2 citations · 3 across the 5 of their papers we have counts for
3 papers · 1 filter
Hide&Seek: Learning to Explain in an End-to-End Differentiable Network
Tal Ellinson, Hadi Mohasel Afshar, Sally Cripps
Instance-wise feature selection is a valuable tool for interpreting labeled data and the predictions of black-box models. In contrast to global feature selection techniques, instan…
Covariate Dependent Mixture of Bayesian Networks
Roman Marchant, Dario Draca, Gilad Francis +4
Learning the structure of Bayesian networks from data provides insights into underlying processes and the causal relationships that generate the data, but its usefulness depends on…
Optimal Particle-based Approximation of Discrete Distributions (OPAD)
Hadi Mohasel Afshar, Gilad Francis, Sally Cripps
Particle-based methods include a variety of techniques, such as Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC), for approximating a probabilistic target distribut…