A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior
Abstract Machine learning (ML) and deep learning (DL) have enabled algorithms to
autonomously acquire knowledge from data, facilitating predictive and decision-making …
autonomously acquire knowledge from data, facilitating predictive and decision-making …
[HTML][HTML] A review of uncertainty estimation and its application in medical imaging
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 …
importance. Deep learning has shown great promise in medical imaging, but the reliability …
Robust multi-view clustering with incomplete information
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 …
view consistency and instance completeness, referred to as the complete information …
Dual contrastive prediction for incomplete multi-view representation learning
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 …
problems in incomplete multi-view representation learning: i) how to learn a consistent …
What makes multi-modal learning better than single (provably)
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 …
different modalities outperform their uni-modal counterparts, since more information is …
Uncertainty-aware multiview deep learning for internet of things applications
As an essential approach in many Internet of Things (IoT) applications, multiview learning
synthesizes multiple features to achieve more comprehensive descriptions of data items …
synthesizes multiple features to achieve more comprehensive descriptions of data items …
Be confident! towards trustworthy graph neural networks via confidence calibration
Abstract Despite Graph Neural Networks (GNNs) have achieved remarkable accuracy,
whether the results are trustworthy is still unexplored. Previous studies suggest that many …
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
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 …
emergence of multi-view data with missing views in real applications. Recent methods …
Provable dynamic fusion for low-quality multimodal data
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 …
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
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 …
networks, these models tend to rely on just one modality while under-fitting the other …