GraphML News (March 16th) - ICLR 2025 Workshops, Mediterranean Summer School
🍻 ICLR 2025 is approaching and it’s time to select some workshops to attend to chat with friends and hide from the heat of Singapore. Graph learning aficionados might be interested in a various bio / health / science workshops:
- Neural Network Weights as a New Data Modality
- GemBio
- ML for Genomics
- Agentic AI 4 Science
- AI for Nucleic Acids
- Learning Meaningful Representations of Life (LMRL)
- AI 4 Material Discovery
- Frontiers in Probabilistic Inference: learning meets Sampling
Some of them have already published the accepted papers on OpenReview - here is a usual reminder to go find some hidden gems as most workshop papers evolve into full conference papers.
🇭🇷 The Balkans get all the fancy machine learning summer schools in 2025: we know that EEML 2025 will take place in Sarajevo, July 21-26. If you won’t make it, the Mediterranean Machine Learning Summer School (M2ML) will open its doors in Split, Sept 8-12 (just 6 hours by bus from Sarajevo). The school is organized by Google DeepMind folks together with the University of Split. The application deadline is March 28th, and there are some rumors about the GNN tutorial held by some familiar faces in this channel 😉
Weekend reading is brought to you by non-equivariant transformers that go brrr in various domains:
Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems by Maksim Zhdanov, Max Welling, and Jan-Willem van de Meent - introduces a smart variant of local attention - ball tree attention - for large particle systems where nearest neighbors are progressively encoded into a ball tree, and the attention is only computed wrt the given ball. Excels in MD and fluid dynamics sims. Code
Proteina: Scaling Flow-based Protein Structure Generative Models by Tomas Geffner, Kieran Didi, Zuobai Zhang, and NVIDIA folks (ICLR 2025 Oral) - scaling flow matching with non-equivariant transformers to 400M params yields significant improvements over Genie2, Chroma, RFDiffusion across many protein design tasks. You only need 128 GPUs 😉 Code and checkpoints
All-atom Diffusion Transformers: Unified generative modelling of molecules and materials by Chaitanya K. Joshi, Xiang Fu, and FAIR at Meta - latent diffusion with a single all-atom transformer for both molecules (on QM9) and periodic structures (MP20) scaled up to 500M params is very competitive with the current equivariant SOTA while being much faster.
🍻 ICLR 2025 is approaching and it’s time to select some workshops to attend to chat with friends and hide from the heat of Singapore. Graph learning aficionados might be interested in a various bio / health / science workshops:
- Neural Network Weights as a New Data Modality
- GemBio
- ML for Genomics
- Agentic AI 4 Science
- AI for Nucleic Acids
- Learning Meaningful Representations of Life (LMRL)
- AI 4 Material Discovery
- Frontiers in Probabilistic Inference: learning meets Sampling
Some of them have already published the accepted papers on OpenReview - here is a usual reminder to go find some hidden gems as most workshop papers evolve into full conference papers.
🇭🇷 The Balkans get all the fancy machine learning summer schools in 2025: we know that EEML 2025 will take place in Sarajevo, July 21-26. If you won’t make it, the Mediterranean Machine Learning Summer School (M2ML) will open its doors in Split, Sept 8-12 (just 6 hours by bus from Sarajevo). The school is organized by Google DeepMind folks together with the University of Split. The application deadline is March 28th, and there are some rumors about the GNN tutorial held by some familiar faces in this channel 😉
Weekend reading is brought to you by non-equivariant transformers that go brrr in various domains:
Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems by Maksim Zhdanov, Max Welling, and Jan-Willem van de Meent - introduces a smart variant of local attention - ball tree attention - for large particle systems where nearest neighbors are progressively encoded into a ball tree, and the attention is only computed wrt the given ball. Excels in MD and fluid dynamics sims. Code
Proteina: Scaling Flow-based Protein Structure Generative Models by Tomas Geffner, Kieran Didi, Zuobai Zhang, and NVIDIA folks (ICLR 2025 Oral) - scaling flow matching with non-equivariant transformers to 400M params yields significant improvements over Genie2, Chroma, RFDiffusion across many protein design tasks. You only need 128 GPUs 😉 Code and checkpoints
All-atom Diffusion Transformers: Unified generative modelling of molecules and materials by Chaitanya K. Joshi, Xiang Fu, and FAIR at Meta - latent diffusion with a single all-atom transformer for both molecules (on QM9) and periodic structures (MP20) scaled up to 500M params is very competitive with the current equivariant SOTA while being much faster.