Abstract: Systems, devices, methods, and computer-readable media for determining a hyper-opinion classification of an object. A method can include receiving data of an object to be classified, and determining, using a neural network, a hyper-opinion classification of the object including an indication of the probabilities of base classes and composite classes that are “or” combinations of proper subsets of the base classes.

Abstract: Discussed herein are devices, systems, and methods for Bayesian neural network (BNN) training using mini-batch particle flow. A method for training a Bayesian neural network (BNN) using batched inputs and operating the trained BNN can include initializing particles such that each particle individually represents pointwise values of respective NN parameters of NNs and such that the particles collectively represent a distribution of parameters of the BNN, optimizing, using min-batch training particle flow, the particles based on batches of inputs, resulting in optimized distributions for the parameters, determining a prediction distribution using the optimized distributions for the parameters and predictions from each of the NNs, and providing a marginalized distribution representative of the prediction distribution.

Abstract: Discussed herein are devices, systems, and methods for training and operating a Bayesian neural network (BNN). A method can include initializing particles that each individually represent pointwise values of respective NN parameters of NNs that collectively represent a distribution of parameters of the BNN, optimizing, using training particle flow, the particles resulting in optimized distributions for the parameters, determining a prediction distribution using the optimized distributions for the parameters and predictions from each of the NNs, and providing a marginalized distribution representative of the prediction distribution.

Abstract: Systems, devices, methods, and computer-readable media for determining a hyper-opinion classification of an object. A method can include receiving data of an object to be classified, and determining, using a neural network, a hyper-opinion classification of the object including an indication of the probabilities of base classes and composite classes that are “or” combinations of proper subsets of the base classes.