Neural Architecture Search
776 papers with code • 26 benchmarks • 27 datasets
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
Benchmarks
These leaderboards are used to track progress in Neural Architecture Search
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Libraries
Use these libraries to find Neural Architecture Search models and implementationsDatasets
CIFAR-10
ImageNet
CIFAR-100
MNIST
Oxford 102 Flower
STL-10
Stanford Cars
DTD
Food-101
FGVC-Aircraft
Latest papers
FR-NAS: Forward-and-Reverse Graph Predictor for Efficient Neural Architecture Search
emi-group/fr-nas
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Additionally, we incorporate a customized training loss within the GNN predictor to ensure efficient utilization of both types of representations.
Unsupervised Domain Adaptation Architecture Search with Self-Training for Land Cover Mapping
Thus, we proposed a simple yet effective framework to search for lightweight neural networks automatically for land cover mapping tasks under domain shifts.
MobileNetV4 - Universal Models for the Mobile Ecosystem
We present the latest generation of MobileNets, known as MobileNetV4 (MNv4), featuring universally efficient architecture designs for mobile devices.
Shears: Unstructured Sparsity with Neural Low-rank Adapter Search
intellabs/hardware-aware-automated-machine-learning
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Recently, several approaches successfully demonstrated that weight-sharing Neural Architecture Search (NAS) can effectively explore a search space of elastic low-rank adapters (LoRA), allowing the parameter-efficient fine-tuning (PEFT) and compression of large language models.
Accel-NASBench: Sustainable Benchmarking for Accelerator-Aware NAS
We present a technique that allows searching for training proxies that reduce the cost of benchmark construction by significant margins, making it possible to construct realistic NAS benchmarks for large-scale datasets.
On Spectrogram Analysis in a Multiple Classifier Fusion Framework for Power Grid Classification Using Electric Network Frequency
The Electric Network Frequency (ENF) serves as a unique signature inherent to power distribution systems.
Neural Architecture Search for Sentence Classification with BERT
themody/nasforsentenceembeddingheads
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Pre training of language models on large text corpora is common practice in Natural Language Processing.
PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture Search
Multiple object tracking is a critical task in autonomous driving.
Building Optimal Neural Architectures using Interpretable Knowledge
Neural Architecture Search is a costly practice.
Boosting Order-Preserving and Transferability for Neural Architecture Search: a Joint Architecture Refined Search and Fine-tuning Approach
beichenzbc/supernet-shifting
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In this work, we analyze the order-preserving ability on the whole search space (global) and a sub-space of top architectures (local), and empirically show that the local order-preserving for current two-stage NAS methods still need to be improved.
