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

Showing 11011125 of 1915 papers

TitleStatusHype
MC-QDSNN: Quantized Deep evolutionary SNN with Multi-Dendritic Compartment Neurons for Stress Detection using Physiological Signals0
MCUBERT: Memory-Efficient BERT Inference on Commodity Microcontrollers0
MCU-MixQ: A HW/SW Co-optimized Mixed-precision Neural Network Design Framework for MCUs0
Boosting Share Routing for Multi-task Learning0
Boosting Network: Learn by Growing Filters and Layers via SplitLBI0
μDARTS: Model Uncertainty-Aware Differentiable Architecture Search0
Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation0
MemNet: Memory-Efficiency Guided Neural Architecture Search with Augment-Trim learning0
BOMP-NAS: Bayesian Optimization Mixed Precision NAS0
Memory-efficient Patch-based Inference for Tiny Deep Learning0
MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification0
Meta knowledge assisted Evolutionary Neural Architecture Search0
Meta-Learning of NAS for Few-shot Learning in Medical Image Applications0
Binarized Neural Architecture Search for Efficient Object Recognition0
Binarized Neural Architecture Search0
MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN0
MFAS: Multimodal Fusion Architecture Search0
Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable Blocks for Image Classification0
M-FasterSeg: An Efficient Semantic Segmentation Network Based on Neural Architecture Search0
MGAS: Multi-Granularity Architecture Search for Trade-Off Between Model Effectiveness and Efficiency0
MicroNAS: An Automated Framework for Developing a Fall Detection System0
MicroNAS: Memory and Latency Constrained Hardware-Aware Neural Architecture Search for Time Series Classification on Microcontrollers0
MicroNAS: Zero-Shot Neural Architecture Search for MCUs0
SpeedLimit: Neural Architecture Search for Quantized Transformer Models0
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SPOS (ProxylessNAS (GPU) latency)Accuracy75.3Unverified
2SPOS (FBNet-C latency)Accuracy75.1Unverified
3SPOS (block search + channel search)Accuracy74.7Unverified
4MUXNet-xsTop-1 Error Rate33.3Unverified
5FBNetV2-F1Top-1 Error Rate31.7Unverified
6LayerNAS-60MTop-1 Error Rate31Unverified
7NASGEPTop-1 Error Rate29.51Unverified
8MUXNet-sTop-1 Error Rate28.4Unverified
9NN-MASS-ATop-1 Error Rate27.1Unverified
10FBNetV2-F3Top-1 Error Rate26.8Unverified
#ModelMetricClaimedVerifiedStatus
1CR-LSOAccuracy (Test)46.98Unverified
2Shapley-NASAccuracy (Test)46.85Unverified
3β-RDARTS-L2Accuracy (Test)46.71Unverified
4β-SDARTS-RSAccuracy (Test)46.71Unverified
5ASE-NAS+Accuracy (Val)46.66Unverified
6NARAccuracy (Test)46.66Unverified
7BaLeNAS-TFAccuracy (Test)46.54Unverified
8AG-NetAccuracy (Test)46.42Unverified
9Local searchAccuracy (Test)46.38Unverified
10NASBOTAccuracy (Test)46.37Unverified
#ModelMetricClaimedVerifiedStatus
1Balanced MixtureAccuracy (% )91.55Unverified
2GDASTop-1 Error Rate3.4Unverified
3Bonsai-NetTop-1 Error Rate3.35Unverified
4Net2 (2)Top-1 Error Rate3.3Unverified
5μDARTSTop-1 Error Rate3.28Unverified
6NN-MASS- CIFAR-CTop-1 Error Rate3.18Unverified
7NN-MASS- CIFAR-ATop-1 Error Rate3Unverified
8DARTS (first order)Top-1 Error Rate3Unverified
9NASGEPTop-1 Error Rate2.82Unverified
10AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82Unverified