4Code0
How to 0wn NAS in Your Spare TimeCode0
EmProx: Neural Network Performance Estimation For Neural Architecture SearchCode0
GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient NASCode0
MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained DevicesCode0
AGNAS: Attention-Guided Micro- and Macro-Architecture SearchCode0
Autoequivariant Network Search via Group DecompositionCode0
Bonsai-Net: One-Shot Neural Architecture Search via Differentiable PrunersCode0
Large Language Model Assisted Adversarial Robustness Neural Architecture SearchCode0
Encodings for Prediction-based Neural Architecture SearchCode0
Latency-Aware Differentiable Neural Architecture SearchCode0
Efficient Decoupled Neural Architecture Search by Structure and Operation SamplingCode0
GraphPAS: Parallel Architecture Search for Graph Neural NetworksCode0
DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution PruningCode0
Are Neural Architecture Search Benchmarks Well Designed? A Deeper Look Into Operation ImportanceCode0
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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