By Thomas Diettrich, Suzanna Becker, Zoubin Ghahramani
The once a year convention on Neural info Processing structures (NIPS) is the flagship convention on neural computation. The convention is interdisciplinary, with contributions in algorithms, studying idea, cognitive technology, neuroscience, imaginative and prescient, speech and sign processing, reinforcement studying and regulate, implementations, and various purposes. in basic terms approximately 30 percentage of the papers submitted are authorised for presentation at NIPS, so the standard is outstandingly excessive. those court cases comprise the entire papers that have been offered on the 2001 convention.
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Extra resources for Advances in Neural Information Processing Systems 14: Proceedings of the 2001 Conference
With a valued map the implication algebra can be used for a super symbolic evaluation of the classical logic. With a suitable code of the symbols in the implication algebra, we can give a modal logic value to every symbol. A connection with the modal logic super valuation and the implication algebra will be usehl for a description of the L algebra with the traditional True, False logic values. Because meta-theory of uncertainty uses modal logic as logic instrument, we suggest in this paper to rewrite the main part of the implication algebra by modal logic in such a way as to write a common language between the traditional fuzzy logic and the implication algebra.
A. Orlovski: Calculus of Decomposable Properties, Fuzzy Sets and Decisions. Allerton Press, New York, 1994. 23. K. Pattanaik: Voting and Collective Choice. Cambdrige University Press, Cambridge, 1971. 24. B. Roy: Decision science or decision-aid science. European Journal of Operational Research 66 (1993), 184-203. 25. L. Savage: The Foundations of Statistics. Wiley, New York, 1954. 26. K. Sen: Collective Choice and Social Welfare. Holden-Day, San Francisco, 1970. 27. G. Shafer: Savage revisited (with discussion).
Nth-order ' higher-order neural unit with an n-dimensional input vector can be expressed as Y = 442) n n where x = [ X I , 2 2 , . . ,xn] E Rn'is the vector of neural inputs, y E R is an output scalar, and $( . ), exists. The structure of this higher-order neural unit is shown in Figure 1. 3 + w12x1x2 +w224 + w112+2 + W 1 2 2 X 1 4 + w 2 2 2 4 ) (3) Theses higher-order neural units can be used in conventional feedforward neural network structures as the hidden units to form higher-order neural networks.