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
Neural Network Surgery: Combining Training with Topology Optimization—0
Neural Operator Search—0
TNASP: A Transformer-based NAS Predictor with a Self-evolution Framework—0
A systematic review of challenges and proposed solutions in modeling multimodal data—0
Asynchronous Evolution of Deep Neural Network Architectures—0
TND-NAS: Towards Non-differentiable Objectives in Progressive Differentiable NAS Framework—0
NeuRN: Neuro-inspired Domain Generalization for Image Classification—0
Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading—0
A Survey on Computationally Efficient Neural Architecture Search—0
NodeNAS: Node-Specific Graph Neural Architecture Search for Out-of-Distribution Generalization—0
A Survey on Optimal Transport for Machine Learning: Theory and Applications—0
Transfer-Once-For-All: AI Model Optimization for Edge—0
Not All Operations Contribute Equally: Hierarchical Operation-Adaptive Predictor for Neural Architecture Search—0
WeNet: Weighted Networks for Recurrent Network Architecture Search—0
A Survey on Neural Architecture Search Based on Reinforcement Learning—0
Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search—0
NSGA-Net: A Multi-Objective Genetic Algorithm for Neural Architecture Search—0
A Survey on Neural Architecture Search—0
What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning—0
OFA^2: A Multi-Objective Perspective for the Once-for-All Neural Architecture Search—0
A Survey on Multi-Objective Neural Architecture Search—0
A Survey on Evolutionary Neural Architecture Search—0
On Accelerating Edge AI: Optimizing Resource-Constrained Environments—0
On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis—0
A Survey on Dataset Distillation: Approaches, Applications and Future Directions—0
Once for All: Train One Network and Specialize it for Efficient Deployment—0
Once Quantized for All: Progressively Searching for Quantized Compact Models—0
A Survey of Techniques for Optimizing Transformer Inference—0
DARTS-PRIME: Regularization and Scheduling Improve Constrained Optimization in Differentiable NAS—0
ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecasting—0
A Surgery of the Neural Architecture Evaluators—0
Adaptive Neural Networks Using Residual Fitting—0
Towards Accurate and Robust Architectures via Neural Architecture Search—0
Towards a Robust Differentiable Architecture Search under Label Noise—0
One-Shot Neural Architecture Search with Network Similarity Directed Initialization for Pathological Image Classification—0
Evaluating Efficient Performance Estimators of Neural Architectures—0
A Study on the Intersection of GPU Utilization and CNN Inference—0
Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting—0
A Study of the Learning Progress in Neural Architecture Search Techniques—0
On Neural Architecture Search for Resource-Constrained Hardware Platforms—0
Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters—0
AdaPruner: Adaptive Channel Pruning and Effective Weights Inheritance—0
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction—0
On the Bounds of Function Approximations—0
On the Communication Complexity of Decentralized Bilevel Optimization—0
On the performance of deep learning for numerical optimization: an application to protein structure prediction—0
ASP: Automatic Selection of Proxy dataset for efficient AutoML—0
Automated Search-Space Generation Neural Architecture Search—0
On Weight-Sharing and Bilevel Optimization in Architecture Search—0
ASFD: Automatic and Scalable Face Detector—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