The rapid development of time series forecasting research has brought many
deep learning-based modules in this field. However, despite the increasing
amount of new forecasting architectures, it is still unclear if we have
leveraged the full potential of these existing modules within a properly
designed architecture. In this work, we propose a novel hierarchical neural
architecture search approach for time series forecasting tasks. With the design
of a hierarchical search space, we incorporate many architecture types designed
for forecasting tasks and allow for the efficient combination of different
forecasting architecture modules. Results on long-term-time-series-forecasting
tasks show that our approach can search for lightweight high-performing
forecasting architectures across different forecasting tasks.

我们提出了一种新颖的层次神经架构搜索方法，用于时间序列预测任务，在长期时间序列预测任务中，我们的方法可以搜索跨不同预测任务的轻量高性能预测架构。

优化时间序列预测架构：一种层次神经架构搜索方法

Optimizing Time Series Forecasting Architectures: A Hierarchical Neural  Architecture Search Approach

We introduce the first Neural Architecture Search (NAS) method to find a
better transformer architecture for image recognition. Recently, transformers
without CNN-based backbones are found to achieve impressive performance for
image recognition. However, the transformer is designed for NLP tasks and thus
could be sub-optimal when directly used for image recognition. In order to
improve the visual representation ability for transformers, we propose a new
search space and searching algorithm. Specifically, we introduce a locality
module that models the local correlations in images explicitly with fewer
computational cost. With the locality module, our search space is defined to
let the search algorithm freely trade off between global and local information
as well as optimizing the low-level design choice in each module. To tackle the
problem caused by huge search space, a hierarchical neural architecture search
method is proposed to search the optimal vision transformer from two levels
separately with the evolutionary algorithm. Extensive experiments on the
ImageNet dataset demonstrate that our method can find more discriminative and
efficient transformer variants than the ResNet family (e.g., ResNet101) and the
baseline ViT for image classification.

通过局部模块来提高图像转换器的视觉表示能力，建立搜索空间和搜索算法体系，提出分层神经架构搜索方法并证明其在 ImageNet 数据集上对于图像分类比 ResNet 和 ViT 机型更优。

GLiT: 全局和局部图像 Transformer 的神经网络架构搜索

GLiT: Neural Architecture Search for Global and Local Image Transformer

Deep neural networks have exhibited promising performance in image
super-resolution (SR). Most SR models follow a hierarchical architecture that
contains both the cell-level design of computational blocks and the
network-level design of the positions of upsampling blocks. However, designing
SR models heavily relies on human expertise and is very labor-intensive. More
critically, these SR models often contain a huge number of parameters and may
not meet the requirements of computation resources in real-world applications.
To address the above issues, we propose a Hierarchical Neural Architecture
Search (HNAS) method to automatically design promising architectures with
different requirements of computation cost. To this end, we design a
hierarchical SR search space and propose a hierarchical controller for
architecture search. Such a hierarchical controller is able to simultaneously
find promising cell-level blocks and network-level positions of upsampling
layers. Moreover, to design compact architectures with promising performance,
we build a joint reward by considering both the performance and computation
cost to guide the search process. Extensive experiments on five benchmark
datasets demonstrate the superiority of our method over existing methods.

提出了一种基于 Hierarchical Neural Architecture Search (HNAS) 的方法来自动设计计算成本不同的高级 SR 架构，同时考虑性能与计算成本来指导搜索过程，实验表明该方法在性能上优于现有方法。