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 …
S Lu, J Li, J Tan, S Yang, J Liu - Advances in Neural …, 2021 - proceedings.neurips.cc
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 …
A Agiollo, A Omicini - Uncertainty in Artificial Intelligence, 2022 - proceedings.mlr.press
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 …