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 501–550 of 1915 papers

TitleStatusHype
Differentiable architecture search with multi-dimensional attention for spiking neural networks—0
Syno: Structured Synthesis for Neural Operators—0
Hyperparameter Optimization in Machine Learning—0
Yoga Pose Classification Using Transfer Learning—0
Developing Convolutional Neural Networks using a Novel Lamarckian Co-Evolutionary Algorithm—0
Towards Robust Out-of-Distribution Generalization: Data Augmentation and Neural Architecture Search Approaches—0
Spatial-Temporal Search for Spiking Neural Networks—0
MCUBERT: Memory-Efficient BERT Inference on Commodity Microcontrollers—0
Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading—0
Structure of Artificial Neural Networks -- Empirical Investigations—0
Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques—0
On the Adversarial Transferability of Generalized "Skip Connections"Code0
ActNAS : Generating Efficient YOLO Models using Activation NAS—0
DistDD: Distributed Data Distillation Aggregation through Gradient Matching—0
Neural Architecture Search of Hybrid Models for NPU-CIM Heterogeneous AR/VR Devices—0
Simultaneous Weight and Architecture Optimization for Neural NetworksCode0
Large Language Model Compression with Neural Architecture Search—0
Automating Data Science Pipelines with Tensor CompletionCode0
Designing a Classifier for Active Fire Detection from Multispectral Satellite Imagery Using Neural Architecture Search—0
MC-QDSNN: Quantized Deep evolutionary SNN with Multi-Dendritic Compartment Neurons for Stress Detection using Physiological Signals—0
LPZero: Language Model Zero-cost Proxy Search from Zero—0
Double Oracle Neural Architecture Search for Game Theoretic Deep Learning Models—0
The OCON model: an old but green solution for distributable supervised classification for acoustic monitoring in smart cities—0
The OCON model: an old but gold solution for distributable supervised classificationCode0
Resource-aware Mixed-precision Quantization for Enhancing Deployability of Transformers for Time-series Forecasting on Embedded FPGAs—0
Scalable Reinforcement Learning-based Neural Architecture Search—0
Lightweight Neural Architecture Search for Cerebral Palsy DetectionCode0
Cartesian Genetic Programming Approach for Designing Convolutional Neural Networks—0
POMONAG: Pareto-Optimal Many-Objective Neural Architecture Generator—0
RNC: Efficient RRAM-aware NAS and Compilation for DNNs on Resource-Constrained Edge DevicesCode0
TensorSocket: Shared Data Loading for Deep Learning Training—0
Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design—0
Flexiffusion: Segment-wise Neural Architecture Search for Flexible Denoising Schedule—0
A Survey on Neural Architecture Search Based on Reinforcement Learning—0
AutoSTF: Decoupled Neural Architecture Search for Cost-Effective Automated Spatio-Temporal Forecasting—0
EM-DARTS: Hierarchical Differentiable Architecture Search for Eye Movement Recognition—0
Investigating the Impact of Hard Samples on Accuracy Reveals In-class Data ImbalanceCode0
OStr-DARTS: Differentiable Neural Architecture Search based on Operation StrengthCode0
Utilizing Data Fingerprints for Privacy-Preserving Algorithm Selection in Time Series Classification: Performance and Uncertainty Estimation on Unseen DatasetsCode0
Pushing Joint Image Denoising and Classification to the Edge—0
Enhancing Convolutional Neural Networks with Higher-Order Numerical Difference Methods—0
MoistNet: Machine Vision-based Deep Learning Models for Wood Chip Moisture Content Measurement—0
NASH: Neural Architecture and Accelerator Search for Multiplication-Reduced Hybrid ModelsCode0
Optimization and Deployment of Deep Neural Networks for PPG-based Blood Pressure Estimation Targeting Low-power Wearables—0
FastBO: Fast HPO and NAS with Adaptive Fidelity Identification—0
PSE-Net: Channel Pruning for Convolutional Neural Networks with Parallel-subnets Estimator—0
SCAN-Edge: Finding MobileNet-speed Hybrid Networks for Diverse Edge Devices via Hardware-Aware Evolutionary Search—0
MONAS: Efficient Zero-Shot Neural Architecture Search for MCUs—0
HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devices—0
A Web-Based Solution for Federated Learning with LLM-Based Automation—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