2 citations · 2 across the 6 of their papers we have counts for
3 papers · 1 filter
Prompting Neural-Guided Equation Discovery Based on Residuals
Jannis Brugger, Viktor Pfanschilling, David Richter +2
Neural-guided equation discovery systems use a data set as prompt and predict an equation that describes the data set without extensive search. However, if the equation does not me…
N-Parties Private Structure and Parameter Learning for Sum-Product Networks
Xenia Heilmann, Ernst Althaus, Mattia Cerrato +3
A sum-product network (SPN) is a graphical model that allows several types of probabilistic inference to be performed efficiently. In this paper, we propose a privacy-preserving pr…
Neural-Guided Equation Discovery
Jannis Brugger, Mattia Cerrato, David Richter +4
Deep learning approaches are becoming increasingly attractive for equation discovery. We show the advantages and disadvantages of using neural-guided equation discovery by giving a…