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 1101–1150 of 1915 papers

TitleStatusHype
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
Boosting Share Routing for Multi-task Learning—0
Boosting Network: Learn by Growing Filters and Layers via SplitLBI—0
μDARTS: Model Uncertainty-Aware Differentiable Architecture Search—0
Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation—0
MemNet: Memory-Efficiency Guided Neural Architecture Search with Augment-Trim learning—0
BOMP-NAS: Bayesian Optimization Mixed Precision NAS—0
Memory-efficient Patch-based Inference for Tiny Deep Learning—0
MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification—0
Meta knowledge assisted Evolutionary Neural Architecture Search—0
Meta-Learning of NAS for Few-shot Learning in Medical Image Applications—0
Binarized Neural Architecture Search for Efficient Object Recognition—0
Binarized Neural Architecture Search—0
MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN—0
MFAS: Multimodal Fusion Architecture Search—0
Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable Blocks for Image Classification—0
M-FasterSeg: An Efficient Semantic Segmentation Network Based on Neural Architecture Search—0
MGAS: Multi-Granularity Architecture Search for Trade-Off Between Model Effectiveness and Efficiency—0
MicroNAS: An Automated Framework for Developing a Fall Detection System—0
MicroNAS: Memory and Latency Constrained Hardware-Aware Neural Architecture Search for Time Series Classification on Microcontrollers—0
MicroNAS: Zero-Shot Neural Architecture Search for MCUs—0
SpeedLimit: Neural Architecture Search for Quantized Transformer Models—0
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures—0
Betty: An Automatic Differentiation Library for Multilevel Optimization—0
Mixed-Block Neural Architecture Search for Medical Image Segmentation—0
Mixed Precision Low-bit Quantization of Neural Network Language Models for Speech Recognition—0
Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search—0
Mixed-precision Supernet Training from Vision Foundation Models using Low Rank Adapter—0
BETANAS: BalancEd TrAining and selective drop for Neural Architecture Search—0
Accelerating Multi-Objective Neural Architecture Search by Random-Weight Evaluation—0
Best Practices for Scientific Research on Neural Architecture Search—0
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator—0
SUTD-PRCM Dataset and Neural Architecture Search Approach for Complex Metasurface Design—0
Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation—0
MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization—0
BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems—0
Modeling Neural Architecture Search Methods for Deep Networks—0
ModuleNet: Knowledge-inherited Neural Architecture Search—0
MO-EMT-NAS: Multi-Objective Continuous Transfer of Architectural Knowledge Between Tasks from Different Datasets—0
MoENAS: Mixture-of-Expert based Neural Architecture Search for jointly Accurate, Fair, and Robust Edge Deep Neural Networks—0
A-DeepPixBis: Attentional Angular Margin for Face Anti-Spoofing—0
MoistNet: Machine Vision-based Deep Learning Models for Wood Chip Moisture Content Measurement—0
MONAS: Efficient Zero-Shot Neural Architecture Search for MCUs—0
MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning—0
MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders—0
Moreau Envelope for Nonconvex Bi-Level Optimization: A Single-loop and Hessian-free Solution Strategy—0
BayesNAS: A Bayesian Approach for Neural Architecture Search—0
Bayesian Deep Learning Hyperparameter Search for Robust Function Mapping to Polynomials with Noise—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