Neural Networks for High-Dimensional Tabular Biomedical Datasets

Add to your list(s) Download to your calendar using vCal

• Andrei Margeloiu (University of Cambridge)
• Tuesday 21 February 2023, 13:00-14:00
• Lecture Theatre 2.

If you have a question about this talk, please contact Mateja Jamnik.

Modern machine learning algorithms frequently overfit on small-sample size and high-dimensional tabular datasets, which are common in medicine, bioinformatics and drug discovery. How can we reduce the overfitting on tabular datasets with D>>N?

This talk presents two neural methods for learning from small-sample size and high-dimensional tabular datasets. First, we present WPFS , a parameter-efficient neural architecture that performs global feature selection during training. Second, we present GCondNet, a general approach which combines Graph Neural Networks (GNNs) for incorporating the implicit relationships between samples when training standard neural networks. GCondNet exploits the high-dimensionality of the data by creating many small graphs to capture the structure between samples within a feature. We show that WPFS and GCondNet outperform both standard and more recent methods on real-world biomedical datasets.

You can also join us on Zoom

This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Artificial Intelligence Research Group Talks (Computer Laboratory)
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge talks
• Chris Davis' list
• Department of Computer Science and Technology talks and seminars
• Guy Emerson's list
• Hanchen DaDaDash
• Interested Talks
• Lecture Theatre 2
• School of Technology
• Speech Seminars
• Trust & Technology Initiative - interesting events
• bld31
• ndk22's list
• ob366-ai4er
• rp587
• yk373's list

Note that ex-directory lists are not shown.