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Found 6 papers, 2 papers with code
Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations
In this work, we investigate both of these challenges and propose a novel class of models, Shape Arithmetic Expressions (SHAREs), that fuses GAM's flexible shape functions with the complex feature interactions found in mathematical expressions.
ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference
Above all, we consider the introduction of a completely new type of solution to be our most important contribution as it may spark entirely new innovations in treatment effects in general.
Causal Deep Learning
Our framework clearly identifies which assumptions are testable and which ones are not, such that the resulting solutions can be judiciously adopted in practice.
Navigating causal deep learning
With CDL, researchers aim to structure and encode causal knowledge in the extremely flexible representation space of deep learning models.
D-CODE: Discovering Closed-form ODEs from Observed Trajectories
In the experiments, D-CODE successfully discovered the governing equations of a diverse range of dynamical systems under challenging measurement settings with high noise and infrequent sampling.
Privacy-preserving Object Detection
Privacy considerations and bias in datasets are quickly becoming high-priority issues that the computer vision community needs to face.
