Federated learning‐based colorectal cancer classification by convolutional neural networks and general visual representation learning
Sign Up to like & getrecommendations! Published in 2023 at "International Journal of Imaging Systems and Technology"
DOI: 10.1002/ima.22875
Abstract: Colorectal cancer is the fourth fatal disease in the world, and the massive burden on the pathologists related to the classification of precancerous and cancerous colorectal lesions can be decreased by deep learning (DL) methods.… read more here.
Keywords: visual representation; representation learning; federated learning; learning ... See more keywords
Extracting metadata of scientific references in patents based on combination of representation learning and machine learning
Sign Up to like & getrecommendations! Published in 2018 at "Proceedings of the Association for Information Science and Technology"
DOI: 10.1002/pra2.2018.14505501189
Abstract: The scientific references in patent (SRPs), one type of non‐patent references (NPRs), is the best representative of scientific knowledge cited in patents (SKCP). However, SKCP is often indicated by NPRs rather than SRPs currently, which… read more here.
Keywords: representation learning; machine learning; metadata; scientific references ... See more keywords
Unsupervised Binary Representation Learning with Deep Variational Networks
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Computer Vision"
DOI: 10.1007/s11263-019-01166-4
Abstract: Learning to hash is regarded as an efficient approach for image retrieval and many other big-data applications. Recently, deep learning frameworks are adopted for image hashing, suggesting an alternative way to formulate the encoding function… read more here.
Keywords: representation learning; learning deep; deep variational; unsupervised binary ... See more keywords
Physical Representation Learning and Parameter Identification from Video Using Differentiable Physics
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Computer Vision"
DOI: 10.1007/s11263-021-01493-5
Abstract: Representation learning for video is increasingly gaining attention in the field of computer vision. For instance, video prediction models enable activity and scene forecasting or vision-based planning and control. In this article, we investigate the… read more here.
Keywords: differentiable physics; representation learning; physics; physical representation ... See more keywords
MGPOOL: multi-granular graph pooling convolutional networks representation learning
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Machine Learning and Cybernetics"
DOI: 10.1007/s13042-021-01328-2
Abstract: Graph convolutional network (GCN) nowadays become new state-of-the-art for networks representation learning. Most of the existing methods are single-granular methods that failed to analyze the graph at multi-granular views so as to lose abundant information.… read more here.
Keywords: coarsest graph; multi granular; networks representation; graph ... See more keywords
SURREAL: Subgraph Robust Representation Learning
Sign Up to like & getrecommendations! Published in 2019 at "Applied Network Science"
DOI: 10.1007/s41109-019-0160-1
Abstract: The success of graph embeddings or nodrepresentation learning in a variety of downstream tasks, such as node classification, link prediction, and recommendation systems, has led to their popularity in recent years. Representation learning algorithms aim… read more here.
Keywords: representation learning; surreal subgraph; graph; subgraph robust ... See more keywords
Representation learning applications in biological sequence analysis
Sign Up to like & getrecommendations! Published in 2021 at "Computational and Structural Biotechnology Journal"
DOI: 10.1016/j.csbj.2021.05.039
Abstract: Although remarkable advances have been reported in high-throughput sequencing, the ability to aptly analyze a substantial amount of rapidly generated biological (DNA/RNA/protein) sequencing data remains a critical hurdle. To tackle this issue, the application of… read more here.
Keywords: sequence analysis; representation learning; biological sequence;
Incomplete multi-modal representation learning for Alzheimer's disease diagnosis
Sign Up to like & getrecommendations! Published in 2021 at "Medical image analysis"
DOI: 10.1016/j.media.2020.101953
Abstract: Alzheimers disease (AD) is a complex neurodegenerative disease. Its early diagnosis and treatment have been a major concern of researchers. Currently, the multi-modality data representation learning of this disease is gradually becoming an emerging research… read more here.
Keywords: representation learning; incomplete multi; multi; view ... See more keywords
Deep spatial representation learning of polyamide nanofiltration membranes
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Membrane Science"
DOI: 10.1016/j.memsci.2020.118910
Abstract: Abstract Machine learning overfitting caused by data scarcity greatly limits the application of chemical artificial intelligence in membrane materials. As the original data for thin film polyamide nanofiltration membranes is limited, here we propose to… read more here.
Keywords: nanofiltration; spatial representation; polyamide nanofiltration; representation learning ... See more keywords
Wave2Vec: Deep representation learning for clinical temporal data
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.03.074
Abstract: Abstract Representation learning for time series has gained increasing attention in healthcare domain. The recent advancement in semantic learning allows researcher to learn meaningful deep representations of clinical medical concepts from Electronic Health Records (EHRs).… read more here.
Keywords: representation learning; wave2vec deep; time; model ... See more keywords
Empirical kernel map-based multilayer extreme learning machines for representation learning
Sign Up to like & getrecommendations! Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.05.032
Abstract: Abstract Recently, multilayer extreme learning machine (ML-ELM) and hierarchical extreme learning machine (H-ELM) were developed for representation learning whose training time can be reduced from hours to seconds compared to traditional stacked autoencoder (SAE). However,… read more here.
Keywords: representation learning; kernel; extreme learning; layer ... See more keywords