35 citations · 65 across the 5 of their papers we have counts for
5 papers
A Mechanistic Study of Tabular Foundation Models
Marin Biloš, James T. Wilson, Anderson Schneider +1
Tabular foundation models with different architectures converge in accuracy across a range of classification and regression tasks. This raises questions a leaderboard cannot answer…
Add and Thin: Diffusion for Temporal Point Processes
David Lüdke, Marin Biloš, Oleksandr Shchur +2
Autoregressive neural networks within the temporal point process (TPP) framework have become the standard for modeling continuous-time event data. Even though these models can expr…
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Kashif Rasul, Arjun Ashok, Andrew Robert Williams +15
Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-shot and few-shot generalization. However,…
Neural Flows: Efficient Alternative to Neural ODEs
Marin Biloš, Johanna Sommer, Syama Sundar Rangapuram +2
Neural ordinary differential equations describe how values change in time. This is the reason why they gained importance in modeling sequential data, especially when the observatio…
Fast and Flexible Temporal Point Processes with Triangular Maps
Oleksandr Shchur, Nicholas Gao, Marin Biloš +1
Temporal point process (TPP) models combined with recurrent neural networks provide a powerful framework for modeling continuous-time event data. While such models are flexible, th…