A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior

E Yaghoubi, E Yaghoubi, A Khamees, D Razmi… - … Applications of Artificial …, 2024 - Elsevier
Abstract Machine learning (ML) and deep learning (DL) have enabled algorithms to
autonomously acquire knowledge from data, facilitating predictive and decision-making …

[HTML][HTML] A review of uncertainty estimation and its application in medical imaging

K Zou, Z Chen, X Yuan, X Shen, M Wang, H Fu - Meta-Radiology, 2023 - Elsevier
The use of AI systems in healthcare for the early screening of diseases is of great clinical
importance. Deep learning has shown great promise in medical imaging, but the reliability …

Robust multi-view clustering with incomplete information

M Yang, Y Li, P Hu, J Bai, J Lv… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The success of existing multi-view clustering methods heavily relies on the assumption of
view consistency and instance completeness, referred to as the complete information …

Dual contrastive prediction for incomplete multi-view representation learning

Y Lin, Y Gou, X Liu, J Bai, J Lv… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In this article, we propose a unified framework to solve the following two challenging
problems in incomplete multi-view representation learning: i) how to learn a consistent …

What makes multi-modal learning better than single (provably)

Y Huang, C Du, Z Xue, X Chen… - Advances in Neural …, 2021 - proceedings.neurips.cc
The world provides us with data of multiple modalities. Intuitively, models fusing data from
different modalities outperform their uni-modal counterparts, since more information is …

Uncertainty-aware multiview deep learning for internet of things applications

C Xu, W Zhao, J Zhao, Z Guan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
As an essential approach in many Internet of Things (IoT) applications, multiview learning
synthesizes multiple features to achieve more comprehensive descriptions of data items …

Be confident! towards trustworthy graph neural networks via confidence calibration

X Wang, H Liu, C Shi, C Yang - Advances in Neural …, 2021 - proceedings.neurips.cc
Abstract Despite Graph Neural Networks (GNNs) have achieved remarkable accuracy,
whether the results are trustworthy is still unexplored. Previous studies suggest that many …

Enhanced tensor low-rank and sparse representation recovery for incomplete multi-view clustering

C Zhang, H Li, W Lv, Z Huang, Y Gao… - Proceedings of the AAAI …, 2023 - ojs.aaai.org
Incomplete multi-view clustering (IMVC) has attracted remarkable attention due to the
emergence of multi-view data with missing views in real applications. Recent methods …

Provable dynamic fusion for low-quality multimodal data

Q Zhang, H Wu, C Zhang, Q Hu, H Fu… - International …, 2023 - proceedings.mlr.press
The inherent challenge of multimodal fusion is to precisely capture the cross-modal
correlation and flexibly conduct cross-modal interaction. To fully release the value of each …

Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks

N Wu, S Jastrzebski, K Cho… - … Conference on Machine …, 2022 - proceedings.mlr.press
We hypothesize that due to the greedy nature of learning in multi-modal deep neural
networks, these models tend to rely on just one modality while under-fitting the other …