Great presentation. I'm working on drug target prediction and it is nice to know that with graph embeddings I'm halfway through my journey. By the way which font did you use for your slides? They really look nice.
Thanks, for such great content. I am working on MultiVariate Time Series Anomaly Detection using GNNs, Transformers, and GANs, do you know of any resource where I can start? I searched a lot but couldn't find anything other than papers, which are not that useful. Thanks again
Hey Muhammad, thank you and thanks for the question! Unfortunately, I just recently started working with Time Series data myself so I can't give you a ton of recommendations. But I think a Contrastive Learning approach would be a good bet for OOD detection in any data-domain - maybe check out "Domain Agnostic Contrastive Learning" for a general framework on how to encode your data into a semantic embedding space for this. Good luck with your project!
Great presentation. I'm working on drug target prediction and it is nice to know that with graph embeddings I'm halfway through my journey. By the way which font did you use for your slides? They really look nice.
Thanks, for such great content.
I am working on MultiVariate Time Series Anomaly Detection using GNNs, Transformers, and GANs, do you know of any resource where I can start?
I searched a lot but couldn't find anything other than papers, which are not that useful.
Thanks again
Hey Muhammad, thank you and thanks for the question! Unfortunately, I just recently started working with Time Series data myself so I can't give you a ton of recommendations. But I think a Contrastive Learning approach would be a good bet for OOD detection in any data-domain - maybe check out "Domain Agnostic Contrastive Learning" for a general framework on how to encode your data into a semantic embedding space for this. Good luck with your project!
@@connor-shorten Thanks alot.
Maybe a practical example