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In mathematics, a rate is the quotient of two quantities, often represented as a fraction. [1] If the divisor (or fraction denominator) in the rate is equal to one expressed as a single unit, and if it is assumed that this quantity can be changed systematically (i.e., is an independent variable), then the dividend (the fraction numerator) of the rate expresses the corresponding rate of change ...
In the adaptive control literature, the learning rate is commonly referred to as gain. [2] In setting a learning rate, there is a trade-off between the rate of convergence and overshooting. While the descent direction is usually determined from the gradient of the loss function, the learning rate determines how big a step is taken in that ...
A conceptually simple extension of stochastic gradient descent makes the learning rate a decreasing function η t of the iteration number t, giving a learning rate schedule, so that the first iterations cause large changes in the parameters, while the later ones do only fine-tuning.
In mathematics — specifically, in large deviations theory — a rate function is a function used to quantify the probabilities of rare events. Such functions are used to formulate large deviation principles. A large deviation principle quantifies the asymptotic probability of rare events for a sequence of probabilities.
In computer science, rate-monotonic scheduling (RMS) [1] is a priority assignment algorithm used in real-time operating systems (RTOS) with a static-priority scheduling class. [2] The static priorities are assigned according to the cycle duration of the job, so a shorter cycle duration results in a higher job priority.
An amortization calculator is used to determine the periodic payment amount due on a loan (typically a mortgage), based on the amortization process.. The amortization repayment model factors varying amounts of both interest and principal into every installment, though the total amount of each payment is the same.
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If R 1 and R 2 are the rate of responses on two schedules that yield obtained (as distinct from programmed) rates of reinforcement Rf 1 and Rf 2, the strict matching law holds that the relative response rate R 1 / (R 1 + R 2) matches, that is, equals, the relative reinforcement rate Rf 1 / (Rf 1 + Rf 2).