This paper applies Bayesian neural networks on the inference of diesel 85% ASTM distillation, and compares the results with traditional multi-layer perceptrons.

Training a multilayer perceptron by an error backpropagation algorithm is slow and uncertain. This paper describes a new approach which is much faster and certain than error backpropagation. The proposed approach is based on combined iterative and direct solution methods. In this approach, we use an inverse transformation for linearization of nonlinear output activation functions, direct soluti...

We introduce an eecient agnostic learning algorithm for the class of half-spaces in < n. We make no assumptions whatsoever on the example-generating distribution. Our performance guarantee is that, given any > 0, our algorithm runs in time polynomial in the sample size and dimension, and outputs a hypothesis half-space that classiies correctly at least the number of points classiied correctly w...

The millimeter-wave frequencies planned for 6G systems present challenges channel modeling. At these frequencies, surface roughness affects wave propagation and causes severe attenuation of (mmWave) signals. In general, beamforming techniques compensate this problem. Analog has some major advantages over its counterpart, digital beamforming, because it uses low-cost phase shifters massive MIMO ...

Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent's environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which co...