D. Ackley, G. Hinton, T. Sejnowski, E. Farguell, F. Mazzanti, E. Gómez-ramírez, ...
HOD process. Therefore, there is a tradeoff between memory and computing time. HOD provides a direct solution for the learning algorithm. In comparison, tuning the MC algorithm to provide lower error...
D. Rumelhart, G. Hinton, R. Williams, Learning Internal Representation
In conclusion, the investigated v€ ( xw GO method has been applied to NN learning and the results from multiple (100) independent test runs have shown consistent and stable performance (although...
B. Flower, Weight An, G. Hinton, T. Sejnowski
VLSI feedforward and recurrent multilayer networks. Neural Computation 3(4):546–565, 1991. [192] Jabri, M.A., and B. Flower. Weight perturbation: An optimal architecture and learning technique for...
Classical and Bayesian inference in neuroimaging: Theory (2002)
K. J. Friston, W. Penny, C. Phillips, S. Kiebel, G. Hinton, J. Ashburner
This paper reviews hierarchical observation models, used in functional neuroimaging, in a Bayesian light. It emphasizes the common ground shared by classical and Bayesian methods to show that...