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Results 1 to 10 of 11

Feature-Preserving Point Cloud Simplification with Gaussian Processes

Date
, 2 - 3pm
Category

The processing, storage and transmission of large-scale point clouds is an ongoing challenge in the computer vision community which hinders progress in the application of 3D models to real-world settings, such as autonomous driving, virtual reality and remote sensing. We propose a novel, one-shot point cloud simplification method which preserves both the salient structural features...

LIDA Data Science Infrastructures Programme Workshop

Date
, 9:30am - 4:30pm
Category

LIDA will be hosting a workshop on infrastructures for data science, machine learning applications and cutting cloud/edge data infrastructures. This will be an opportunity to know more about scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to data scientists. The Data Science Infrastructures Programme stands up an infrastructure stack from...

SciML Leeds Hackathon

Date
, 12pm - 6pm
Category

Welcome to the SciML Leeds Hackathon! This exciting event will focus on using machine learning to identify the dominant ice-water interface via a fusion of SAR and optical imagery. This hackathon is designed to be accessible to participants of all disciplines and skill levels. Whether you're a machine learning enthusiast or simply curious about its...

Machine Learning for Early Diagnosis of Dementia

Date
, 1 - 2pm
Category

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...

Physics-based domain adaptation for dynamical systems forecasting; towards a generalizable and interpretable machine learning for applied engineering

Date
, 2 - 3pm
Category

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...

Weights and Biases Workshop - Machine Learning

Date
, 3 - 4:30pm
Category

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...

Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)

Date
, 11am - 12noon
Category

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...

Hands-On SciML: Reinforcement Learning

Date
, 3pm - 5pm
Category

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...

ClimaX: A foundation model for weather and climate

Date
, 3-4pm
Category

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...

Sea ice detection from concurrent visible and SAR imagery using a convolutional neural network

Date
, 11am - 12noon
Category

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;...