Max van Spengler

You can find our paper at: arxiv.org/abs/2502.17130 This paper would not have been possible without the support and supervision by @PascalMettes! 🙌

Our method leads to highly faithful embeddings that can easily be used in any downstream machine learning application! If you would like to know more or if you want to chat about hyperbolic geometry in general, come check out the poster on Wednesday at 16:30!

Excited to be in Vancouver for #ICML2025 this week! I’m here to talk about our latest work “Low-distortion and GPU-compatible tree embeddings in hyperbolic space”. If you're interested in graph embeddings and hyperbolic geometry, come and check it out! More details below 👇

(1/6)🥳 Excited to share my latest research done as part of my MSc AI thesis! We introduced Hyperbolic Compositional CLIP (HyCoCLIP)—a novel framework that leverages the hierarchical nature of hyperbolic space for learning vision-language representations using scene compositions.

Tomorrow I'll be presenting our hyperbolic learning library HypLL at the open-source software competition of #ACMMM2023! If you are around, come check it out!

Curious to check it out or want to learn more? Check out the library at …bolic-learning-library.readthedocs.io/en/latest/ and github.com/maxvanspengler…. Or read the paper at arxiv.org/abs/2306.06154. 3/3

Following recent successes hyperbolic geometry is rapidly gaining traction in the field of deep learning. Our library allows for easy integration of these new hyperbolic techniques into any PyTorch model, while also allowing for easy and transparent debugging. 2/3

Excited to share that our previously announced Hyperbolic Learning Library and the corresponding paper "HypLL: The Hyperbolic Learning Library" have been accepted for the open-source software competition at #ACMMM2023! w/ @phippli and @PascalMettes 1/3

Initial experiments show that our Poincaré ResNet is competitive and complementary to its Euclidean counterpart, while being more robust to adversarial attacks and out-of-distribution samples. Want to know more? Check out our paper at: arxiv.org/abs/2303.14027 5/5

Hierarchies appear everywhere in the visual world, making hyperbolic learning an obvious choice for computer vision as well. Our Poincaré ResNet is a first step in this direction, extracting fully hyperbolic features from visual data for any downstream task you want. 4/5

Happy to share that our paper "Poincaré ResNet" has been accepted for #ICCV2023! w/ Erwin Berkhout and @PascalMettes 1/5

Curious to explore the benefits of hyperbolic geometry? Give it a shot! Check out the docs at …bolic-learning-library.readthedocs.io/en/latest/ 4/4

With HypLL you can expect to - Seamlessly integrate hyperbolic geometry into any PyTorch model. - Enjoy easy and transparent debugging, even with multiple manifolds involved! 3/4

Excited to introduce HypLL, our brand-new hyperbolic learning library developed together with @phippli and @PascalMettes! 1/4