Graph Neural Networks & LLMs in PyG on Marlowe

Graph Neural Networks & LLMs in PyG on Marlowe

Abstract: This talk will cover how Graph Neural Networks can be used to enhance LLMs using PyG to improve accuracy for RAG like tasks across any kind of data domain. This will include examples on real world data. We will also cover how LLMs can be used to enhance GNNs for graph machine learning tasks. While not running on Marlowe, the techniques can be applied to any GPU cluster!

Speaker: Rishi Puri graduated from UC Berkeley and is a lead engineer for the Deep Learning FrameWork PyG at NVIDIA. He is also a core contributor to the open source PyG framework and community. His main focus is researching how to combine state of the art graph and language modeling techniques. He enjoys teaching about this work at Stanford, conferences, webinars, and through the PyG Slack and LinkedIn communities.

About the Marlowe NVIDIA GPU Computing Workshop Series 

We are excited to announce a series of Marlowe NVIDIA-led workshops designed to enhance your expertise in GPU computing. Whether you're a beginner or looking to deepen your skills, these sessions offer valuable insights and hands-on learning opportunities. Workshops are expected to be held on a monthly basis.  

NOTE: This event is open to Stanford and NVIDIA affiliates only.