Deep learning-based clustering approaches for bioinformatics
Clustering is central to many data-driven bioinformatics research and serves a powerful
computational method. In particular, clustering helps at analyzing unstructured and high …
computational method. In particular, clustering helps at analyzing unstructured and high …
On the role of knowledge graphs in explainable AI
F Lecue - Semantic Web, 2020 - content.iospress.com
The current hype of Artificial Intelligence (AI) mostly refers to the success of machine
learning and its sub-domain of deep learning. However, AI is also about other areas, such …
learning and its sub-domain of deep learning. However, AI is also about other areas, such …
Deepproblog: Neural probabilistic logic programming
We introduce DeepProbLog, a probabilistic logic programming language that incorporates
deep learning by means of neural predicates. We show how existing inference and learning …
deep learning by means of neural predicates. We show how existing inference and learning …
From statistical relational to neuro-symbolic artificial intelligence
Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for
learning with logical reasoning. This survey identifies several parallels across seven …
learning with logical reasoning. This survey identifies several parallels across seven …
Combining deep learning and ontology reasoning for remote sensing image semantic segmentation
Y Li, S Ouyang, Y Zhang - Knowledge-based systems, 2022 - Elsevier
Because of its wide potential applications, remote sensing (RS) image semantic
segmentation has attracted increasing research interest in recent years. Until now, deep …
segmentation has attracted increasing research interest in recent years. Until now, deep …
[HTML][HTML] Geoscience-aware deep learning: A new paradigm for remote sensing
Abstract Information extraction is a key activity for remote sensing images. A common
distinction exists between knowledge-driven and data-driven methods. Knowledge-driven …
distinction exists between knowledge-driven and data-driven methods. Knowledge-driven …
Combining deep semantic segmentation network and graph convolutional neural network for semantic segmentation of remote sensing imagery
S Ouyang, Y Li - Remote Sensing, 2020 - mdpi.com
Although the deep semantic segmentation network (DSSN) has been widely used in remote
sensing (RS) image semantic segmentation, it still does not fully mind the spatial …
sensing (RS) image semantic segmentation, it still does not fully mind the spatial …
[PDF][PDF] Semantic web technologies for explainable machine learning models: A literature review.
Due to their tremendous potential in predictive tasks, Machine Learning techniques such as
Artificial Neural Networks have received great attention from both research and practice …
Artificial Neural Networks have received great attention from both research and practice …
Multi-turn intent determination and slot filling with neural networks and regular expressions
Intent determination and slot filling are two prominent research areas related to natural
language understanding (NLU). In a multi-turn NLU system, contextual information from …
language understanding (NLU). In a multi-turn NLU system, contextual information from …
Neuro-symbolic learning: Principles and applications in ophthalmology
Neural networks have been rapidly expanding in recent years, with novel strategies and
applications. However, challenges such as interpretability, explainability, robustness, safety …
applications. However, challenges such as interpretability, explainability, robustness, safety …