Autoencoders
An autoencoder is a specific type of a neural network, which is mainly designed to encode
the input into a compressed and meaningful representation and then decode it back such …
the input into a compressed and meaningful representation and then decode it back such …
Towards the next generation of machine learning models in additive manufacturing: A review of process dependent material evolution
Additive manufacturing facilitates producing of complex parts due to its design freedom in a
wide range of applications. Despite considerable advancements in additive manufacturing …
wide range of applications. Despite considerable advancements in additive manufacturing …
Contrastive clustering
In this paper, we propose an online clustering method called Contrastive Clustering (CC)
which explicitly performs the instance-and cluster-level contrastive learning. To be specific …
which explicitly performs the instance-and cluster-level contrastive learning. To be specific …
Deep learning for anomaly detection: A survey
R Chalapathy, S Chawla - ar** deep learning approaches to learn a …
Cancer diagnosis using deep learning: a bibliographic review
In this paper, we first describe the basics of the field of cancer diagnosis, which includes
steps of cancer diagnosis followed by the typical classification methods used by doctors …
steps of cancer diagnosis followed by the typical classification methods used by doctors …
Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study
J Tromp, PJ Seekings, CL Hung, MB Iversen… - The Lancet Digital …, 2022 - thelancet.com
Background Echocardiography is the diagnostic modality for assessing cardiac systolic and
diastolic function to diagnose and manage heart failure. However, manual interpretation of …
diastolic function to diagnose and manage heart failure. However, manual interpretation of …
Deep learning in mental health outcome research: a sco** review
Mental illnesses, such as depression, are highly prevalent and have been shown to impact
an individual's physical health. Recently, artificial intelligence (AI) methods have been …
an individual's physical health. Recently, artificial intelligence (AI) methods have been …
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 …