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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1251–1300 of 1915 papers

TitleStatusHype
TAPAS: Train-less Accuracy Predictor for Architecture Search—0
Task-Agnostic and Adaptive-Size BERT Compression—0
Teacher Guided Architecture Search—0
TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks—0
Topology-aware Tensor Decomposition for Meta-graph Learning—0
TensorSocket: Shared Data Loading for Deep Learning Training—0
Tetra-AML: Automatic Machine Learning via Tensor Networks—0
TextNAS: A Neural Architecture Search Space tailored for Text Representation—0
TG-NAS: Generalizable Zero-Cost Proxies with Operator Description Embedding and Graph Learning for Efficient Neural Architecture Search—0
Neural Architectural Backdoors—0
The devil is in discretization discrepancy. Robustifying Differentiable NAS with Single-Stage Searching Protocol—0
The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devices—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
The OCON model: an old but green solution for distributable supervised classification for acoustic monitoring in smart cities—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
The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments—0
Tiered Pruning for Efficient Differentialble Inference-Aware Neural Architecture Search—0
Tiny Adversarial Mulit-Objective Oneshot Neural Architecture Search—0
TNASP: A Transformer-based NAS Predictor with a Self-evolution Framework—0
TND-NAS: Towards Non-differentiable Objectives in Progressive Differentiable NAS Framework—0
Transfer-Once-For-All: AI Model Optimization for Edge—0
Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search—0
Towards Accurate and Robust Architectures via Neural Architecture Search—0
Towards a Robust Differentiable Architecture Search under Label Noise—0
Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters—0
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction—0
Automated Search-Space Generation Neural Architecture Search—0
Towards Bi-directional Skip Connections in Encoder-Decoder Architectures and Beyond—0
Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation—0
Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search—0
Towards Interpretable Physical-Conceptual Catchment-Scale Hydrological Modeling using the Mass-Conserving-Perceptron—0
CiMNet: Towards Joint Optimization for DNN Architecture and Configuration for Compute-In-Memory Hardware—0
Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification—0
Towards Neural Architecture Search for Transfer Learning in 6G Networks—0
Towards One Shot Search Space Poisoning in Neural Architecture Search—0
Towards Optimal Compression: Joint Pruning and Quantization—0
Towards Oracle Knowledge Distillation with Neural Architecture Search—0
Towards Privacy-Preserving Neural Architecture Search—0
Towards Regression-Free Neural Networks for Diverse Compute Platforms—0
Towards Robust Out-of-Distribution Generalization: Data Augmentation and Neural Architecture Search Approaches—0
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search—0
TRACE: Tensorizing and Generalizing Supernets from Neural Architecture Search—0
Training BatchNorm Only in Neural Architecture Search and Beyond—0
Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights—0
Trainless Model Performance Estimation for Neural Architecture Search—0
TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction—0
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning—0
Transfer Learning based Search Space Design for Hyperparameter Tuning—0
Show:102550
← PrevPage 26 of 39Next →
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