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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 351–375 of 1915 papers

Title	Date	Tasks	Status	Hype
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation	Oct 29, 2023	DiversityEvolutionary Algorithms	—Unverified	0
MicroNAS: Memory and Latency Constrained Hardware-Aware Neural Architecture Search for Time Series Classification on Microcontrollers	Oct 27, 2023	Hardware Aware Neural Architecture SearchNeural Architecture Search	—Unverified	0
Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image Classification	Oct 26, 2023	Computational EfficiencyDecoder	CodeCode Available	0
An Approach for Efficient Neural Architecture Search Space Definition	Oct 25, 2023	AutoMLNeural Architecture Search	—Unverified	0
LLM Performance Predictors are good initializers for Architecture Search	Oct 25, 2023	Machine TranslationNeural Architecture Search	CodeCode Available	0
Cascaded Multi-task Adaptive Learning Based on Neural Architecture Search	Oct 23, 2023	Neural Architecture Search	—Unverified	0
MGAS: Multi-Granularity Architecture Search for Trade-Off Between Model Effectiveness and Efficiency	Oct 23, 2023	Neural Architecture Search	—Unverified	0
Fairer and More Accurate Tabular Models Through NAS	Oct 18, 2023	FairnessHyperparameter Optimization	—Unverified	0
ASP: Automatic Selection of Proxy dataset for efficient AutoML	Oct 17, 2023	AutoMLNeural Architecture Search	—Unverified	0
Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devices	Oct 11, 2023	Neural Architecture Search	CodeCode Available	1
Entropic Score metric: Decoupling Topology and Size in Training-free NAS	Oct 6, 2023	GPUNeural Architecture Search	—Unverified	0
Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data	Oct 4, 2023	AutoMLHyperparameter Optimization	CodeCode Available	0
Brain development dictates energy constraints on neural architecture search: cross-disciplinary insights on optimization strategies	Oct 3, 2023	Neural Architecture Search	—Unverified	0
Evolutionary Neural Architecture Search for Transformer in Knowledge Tracing	Oct 2, 2023	feature selectionKnowledge Tracing	CodeCode Available	1
Learnable Extended Activation Function (LEAF) for Deep Neural Networks	Sep 30, 2023	Activation Function Synthesisimage-classification	CodeCode Available	0
Graph Neural Architecture Search with GPT-4	Sep 30, 2023	Neural Architecture Search	—Unverified	0
Order-Preserving GFlowNets	Sep 30, 2023	Neural Architecture Search	CodeCode Available	0
DataDAM: Efficient Dataset Distillation with Attention Matching	Sep 29, 2023	Continual LearningDataset Distillation	CodeCode Available	1
DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks	Sep 26, 2023	DenoisingImage Denoising	—Unverified	0
ZiCo-BC: A Bias Corrected Zero-Shot NAS for Vision Tasks	Sep 26, 2023	image-classificationImage Classification	—Unverified	0
NAS-NeRF: Generative Neural Architecture Search for Neural Radiance Fields	Sep 25, 2023	GPUNeRF	—Unverified	0
Grassroots Operator Search for Model Edge Adaptation	Sep 20, 2023	Hardware Aware Neural Architecture Searchmodel	—Unverified	0
iHAS: Instance-wise Hierarchical Architecture Search for Deep Learning Recommendation Models	Sep 14, 2023	Clusteringfeature selection	—Unverified	0
Harmonic-NAS: Hardware-Aware Multimodal Neural Architecture Search on Resource-constrained Devices	Sep 12, 2023	General ClassificationMultimodal Text and Image Classification	CodeCode Available	1
Band-gap regression with architecture-optimized message-passing neural networks	Sep 12, 2023	Band Gapband gap regression	CodeCode Available	0

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All datasets ImageNet NAS-Bench-201, ImageNet-16-120 CIFAR-10 NAS-Bench-201, CIFAR-100 NAS-Bench-201, CIFAR-10 CIFAR-10 Image Classification CIFAR-100 NATS-Bench Topology, CIFAR-10 NATS-Bench Topology, CIFAR-100 NATS-Bench Topology, ImageNet16-120 Food-101 NAS-Bench-101

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	SPOS (ProxylessNAS (GPU) latency)	Accuracy	75.3	—	Unverified
2	SPOS (FBNet-C latency)	Accuracy	75.1	—	Unverified
3	SPOS (block search + channel search)	Accuracy	74.7	—	Unverified
4	MUXNet-xs	Top-1 Error Rate	33.3	—	Unverified
5	FBNetV2-F1	Top-1 Error Rate	31.7	—	Unverified
6	LayerNAS-60M	Top-1 Error Rate	31	—	Unverified
7	NASGEP	Top-1 Error Rate	29.51	—	Unverified
8	MUXNet-s	Top-1 Error Rate	28.4	—	Unverified
9	NN-MASS-A	Top-1 Error Rate	27.1	—	Unverified
10	FBNetV2-F3	Top-1 Error Rate	26.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CR-LSO	Accuracy (Test)	46.98	—	Unverified
2	Shapley-NAS	Accuracy (Test)	46.85	—	Unverified
3	β-SDARTS-RS	Accuracy (Test)	46.71	—	Unverified
4	β-RDARTS-L2	Accuracy (Test)	46.71	—	Unverified
5	NAR	Accuracy (Test)	46.66	—	Unverified
6	ASE-NAS+	Accuracy (Val)	46.66	—	Unverified
7	BaLeNAS-TF	Accuracy (Test)	46.54	—	Unverified
8	AG-Net	Accuracy (Test)	46.42	—	Unverified
9	Local search	Accuracy (Test)	46.38	—	Unverified
10	NASBOT	Accuracy (Test)	46.37	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Balanced Mixture	Accuracy (% )	91.55	—	Unverified
2	GDAS	Top-1 Error Rate	3.4	—	Unverified
3	Bonsai-Net	Top-1 Error Rate	3.35	—	Unverified
4	Net2 (2)	Top-1 Error Rate	3.3	—	Unverified
5	μDARTS	Top-1 Error Rate	3.28	—	Unverified
6	NN-MASS- CIFAR-C	Top-1 Error Rate	3.18	—	Unverified
7	DARTS (first order)	Top-1 Error Rate	3	—	Unverified
8	NN-MASS- CIFAR-A	Top-1 Error Rate	3	—	Unverified
9	AlphaX-1 (cutout NASNet)	Top-1 Error Rate	2.82	—	Unverified
10	NASGEP	Top-1 Error Rate	2.82	—	Unverified