Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-
dimensional spaces according to specific tasks. Up to now, there have been several surveys …

Deep learning has been successful in automating the design of features in machine learning
pipelines. However, the algorithms optimizing neural network parameters remain largely …

Correlated time series (CTS) forecasting plays an essential role in many cyber-physical
systems, where multiple sensors emit time series that capture interconnected processes …

Time-consuming performance evaluation is the bottleneck of traditional Neural Architecture
Search (NAS) methods. Predictor-based NAS can speed up performance evaluation by …

Abstract Predictor-based Neural Architecture Search (NAS) continues to be an important
topic because it aims to mitigate the time-consuming search procedure of traditional NAS …

Abstract Knowledge distillation has become increasingly important in model compression. It
boosts the performance of a miniaturized student network with the supervision of the output …

With the wide and deep adoption of deep learning models in real applications, there is an
increasing need to model and learn the representations of the neural networks themselves …

The success of neural networks (NNs) is tightly linked with their architectural design—a
complex problem by itself. We here introduce a novel framework leveraging Graph Neural …

Graph-based predictors have recently shown promising results on neural architecture
search (NAS). Despite their efficiency, current graph-based predictors treat all operations …

Neural Architecture Search (NAS), aiming to automatically search for neural structure that
performs the best, has attracted lots of attentions from both the academy and industry …