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Found 9 papers, 2 papers with code
Estimation of Counterfactual Interventions under Uncertainties
This ambiguity is particularly challenging in continuous settings in which a continuum of explanations exist for the same observation.
Validation of Composite Systems by Discrepancy Propagation
Assessing the validity of a real-world system with respect to given quality criteria is a common yet costly task in industrial applications due to the vast number of required real-world tests.
Inferring the Structure of Ordinary Differential Equations
Understanding physical phenomena oftentimes means understanding the underlying dynamical system that governs observational measurements.
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
Neural Stochastic Differential Equations model a dynamical environment with neural nets assigned to their drift and diffusion terms.
Bayesian Evidential Deep Learning with PAC Regularization
We propose a novel method for closed-form predictive distribution modeling with neural nets.
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds
Gaussian Processes (GPs) are a generic modelling tool for supervised learning.
A joint maximum-entropy model for binary neural population patterns and continuous signals
Second-order maximum-entropy models have recently gained much interest for describing the statistics of binary spike trains.
Neurometric function analysis of population codes
In this way, we provide a new rigorous framework for assessing the functional consequences of noise correlation structures for the representational accuracy of neural population codes that is in particular applicable to short-time population coding.
Bayesian estimation of orientation preference maps
Imaging techniques such as optical imaging of intrinsic signals, 2-photon calcium imaging and voltage sensitive dye imaging can be used to measure the functional organization of visual cortex across different spatial scales.
