Saee Paliwal — accepted AI-conference papers

2026

Scaling Laws and Architectural Frontiers in Metagenomic Foundation Models

Geraldene Munsamy, Gavin Ayres, Jérémie DONA, Carla Greco, Daniel P Anderson, Srijani Sridhar +19

ICML 2026poster

Foundation models for genomics have the potential to revolutionize therapeutic design, yet the optimal architectural choices for modeling the vast and diverse distribution of metagenomic data remain under-explored. In this work, we present the machine learning methodology behind MODEL, a family of m…

Cited by 0 Source ↗Scholar ↗

2021

Directed Graph Embeddings in Pseudo-Riemannian Manifolds

Aaron Sim, Maciej L Wiatrak, Angus Brayne, Paidi Creed, Saee Paliwal

ICML 2021spotlight

The inductive biases of graph representation learning algorithms are often encoded in the background geometry of their embedding space. In this paper, we show that general directed graphs can be effectively represented by an embedding model that combines three components: a pseudo-Riemannian metric…

Cited by 19 Source ↗PDF ↗code ↗Scholar ↗