image: NAS methods can generally be classified based on tailored designs from the following aspects: search space, search strategy, and evaluation strategy. In particular, search space can be further categorized into two types, macro space for the entire network and micro space for modules or blocks of the neural network. The search strategy is to discover the optimal architecture from the search space, which needs to balance the effectiveness and efficiency simultaneously. The evaluation strategy decides how to estimate the performance of different architectures.
Credit: ©Science China Press
This study, led by Dr. Xin Wang and Dr. Wenwu Zhu from Tsinghua University, delves into the rapidly evolving field of Neural Architecture Search (NAS), an area within Automated Machine Learning (AutoML), which has gained significant research attention for its potential to automate the design of optimal neural network architectures. The research is particularly timely, given the increasing computational demands and complexities associated with designing neural networks for diverse applications. Wang and Zhu explore the core components of NAS, covering the definition of search spaces, the development of search strategies, and the design of effective evaluation mechanisms.
One key contribution of this study is its detailed analysis of the transition from early NAS methods, which were groundbreaking but computationally intensive, to more efficient learning paradigms. These newer approaches, such as weight sharing and evaluation estimation, significantly reduce the computational cost, making NAS more accessible and practical for a wider range of applications. The introduction of specialized benchmarks is highlighted as a critical advancement, enabling standardized comparisons, and fostering more reliable assessments of NAS methodologies.
The adaptability and generalization ability of NAS to various data types, including graphs, tabular data, and videos, is another focal point of this study. Wang and Zhu highlight the necessity of tailoring NAS techniques to the specific characteristics of these data types, which has led to innovations in both search spaces and strategies. For instance, the application of NAS to Graph Neural Networks (GNNs) is explored in depth, with the authors discussing the unique challenges and opportunities presented by non-Euclidean data.
In addition to summarizing current trends, the study offers a forward-looking perspective on NAS, identifying several promising directions for future research. These include leveraging large language models (LLMs) for NAS, which could unlock new capabilities in zero-shot and in-context learning. The potential for NAS to optimize multimodal neural networks, which integrate diverse data types such as images, texts, and graphs, is also discussed. The authors suggest that future NAS research could focus on improving the efficiency of these techniques, particularly in resource-constrained environments, and on developing more effective evaluation strategies that account for the unique challenges of different data modalities.
Wang and Zhu’s research serves as a comprehensive overview that not only summarizes the progress of NAS but also sets the stage for future developments in the field. Their work highlights the importance of continuing to refine and adapt NAS methodologies to meet the growing demands of machine learning applications across a broad spectrum of industries and research areas. By pushing the boundaries of what NAS can achieve, this study contributes significantly to the ongoing evolution of automated machine learning, with implications that extend far beyond the immediate scope of neural network design.
See the article:
Advances in Neural Architecture Search
https://doi.org/10.1093/nsr/nwae282
Journal
National Science Review