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 951–1000 of 1915 papers

TitleStatusHype
Tackling Neural Architecture Search With Quality Diversity OptimizationCode0
Evaluating the Practicality of Learned Image Compression—0
Neural Architecture Search on Efficient Transformers and Beyond—0
Guided Evolutionary Neural Architecture Search With Efficient Performance Estimation—0
Efficient Search of Multiple Neural Architectures with Different Complexities via Importance Sampling—0
EASNet: Searching Elastic and Accurate Network Architecture for Stereo MatchingCode0
Improving Neural Architecture Search by Mixing a FireFly algorithm with a Training Free EvaluationCode0
CLOSE: Curriculum Learning On the Sharing Extent Towards Better One-shot NAS—0
PASHA: Efficient HPO and NAS with Progressive Resource AllocationCode0
MRF-UNets: Searching UNet with Markov Random FieldsCode0
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures—0
Long-term Reproducibility for Neural Architecture SearchCode0
Noisy Heuristics NAS: A Network Morphism based Neural Architecture Search using HeuristicsCode0
Betty: An Automatic Differentiation Library for Multilevel Optimization—0
Architecture Augmentation for Performance Predictor Based on Graph Isomorphism—0
Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spacesCode0
Multi-scale Attentive Image De-raining Networks via Neural Architecture SearchCode0
Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization—0
FEATHERS: Federated Architecture and Hyperparameter Search—0
Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection—0
FedorAS: Federated Architecture Search under system heterogeneity—0
Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy GuaranteeCode0
DFG-NAS: Deep and Flexible Graph Neural Architecture SearchCode0
Channel-wise Mixed-precision Assignment for DNN Inference on Constrained Edge NodesCode0
Neural Architecture Adaptation for Object Detection by Searching Channel Dimensions and Mapping Pre-trained Parameters—0
ERNAS: An Evolutionary Neural Architecture Search for Magnetic Resonance Image Reconstructions—0
Improve Ranking Correlation of Super-net through Training Scheme from One-shot NAS to Few-shot NASCode0
EmProx: Neural Network Performance Estimation For Neural Architecture SearchCode0
Exploring the Intersection between Neural Architecture Search and Continual Learning—0
SDQ: Stochastic Differentiable Quantization with Mixed Precision—0
Towards Self-supervised and Weight-preserving Neural Architecture SearchCode0
RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation—0
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning—0
Transfer Learning based Search Space Design for Hyperparameter Tuning—0
AGNAS: Attention-Guided Micro- and Macro-Architecture SearchCode0
Search Space Adaptation for Differentiable Neural Architecture Search in Image Classification—0
Supernet Training for Federated Image Classification under System HeterogeneityCode0
A Survey on Computationally Efficient Neural Architecture Search—0
Multi-Complexity-Loss DNAS for Energy-Efficient and Memory-Constrained Deep Neural NetworksCode0
Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning—0
Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture—0
MAPLE-X: Latency Prediction with Explicit Microprocessor Prior Knowledge—0
Concurrent Neural Tree and Data Preprocessing AutoML for Image Classification—0
FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid?—0
Incremental Learning with Differentiable Architecture and Forgetting Search—0
A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities—0
AutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System—0
A Classification of G-invariant Shallow Neural NetworksCode0
Heat Source Layout Optimization Using Automatic Deep Learning Surrogate and Multimodal Neighborhood Search Algorithm—0
Proxyless Neural Architecture Adaptation for Supervised Learning and Self-Supervised Learning—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