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SUMMARY:An Overview of Probabilistic Latent Variable Models - Aditya Ravur
 i (Computer Lab)
DTSTART:20240214T100000Z
DTEND:20240214T110000Z
UID:TALK217732@talks.cam.ac.uk
CONTACT:65128
DESCRIPTION:This talk showcases some interesting probabilistic interpretat
 ions of dimensionality reduction and unsupervised representation learning 
 algorithms and presents the common statistical modelling assumptions that 
 underpin these algorithms. Specifically\, we'll look at methods such as PC
 A\, GMM\, ICA\, FA\, VAE and GPLVM and show how these share the same model
 ling framework. If there's interest\, I'll also talk about a large class o
 f other algorithms that also fit into this framework (such as t-SNE\, UMAP
 \, isomap and MDS). I'll cover some newer methods like contrastive learnin
 g and show how these fit in with classical latent variable models.
LOCATION:Martin Ryle Seminar Room\, KICC
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