Neural networks

Absstract of: EP4657316A1

Methods and systems for fully quantifying predictive uncertainty for recommender systems are disclosed. Embodiments include providing a fully Bayesian multiway neural network model, assigning a prior distribution over a plurality of weight parameters of a model parameter set of the neural network model such that a corresponding posterior distribution represents an epistemic uncertainty of the neural network model; incorporating a mixture density model for a target layer of the neural network model, wherein the mixture density model models aleatoric uncertainty of input data; estimating the posterior distribution of the weight parameters given training data by updating the prior distribution based on data likelihood of the training data; sampling a plurality of empirical samples from the estimated posterior distribution of the weight parameters; performing inference for each stochastic realization of the neural network model with the sampled weight parameters on an inference sample to generate one or more predictive distributions; drawing samples from each of the one or more predictive distributions as final predictions for the inference sample to obtain a matrix of predictions for the inference sample, wherein the matrix represents a joint distribution of both the aleatoric and epistemic uncertainty of the recommender system.