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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 1001–1050 of 1915 papers

TitleStatusHype
Cascaded Multi-task Adaptive Learning Based on Neural Architecture Search—0
Cartesian Genetic Programming Approach for Designing Convolutional Neural Networks—0
Building high accuracy emulators for scientific simulations with deep neural architecture search—0
Knowledge Distillation: A Survey—0
L^2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning—0
Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models—0
Landscape of Neural Architecture Search across sensors: how much do they differ ?—0
Stochastic analysis of heterogeneous porous material with modified neural architecture search (NAS) based physics-informed neural networks using transfer learning—0
Straight Through Gumbel Softmax Estimator based Bimodal Neural Architecture Search for Audio-Visual Deepfake Detection—0
Large Scale Neural Architecture Search with Polyharmonic Splines—0
StressNAS: Affect State and Stress Detection Using Neural Architecture Search—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
Structural Analysis of Sparse Neural Networks—0
3-Dimensional residual neural architecture search for ultrasonic defect detection—0
UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation—0
Adversarially Robust Neural Architecture Search for Graph Neural 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
VAENAS: Sampling Matters in Neural Architecture Search—0
Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer—0
Carbon Emissions and Large Neural Network Training—0
Carbon-Efficient Neural Architecture Search—0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning—0
Structure of Artificial Neural Networks -- Empirical Investigations—0
Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search—0
StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks—0
Learning Graph Representation of Person-specific Cognitive Processes from Audio-visual Behaviours for Automatic Personality Recognition—0
C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation—0
Subnet-Aware Dynamic Supernet Training for Neural Architecture Search—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
Can weight sharing outperform random architecture search? An investigation with TuNAS—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
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search—0
SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-Device Inference—0
Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models?—0
Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS—0
Learn Layer-wise Connections in Graph Neural Networks—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-SDARTS-RSAccuracy (Test)46.71—Unverified
4β-RDARTS-L2Accuracy (Test)46.71—Unverified
5NARAccuracy (Test)46.66—Unverified
6ASE-NAS+Accuracy (Val)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
7DARTS (first order)Top-1 Error Rate3—Unverified
8NN-MASS- CIFAR-ATop-1 Error Rate3—Unverified
9AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82—Unverified
10NASGEPTop-1 Error Rate2.82—Unverified