Delta-NAS: Encoding Architecture Differences for More Efficient Neural Architecture Search
Delta-NAS is a novel approach to Neural Architecture Search (NAS) that improves efficiency by predicting the difference in accuracy between similar networks, enabling fine-grained search at a lower computational cost.
成長するネットワークのためのガウス・ニュートン近似を用いたモルフィズムの学習
本稿では、計算コストを抑えつつ高精度なニューラルネットワークアーキテクチャを自動的に構築する、新しいNeural Architecture Search (NAS)手法を提案する。
Learning Neural Network Architectures by Growing Networks with Learned Morphisms
This paper proposes a novel Neural Architecture Search (NAS) method that efficiently grows neural networks by learning and applying network morphisms, achieving comparable or superior performance to existing NAS techniques at a lower computational cost.
Lamarckian Co-Evolutionary Algorithm (LCoDeepNEAT) for Developing Convolutional Neural Networks with Optimized Last Layer Weights
This paper introduces LCoDeepNEAT, a novel Neural Architecture Search (NAS) method based on Lamarckian genetic algorithms, which co-evolves CNN architectures and their last layer weights to achieve faster convergence and higher accuracy in image classification tasks.
GPT-NAS: Optimizing Neural Architecture Search Using Generative Pre-Trained Transformers and Evolutionary Algorithms
GPT-NAS leverages the pattern recognition and generative capabilities of pre-trained GPT models to enhance the efficiency of evolutionary algorithms in finding optimal neural architectures.
Structure of Artificial Neural Networks: An Empirical Investigation
By formalizing the structure of deep neural networks as directed acyclic graphs, this dissertation investigates the impact of structure on network performance, analyzes various automated construction methods, and proposes new predictive and generative models for neural architecture search.
Dense Optimizer: Using Information Entropy to Automatically Design Dense-like Neural Networks
Dense Optimizer is a novel approach to automatically design efficient dense-like neural networks by maximizing the network's information entropy while adhering to a power-law distribution across different stages, leading to superior performance in image classification tasks.
A Comprehensive Survey of Efficient Evaluation Methods for Neural Architecture Search
Efficient Evaluation Methods (EEMs) are crucial for mitigating the high computational cost of Neural Architecture Search (NAS) by accelerating the performance evaluation of candidate architectures, enabling wider accessibility and practical application of NAS.
Scalable Reinforcement Learning-based Neural Architecture Search: Efficiently Exploring Large Search Spaces
A novel Reinforcement Learning-based solution for Neural Architecture Search that learns to efficiently search large spaces, outperforming strong baselines like local search and random search.
Efficient Neural Architecture Search Using Graph-Based Metrics Without Training
NASGraph, a training-free and data-agnostic neural architecture search method, maps neural architectures to graphs and uses graph measures as proxy metrics to efficiently rank and search for optimal architectures.
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