20 citations · 36 across the 17 of their papers we have counts for
18 papers
Prior-Fitted Functional Flow: In-Context Generative Models for Pharmacokinetics
César Ojeda, Niklas Hartung, Wilhelm Huisinga +6
We introduce Prior-Fitted Functional Flows, a generative foundation model for pharmacokinetics that enables zero-shot population synthesis and individual forecasting without manual…
Foundation Inference Models for Ordinary Differential Equations
Maximilian Mauel, Johannes R. Hübers, David Berghaus +2
Ordinary differential equations (ODEs) are central to scientific modelling, but inferring their vector fields from noisy trajectories remains challenging. Current approaches such a…
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…
Zero-shot Imputation with Foundation Inference Models for Dynamical Systems
Patrick Seifner, Kostadin Cvejoski, Antonia Körner +1
Dynamical systems governed by ordinary differential equations (ODEs) serve as models for a vast number of natural and social phenomena. In this work, we offer a fresh perspective o…
Neural Markov Jump Processes
Patrick Seifner, Ramses J. Sanchez
Markov jump processes are continuous-time stochastic processes with a wide range of applications in both natural and social sciences. Despite their widespread use, inference in the…
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…
Dynamic Review-based Recommenders
Kostadin Cvejoski, Ramses J. Sanchez, Christian Bauckhage +1
Just as user preferences change with time, item reviews also reflect those same preference changes. In a nutshell, if one is to sequentially incorporate review content knowledge in…
Recurrent Point Review Models
Kostadin Cvejoski, Ramses J. Sanchez, Bogdan Georgiev +2
Deep neural network models represent the state-of-the-art methodologies for natural language processing. Here we build on top of these methodologies to incorporate temporal informa…
Recurrent Point Processes for Dynamic Review Models
Kostadin Cvejoski, Ramses J. Sanchez, Bogdan Georgiev +3
Recent progress in recommender system research has shown the importance of including temporal representations to improve interpretability and performance. Here, we incorporate temp…
Recurrent Adversarial Service Times
César Ojeda, Kostadin Cvejosky, Ramsés J. Sánchez +3
Service system dynamics occur at the interplay between customer behaviour and a service provider's response. This kind of dynamics can effectively be modeled within the framework o…
Anomalous and regular transport in spin 1/2 chains: AC conductivity
Ramsés J. Sánchez, Vipin Kerala Varma, Vadim Oganesyan
We study magnetization transport in anisotropic spin- chains governed by the integrable XXZ model with and without integrability-breaking perturbations at high temperatures ($…
Finite-size anomalies of the Drude weight: role of symmetries and ensembles
Ramsés J. Sánchez, Vipin Kerala Varma
We revisit the subtelties of computing the high temperature spin stiffness of the spin- XXZ chain using exact diagonalization to analyze its dependence on system symmetrie…
Interaction-induced Drude weight enhancement in frustrated systems
Vipin Kerala Varma, Ramsés Sánchez
We investigate the effect of next nearest-neighbour hopping on the zero temperature Drude weight or superfluidity in mesoscopic one-dimensional systems of (a) single particle with…
Entanglement sudden-death time: a geometric quantity
Ramsés Sánchez, Esteban Isasi, Douglas Mundarain
We study the entanglement evolution of the set of Bell diagonal states for a two-qubit system coupled to two independent vacuum noise sources. This set can be represented geometric…