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

Showing 1301–1350 of 1915 papers

TitleStatusHype
Weight-Entanglement Meets Gradient-Based Neural Architecture Search—0
AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models—0
Neural Architecture Search for Natural Language Understanding—0
Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models—0
The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devices—0
Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks—0
Neural Architecture Search for Speech Emotion Recognition—0
Auto-CsiNet: Scenario-customized Automatic Neural Network Architecture Generation for Massive MIMO CSI Feedback—0
The M-factor: A Novel Metric for Evaluating Neural Architecture Search in Resource-Constrained Environments—0
The Nonlinearity Coefficient -- A Practical Guide to Neural Architecture Design—0
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap—0
Neural Architecture Search in a Proxy Validation Loss Landscape—0
Neural Architecture Search in Embedding Space—0
Neural Architecture Searching for Facial Attributes-based Depression Recognition—0
AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping Reinforcement—0
Neural Architecture Search in operational context: a remote sensing case-study—0
The OCON model: an old but green solution for distributable supervised classification for acoustic monitoring in smart cities—0
AutoCoG: A Unified Data-Modal Co-Search Framework for Graph Neural Networks—0
Neural Architecture Search of Hybrid Models for NPU-CIM Heterogeneous AR/VR Devices—0
Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-time Mobile Telepresence—0
Neural Architecture Search on Acoustic Scene Classification—0
Neural Architecture Search on Efficient Transformers and Beyond—0
AutoCaption: Image Captioning with Neural Architecture Search—0
Neural Architecture Search Over a Graph Search Space—0
Federated Neural Architecture Search—0
AutoBERT-Zero: Evolving BERT Backbone from Scratch—0
Neural Architecture Search using Covariance Matrix Adaptation Evolution Strategy—0
Neural Architecture Search using Particle Swarm and Ant Colony Optimization—0
The UniNAS framework: combining modules in arbitrarily complex configurations with argument trees—0
The Untapped Potential of Off-the-Shelf Convolutional Neural Networks—0
Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search—0
Neural Architecture Search via Combinatorial Multi-Armed Bandit—0
Neural Architecture Search via Ensemble-based Knowledge Distillation—0
The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments—0
Neural Architecture Search with an Efficient Multiobjective Evolutionary Framework—0
Tiered Pruning for Efficient Differentialble Inference-Aware Neural Architecture Search—0
AutoADR: Automatic Model Design for Ad Relevance—0
Neural Architecture Search with Multimodal Fusion Methods for Diagnosing Dementia—0
AutoAdapt: Automated Segmentation Network Search for Unsupervised Domain Adaptation—0
A Unified Deep Framework for Joint 3D Pose Estimation and Action Recognition from a Single RGB Camera—0
Hyperparameter optimization with REINFORCE and Transformers—0
Tiny Adversarial Mulit-Objective Oneshot Neural Architecture Search—0
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML—0
Neural Architecture Transfer 2: A Paradigm for Improving Efficiency in Multi-Objective Neural Architecture Search—0
NeuralArTS: Structuring Neural Architecture Search with Type Theory—0
Neural Attention Search—0
AttentionSmithy: A Modular Framework for Rapid Transformer Development and Customization—0
A Transferable General-Purpose Predictor for Neural Architecture Search—0
Neural Inheritance Relation Guided One-Shot Layer Assignment Search—0
Adaptive quantization with mixed-precision based on low-cost proxy—0
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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
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
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