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  2. Exponential decay - Wikipedia

    en.wikipedia.org/wiki/Exponential_decay

    Exponential decay. A quantity undergoing exponential decay. Larger decay constants make the quantity vanish much more rapidly. This plot shows decay for decay constant (λ) of 25, 5, 1, 1/5, and 1/25 for x from 0 to 5. A quantity is subject to exponential decay if it decreases at a rate proportional to its current value.

  3. Exponential growth - Wikipedia

    en.wikipedia.org/wiki/Exponential_growth

    Exponential growth is a process that increases quantity over time at an ever-increasing rate. It occurs when the instantaneous rate of change (that is, the derivative) of a quantity with respect to time is proportional to the quantity itself. Described as a function, a quantity undergoing exponential growth is an exponential function of time ...

  4. Time constant - Wikipedia

    en.wikipedia.org/wiki/Time_constant

    First order LTI systems are characterized by the differential equation + = where τ represents the exponential decay constant and V is a function of time t = (). The right-hand side is the forcing function f(t) describing an external driving function of time, which can be regarded as the system input, to which V(t) is the response, or system output.

  5. Stochastic gradient descent - Wikipedia

    en.wikipedia.org/wiki/Stochastic_gradient_descent

    where the parameter which minimizes () is to be estimated, is a step size (sometimes called the learning rate in machine learning) and is an exponential decay factor between 0 and 1 that determines the relative contribution of the current gradient and earlier gradients to the weight change.

  6. Exponential smoothing - Wikipedia

    en.wikipedia.org/wiki/Exponential_smoothing

    Exponential smoothing. Exponential smoothing or exponential moving average (EMA) is a rule of thumb technique for smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time.

  7. Hopfield network - Wikipedia

    en.wikipedia.org/wiki/Hopfield_network

    In the case of log-sum-exponential Lagrangian function the update rule (if applied once) for the states of the feature neurons is the attention mechanism [14] commonly used in many modern AI systems (see Ref. [15] for the derivation of this result from the continuous time formulation).

  8. Fermi's golden rule - Wikipedia

    en.wikipedia.org/wiki/Fermi's_golden_rule

    Fermi's golden rule. In quantum physics, Fermi's golden rule is a formula that describes the transition rate (the probability of a transition per unit time) from one energy eigenstate of a quantum system to a group of energy eigenstates in a continuum, as a result of a weak perturbation. This transition rate is effectively independent of time ...

  9. Rule of 72 - Wikipedia

    en.wikipedia.org/wiki/Rule_of_72

    These rules apply to exponential growth and are therefore used for compound interest as opposed to simple interest calculations. They can also be used for decay to obtain a halving time. The choice of number is mostly a matter of preference: 69 is more accurate for continuous compounding, while 72 works well in common interest situations and is ...