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 1451–1500 of 1915 papers

TitleStatusHype
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers—0
Machine Learning based Anomaly Detection for 5G Networks—0
MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for Heterogeneous MPSoC Deployment—0
Making Differentiable Architecture Search less local—0
MANAS: Multi-Agent Neural Architecture Search—0
MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising—0
MAPLE-Edge: A Runtime Latency Predictor for Edge Devices—0
MAPLE: Microprocessor A Priori for Latency Estimation—0
MAPLE-X: Latency Prediction with Explicit Microprocessor Prior Knowledge—0
MARCO: Hardware-Aware Neural Architecture Search for Edge Devices with Multi-Agent Reinforcement Learning and Conformal Prediction Filtering—0
MaskConnect: Connectivity Learning by Gradient Descent—0
Masked Autoencoders Are Robust Neural Architecture Search Learners—0
Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques—0
Max and Coincidence Neurons in Neural Networks—0
Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition—0
MC-QDSNN: Quantized Deep evolutionary SNN with Multi-Dendritic Compartment Neurons for Stress Detection using Physiological Signals—0
MCUBERT: Memory-Efficient BERT Inference on Commodity Microcontrollers—0
MCU-MixQ: A HW/SW Co-optimized Mixed-precision Neural Network Design Framework for MCUs—0
Homogeneous Architecture Augmentation for Neural PredictorCode0
Dataset Condensation with Differentiable Siamese AugmentationCode0
Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource UtilizationCode0
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass LensCode0
Toward Synergism in Macro Action EnsemblesCode0
How does topology of neural architectures impact gradient propagation and model performance?Code0
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture SearchCode0
DATA: Differentiable ArchiTecture ApproximationCode0
How to 0wn NAS in Your Spare TimeCode0
How to 0wn the NAS in Your Spare TimeCode0
SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture SearchCode0
Data Aware Neural Architecture SearchCode0
Noisy Heuristics NAS: A Network Morphism based Neural Architecture Search using HeuristicsCode0
HiveNAS: Neural Architecture Search using Artificial Bee Colony OptimizationCode0
Architecture-Aware Minimization (A^2M): How to Find Flat Minima in Neural Architecture SearchCode0
Hierarchical Representations for Efficient Architecture SearchCode0
Novelty Driven Evolutionary Neural Architecture SearchCode0
Heterogeneous Graph Neural Architecture Search with GPT-4Code0
DartsReNet: Exploring new RNN cells in ReNet architecturesCode0
NSGA-Net: Neural Architecture Search using Multi-Objective Genetic AlgorithmCode0
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanismCode0
Traditional and accelerated gradient descent for neural architecture searchCode0
DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator SearchCode0
ObfuNAS: A Neural Architecture Search-based DNN Obfuscation ApproachCode0
Hardware/Software Co-Exploration of Neural ArchitecturesCode0
Hardware Aware Neural Network Architectures using FbNetCode0
Tackling Neural Architecture Search With Quality Diversity OptimizationCode0
CycleGANAS: Differentiable Neural Architecture Search for CycleGANCode0
Guided Evolution for Neural Architecture SearchCode0
Customized Subgraph Selection and Encoding for Drug-drug Interaction PredictionCode0
Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image ClassificationCode0
Automated Machine Learning: From Principles to PracticesCode0
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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