11 citations · 20 across the 14 of their papers we have counts for
15 papers
In-Context Learning of Temporal Point Processes with Foundation Inference Models
David Berghaus, Patrick Seifner, Kostadin Cvejoski +2
Modeling event sequences of multiple event types with marked temporal point processes (MTPPs) provides a principled way to uncover governing dynamical rules and predict future even…
In-Context Learning of Stochastic Differential Equations with Foundation Inference Models
Patrick Seifner, Kostadin Cvejoski, David Berghaus +2
Stochastic differential equations (SDEs) describe dynamical systems where deterministic flows, governed by a drift function, are superimposed with random fluctuations, dictated by…
Foundation Inference Models for Markov Jump Processes
David Berghaus, Kostadin Cvejoski, Patrick Seifner +2
Markov jump processes are continuous-time stochastic processes which describe dynamical systems evolving in discrete state spaces. These processes find wide application in the natu…
Neural Dynamic Focused Topic Model
Kostadin Cvejoski, Ramsés J. Sánchez, César Ojeda
Topic models and all their variants analyse text by learning meaningful representations through word co-occurrences. As pointed out by Williamson et al. (2010), such models implici…
The future is different: Large pre-trained language models fail in prediction tasks
Kostadin Cvejoski, Ramsés J. Sánchez, César Ojeda
Large pre-trained language models (LPLM) have shown spectacular success when fine-tuned on downstream supervised tasks. Yet, it is known that their performance can drastically drop…
Hidden Schema Networks
Ramsés J. Sánchez, Lukas Conrads, Pascal Welke +2
Large, pretrained language models infer powerful representations that encode rich semantic and syntactic content, albeit implicitly. In this work we introduce a novel neural langua…