Scenario understanding and motion prediction for autonomous vehicles—review and comparison
Scenario understanding and motion prediction are essential components for completely
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …
Integration of machine learning and coarse-grained molecular simulations for polymer materials: physical understandings and molecular design
In recent years, the synthesis of monomer sequence-defined polymers has expanded into
broad-spectrum applications in biomedical, chemical, and materials science fields. Pursuing …
broad-spectrum applications in biomedical, chemical, and materials science fields. Pursuing …
Benchmarking multi-task learning for sentiment analysis and offensive language identification in under-resourced dravidian languages
To obtain extensive annotated data for under-resourced languages is challenging, so in this
research, we have investigated whether it is beneficial to train models using multi-task …
research, we have investigated whether it is beneficial to train models using multi-task …
[PDF][PDF] The Impacts and Challenges of Generative Artificial Intelligence in Medical Education, Clinical Diagnostics, Administrative Efficiency, and Data Generation
JP Singh - International Journal of Applied Health Care Analytics, 2023 - researchgate.net
The objective of this study is to investigate the role of generative artificial intelligence (AI) in
improving healthcare, focusing on four key areas: medical education, clinical diagnosis …
improving healthcare, focusing on four key areas: medical education, clinical diagnosis …
Variationally regularized graph-based representation learning for electronic health records
W Zhu, N Razavian - Proceedings of the Conference on Health …, 2021 - dl.acm.org
Electronic Health Records (EHR) are high-dimensional data with implicit connections
among thousands of medical concepts. These connections, for instance, the co-occurrence …
among thousands of medical concepts. These connections, for instance, the co-occurrence …
Deep learning in medicine: advancing healthcare with intelligent solutions and the future of holography imaging in early diagnosis
Deep Learning (DL) is currently transforming health services by significantly improving early
cancer diagnosis, drug discovery, protein–protein interaction analysis, and gene editing …
cancer diagnosis, drug discovery, protein–protein interaction analysis, and gene editing …
Multi-task learning in under-resourced Dravidian languages
It is challenging to obtain extensive annotated data for under-resourced languages, so we
investigate whether it is beneficial to train models using multi-task learning. Sentiment …
investigate whether it is beneficial to train models using multi-task learning. Sentiment …
Optimizing training trajectories in variational autoencoders via latent Bayesian optimization approach
Unsupervised and semi-supervised ML methods such as variational autoencoders (VAE)
have become widely adopted across multiple areas of physics, chemistry, and materials …
have become widely adopted across multiple areas of physics, chemistry, and materials …
Multi-task learning for predicting synergistic drug combinations based on auto-encoding multi-relational graphs
Combinatorial drug therapy is a promising approach for treating complex diseases by
combining drugs with synergistic effects. However, predicting effective drug combinations is …
combining drugs with synergistic effects. However, predicting effective drug combinations is …
On the effect of isotropy on vae representations of text
Injecting desired geometric properties into text representations has attracted a lot of
attention. A property that has been argued for, due to its better utilisation of representation …
attention. A property that has been argued for, due to its better utilisation of representation …