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 1051–1100 of 1915 papers

TitleStatusHype
Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting—0
LeMo-NADe: Multi-Parameter Neural Architecture Discovery with LLMs—0
Superkernel Neural Architecture Search for Image Denoising—0
LENS: Layer Distribution Enabled Neural Architecture Search in Edge-Cloud Hierarchies—0
A Survey of Supernet Optimization and its Applications: Spatial and Temporal Optimization for Neural Architecture Search—0
Lessons from the Clustering Analysis of a Search Space: A Centroid-based Approach to Initializing NAS—0
LETI: Latency Estimation Tool and Investigation of Neural Networks inference on Mobile GPU—0
Leveraging End-to-End Speech Recognition with Neural Architecture Search—0
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds—0
Variation Matters: from Mitigating to Embracing Zero-Shot NAS Ranking Function Variation—0
Accelerating Neural Architecture Exploration Across Modalities Using Genetic Algorithms—0
BUSU-Net: An Ensemble U-Net Framework for Medical Image Segmentation—0
BS-NAS: Broadening-and-Shrinking One-Shot NAS with Searchable Numbers of Channels—0
Lightweight Diffusion Models with Distillation-Based Block Neural Architecture Search—0
BRP-NAS: Prediction-based NAS using GCNs—0
Lightweight Monocular Depth with a Novel Neural Architecture Search Method—0
Bringing AI To Edge: From Deep Learning's Perspective—0
SuperShaper: Task-Agnostic Super Pre-training of BERT Models with Variable Hidden Dimensions—0
LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search—0
Breaking the Architecture Barrier: A Method for Efficient Knowledge Transfer Across Networks—0
LLaMA-NAS: Efficient Neural Architecture Search for Large Language Models—0
Llama-Nemotron: Efficient Reasoning Models—0
LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search—0
Branched Multi-Task Networks: Deciding What Layers To Share—0
Large Language Model Compression with Neural Architecture Search—0
LLM-Guided Evolution: An Autonomous Model Optimization for Object Detection—0
SuperShaper: Task-Agnostic Super Pre-training of BERT Models with Variable Hidden Dimensions—0
Brain development dictates energy constraints on neural architecture search: cross-disciplinary insights on optimization strategies—0
Bractivate: Dendritic Branching in Segmentation Neural Architecture Search—0
Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction—0
Local-to-Global Information Communication for Real-Time Semantic Segmentation Network Search—0
A Design Space Study for LISTA and Beyond—0
Low-Rank Adapters Meet Neural Architecture Search for LLM Compression—0
LPZero: Language Model Zero-cost Proxy Search from Zero—0
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
VINNAS: Variational Inference-based Neural Network Architecture Search—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
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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