By Michael S. Kearns, Michael J. Kearns, Sara A. Solla, David A. Cohn
The yearly convention on Neural details Processing structures (NIPS) is the flagship convention on neural computation. It attracts preeminent educational researchers from worldwide and is broadly thought of to be a exhibit convention for brand new advancements in community algorithms and architectures. The large diversity of interdisciplinary study parts represented comprises desktop technological know-how, neuroscience, records, physics, cognitive technological know-how, and plenty of branches of engineering, together with sign processing and regulate thought. merely approximately 30 percentage of the papers submitted are authorised for presentation at NIPS, so the standard is incredibly excessive. those complaints comprise all the papers that have been awarded.
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Extra info for Advances in Neural Information Processing Systems 11
The range of conditions that can be studied through network simulation is very large, since even physically impossible assumptions can be used, as in analytical modeling. Result reliability Given the exclusive use of models in network simulation, the realism of the experimental results can be low, especially if very complex scenarios are simulated. Result quality also depends on how detailed the computer models are, but the trade-oﬀ between level of detail and execution speed implies that, more often than not, simulation uses simpliﬁed models of reality.
An analytical model (also called “mathematical model”) is a model that uses mathematical equations to describe a system. , the total number of incoming items • service discipline, meaning in what order the queue items are served, usually compared with their arrival time Distributions can be, for example, deterministic, exponential, general (arbitrary), and the number of servers can be 1, multiple, or even inﬁnite. System capacity and calling population are also usually assumed to be inﬁnite. The service discipline is typically ﬁrst-in, ﬁrst-out (FIFO).
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Advances in Neural Information Processing Systems 11 by Michael S. Kearns, Michael J. Kearns, Sara A. Solla, David A. Cohn