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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 551–600 of 1915 papers

TitleStatusHype
SpiKernel: A Kernel Size Exploration Methodology for Improving Accuracy of the Embedded Spiking Neural Network Systems—0
Exploiting Operation Importance for Differentiable Neural Architecture Search—0
EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS—0
Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification—0
Conditional Neural Architecture Search—0
A method for quantifying the generalization capabilities of generative models for solving Ising models—0
Concurrent Neural Tree and Data Preprocessing AutoML for Image Classification—0
ConCoDE: Hard-constrained Differentiable Co-Exploration Method for Neural Architectures and Hardware Accelerators—0
Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence—0
Conceptual Expansion Neural Architecture Search (CENAS)—0
Computation Reallocation for Object Detection—0
AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System—0
Compression of Site-Specific Deep Neural Networks for Massive MIMO Precoding—0
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models—0
AutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System—0
A Memetic Algorithm based on Variational Autoencoder for Black-Box Discrete Optimization with Epistasis among Parameters—0
Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study—0
AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models—0
Compatibility-aware Heterogeneous Visual Search—0
Comparative Analysis of Unsupervised and Supervised Autoencoders for Nuclei Classification in Clear Cell Renal Cell Carcinoma Images—0
AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models—0
3-Dimensional residual neural architecture search for ultrasonic defect detection—0
Exploring Resiliency to Natural Image Corruptions in Deep Learning using Design Diversity—0
FACETS: Efficient Once-for-all Object Detection via Constrained Iterative Search—0
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing—0
Communication-Efficient Separable Neural Network for Distributed Inference on Edge Devices—0
Combining Neural Architecture Search and Automatic Code Optimization: A Survey—0
Combined Scheduling, Memory Allocation and Tensor Replacement for Minimizing Off-Chip Data Accesses of DNN Accelerators—0
EvoPrompting: Language Models for Code-Level Neural Architecture Search—0
Auto-CsiNet: Scenario-customized Automatic Neural Network Architecture Generation for Massive MIMO CSI Feedback—0
Colony-Enhanced Recurrent Neural Architecture Search: Collaborative Ant-Based Optimization—0
EZNAS: Evolving Zero Cost Proxies For Neural Architecture Scoring—0
ALT: An Automatic System for Long Tail Scenario Modeling—0
Evo-NAS: Evolutionary-Neural Hybrid Agent for Architecture Search—0
Exascale Deep Learning to Accelerate Cancer Research—0
Cognitive Neural Architecture Search Reveals Hierarchical Entailment—0
Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks—0
AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping Reinforcement—0
Coarse-to-Fine Searching for Efficient Generative Adversarial Networks—0
CNAS: Channel-Level Neural Architecture Search—0
AutoCoG: A Unified Data-Modal Co-Search Framework for Graph Neural Networks—0
Accelerating Multi-Objective Neural Architecture Search by Random-Weight Evaluation—0
Clustering and Classification Networks—0
Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-time Mobile Telepresence—0
CLOSE: Curriculum Learning On the Sharing Extent Towards Better One-shot NAS—0
AutoCaption: Image Captioning with Neural Architecture Search—0
ExperienceThinking: Constrained Hyperparameter Optimization based on Knowledge and Pruning—0
CLASH: Complementary Learning with Neural Architecture Search for Gait Recognition—0
Chimera: A Block-Based Neural Architecture Search Framework for Event-Based Object Detection—0
AutoBERT-Zero: Evolving BERT Backbone from Scratch—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