Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed. - GitHub - divamgupta/diffusionbee-stable-diffusion-ui: Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.
Engineer friends often ask me: Graph Deep Learning sounds great, but are there any big commercial success stories? Is it being deployed in practical applications? Besides the obvious ones–recommendation systems at Pinterest, Alibaba and Twitter–a slightly nuanced success story is the Transformer architecture, which has taken the NLP industry by storm. Through this post, I want to establish links between Graph Neural Networks (GNNs) and Transformers. I’ll talk about the intuitions behind model architectures in the NLP and GNN communities, make connections using equations and figures, and discuss how we could work together to drive progress.
L. Schmidt-Thieme. Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 27-30 November 2005, page 378-385. Houston, Texas, USA, IEEE Computer Society, (2005)
C. Kemp, and K. Ramamohanarao. Proceedings of the 6th European Conference on Principles
of Data Mining and Knowledge Discovery (PKDD 2002), page 263--274. Berlin, Springer, (2002)