Results 11 to 18 of 18
Combining Supervised and Unsupervised Machine Learning for Early Diagnosis of Dementia Despite the increasing availability of health data, clinically translatable methods to predict the conversion from Mild Cognitive Impairment (MCI) to dementia are still lacking. MCI represents a precursor to dementia for many individuals; however, some forms of MCI tend to remain stable over time...
Data-driven models, especially machine learning-based models such as recurrent neural networks, are a popular choice for time-series forecasting because they can capture spatiotemporal structures from timeseries data, without reference to the mechanics governing the underlying phenomenon. However, their ability to generalize robustly depends on how well-represented the governing dynamics are in the data. This is...
The Weights and Biases workshop is a hands-on event designed for researchers seeking to stay up-to-date with the latest developments in machine learning. The workshop will be led by an experienced ML engineer who will guide participants through practical applications of Weights and Biases in their ML projects. Attendees will have the opportunity to learn...
Hybrid event – Room 11.87, Worsley Building / Microsoft Teams Speaker: Dr. Ramesh Nadarajah, BHF Clinical Research Fellow REGISTER NOW This talk will cover Ramesh’s research to develop FIND-AF (Future Innovations in Novel Detection of Atrial Fibrillation). It will cover the burden of atrial fibrillation in the health service, the learning that has gone into...
This event is primarily an in-person event, however an online option is available to attend the talk in the first hour (3-4pm). REGISTER NOW Speaker: Alhanof Alolyan, Ph.D. candidate at School of Computing Reinforcement learning (RL) might serve as an investigative way to explore unexpected and valuable findings in your work. Have you ever considered...
Speaker – Tung Nguyen, PhD student, UCL REGISTER NOW Recent data-driven approaches based on machine learning aim to directly solve a downstream forecasting or projection task by learning a data-driven functional mapping using deep neural networks. However, these networks are trained using curated and homogeneous climate datasets for specific spatiotemporal tasks, and thus lack the...
Speaker – Martin Rodgers, Machine Learning Researcher, British Antarctic Survey’s AI lab REGISTER NOW Abstract: Accurate measurements of Antarctic sea ice extent are important for safe navigation of polar ships, understanding ecosystem dynamics and identifying sea ice response to changing temperature and weather patterns. Sea ice has primarily been detected using passive microwave satellite data;...
Friday 18th November - 11am – 12:30 pm University of Leeds - Worsley Seminar Room 8.20Q Speaker - Dr Claire Heaney (Imperial College London) Abstract: This presentation will summarise a couple of research directions involving machine learning and computational science before focusing on the main topic of data-driven reduced-order models (DDROM). Often involving neural networks,...