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 326–350 of 1915 papers

TitleStatusHype
Provably Convergent Federated Trilevel Learning—0
Weight-Entanglement Meets Gradient-Based Neural Architecture Search—0
OTOv3: Automatic Architecture-Agnostic Neural Network Training and Compression from Structured Pruning to Erasing OperatorsCode1
Heterogeneous Graph Neural Architecture Search with GPT-4Code0
XC-NAS: A New Cellular Encoding Approach for Neural Architecture Search of Multi-path Convolutional Neural Networks—0
Neural Architecture Codesign for Fast Bragg Peak Analysis—0
How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor—0
Combined Scheduling, Memory Allocation and Tensor Replacement for Minimizing Off-Chip Data Accesses of DNN Accelerators—0
TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly DetectionCode0
QuadraNet: Improving High-Order Neural Interaction Efficiency with Hardware-Aware Quadratic Neural Networks—0
Auto-CsiNet: Scenario-customized Automatic Neural Network Architecture Generation for Massive MIMO CSI Feedback—0
SiGeo: Sub-One-Shot NAS via Information Theory and Geometry of Loss Landscape—0
Masked Autoencoders Are Robust Neural Architecture Search Learners—0
On the Communication Complexity of Decentralized Bilevel Optimization—0
NAS-ASDet: An Adaptive Design Method for Surface Defect Detection Network using Neural Architecture Search—0
Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale—0
AutoML for Large Capacity Modeling of Meta's Ranking Systems—0
CycleGANAS: Differentiable Neural Architecture Search for CycleGANCode0
EPIM: Efficient Processing-In-Memory Accelerators based on Epitome—0
Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification—0
Lightweight Diffusion Models with Distillation-Based Block Neural Architecture Search—0
Auto deep learning for bioacoustic signalsCode0
Hardware Aware Evolutionary Neural Architecture Search using Representation Similarity Metric—0
3-Dimensional residual neural architecture search for ultrasonic defect detection—0
Farthest Greedy Path Sampling for Two-shot Recommender Search—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