most citedCheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems

1 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting

Busra Asan, Abdullah Akgül, Alper Unal +2

Seasonal forecasting is a crucial task when it comes to detecting the extreme heat and colds that occur due to climate change. Confidence in the predictions should be reliable sinc…

cs.LG2023

Demystifying the Myths and Legends of Nonconvex Convergence of SGD

Aritra Dutta, El Houcine Bergou, Soumia Boucherouite +3

Stochastic gradient descent (SGD) and its variants are the main workhorses for solving large-scale optimization problems with nonconvex objective functions. Although the convergenc…

cs.LG20231 cited

Estimation of Counterfactual Interventions under Uncertainties

Juliane Weilbach, Sebastian Gerwinn, Melih Kandemir +1

Counterfactual analysis is intuitively performed by humans on a daily basis eg. "What should I have done differently to get the loan approved?". Such counterfactual questions also…

cs.LG20231 cited

Sampling-Free Probabilistic Deep State-Space Models

Andreas Look, Melih Kandemir, Barbara Rakitsch +1

Many real-world dynamical systems can be described as State-Space Models (SSMs). In this formulation, each observation is emitted by a latent state, which follows first-order Marko…

cs.LG20231 cited

Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems

Andreas Look, Melih Kandemir, Barbara Rakitsch +1

Graph neural networks are often used to model interacting dynamical systems since they gracefully scale to systems with a varying and high number of agents. While there has been mu…