Search Results for author:
Found 7 papers, 2 papers with code
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.
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.
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.
