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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 1401–1450 of 1915 papers

TitleStatusHype
Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition—0
Joint Neural Architecture Search and Quantization—0
Katib: A Distributed General AutoML Platform on Kubernetes—0
Kernel-Level Energy-Efficient Neural Architecture Search for Tabular Dataset—0
Knowledge Distillation: A Survey—0
L^2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning—0
Landscape of Neural Architecture Search across sensors: how much do they differ ?—0
Large Scale Neural Architecture Search with Polyharmonic Splines—0
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization—0
Latency-Controlled Neural Architecture Search for Streaming Speech Recognition—0
LayerNAS: Neural Architecture Search in Polynomial Complexity—0
LC-NAS: Latency Constrained Neural Architecture Search for Point Cloud Networks—0
Learned Transferable Architectures Can Surpass Hand-Designed Architectures for Large Scale Speech Recognition—0
Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering—0
Learning Architectures from an Extended Search Space for Language Modeling—0
Learning by Grouping: A Multilevel Optimization Framework for Improving Fairness in Classification without Losing Accuracy—0
Learning by Passing Tests, with Application to Neural Architecture Search—0
Learning by Self-Explanation, with Application to Neural Architecture Search—0
Learning by Teaching, with Application to Neural Architecture Search—0
Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer—0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning—0
Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search—0
Learning Graph Representation of Person-specific Cognitive Processes from Audio-visual Behaviours for Automatic Personality Recognition—0
Learning Language-Specific Layers for Multilingual Machine Translation—0
Learning Latent Architectural Distribution in Differentiable Neural Architecture Search via Variational Information Maximization—0
Learning Morphisms with Gauss-Newton Approximation for Growing Networks—0
Learning Novel Transformer Architecture for Time-series Forecasting—0
Learning the Hypotheses Space from data: Learning Space and U-curve Property—0
Continual Learning via Learning a Continual Memory in Vision Transformer—0
Learning to Rank Learning Curves—0
Learn Layer-wise Connections in Graph Neural Networks—0
Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting—0
LeMo-NADe: Multi-Parameter Neural Architecture Discovery with LLMs—0
LENS: Layer Distribution Enabled Neural Architecture Search in Edge-Cloud Hierarchies—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
Lightweight Diffusion Models with Distillation-Based Block Neural Architecture Search—0
Lightweight Monocular Depth with a Novel Neural Architecture Search Method—0
LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search—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
Large Language Model Compression with Neural Architecture Search—0
LLM-Guided Evolution: An Autonomous Model Optimization for Object Detection—0
Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction—0
Local-to-Global Information Communication for Real-Time Semantic Segmentation Network Search—0
Low-Rank Adapters Meet Neural Architecture Search for LLM Compression—0
LPZero: Language Model Zero-cost Proxy Search from Zero—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