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  2. Backpropagation through time - Wikipedia

    en.wikipedia.org/wiki/Backpropagation_through_time

    Below is pseudocode for a truncated version of BPTT, where the training data contains input-output pairs, and the network is unfolded for time steps: Back_Propagation_Through_Time(a, y) // a[t] is the input at time t. y[t] is the output Unfold the network to contain k instances of f do until stopping criterion is met: x := the zero-magnitude ...

  3. Echo state network - Wikipedia

    en.wikipedia.org/wiki/Echo_state_network

    Modeling of biological systems, neurosciences (cognitive neurodynamics), memory modeling, brain-computer interfaces (BCIs), filtering and Kalman processes, military applications, volatility modeling etc. For the training of RNNs a number of learning algorithms are available: backpropagation through time, real-time recurrent learning ...

  4. Vanishing gradient problem - Wikipedia

    en.wikipedia.org/wiki/Vanishing_gradient_problem

    Backpropagation allowed researchers to train supervised deep artificial neural networks from scratch, initially with little success. Hochreiter 's diplom thesis of 1991 formally identified the reason for this failure in the "vanishing gradient problem", [ 2 ] [ 3 ] which not only affects many-layered feedforward networks , [ 4 ] but also ...

  5. Backpropagation - Wikipedia

    en.wikipedia.org/wiki/Backpropagation

    Backpropagation computes the gradient of a loss function with respect to the weights of the network for a single input–output example, and does so efficiently, computing the gradient one layer at a time, iterating backward from the last layer to avoid redundant calculations of intermediate terms in the chain rule; this can be derived through ...

  6. Recurrent neural network - Wikipedia

    en.wikipedia.org/wiki/Recurrent_neural_network

    The standard method for training RNN by gradient descent is the "backpropagation through time" (BPTT) algorithm, which is a special case of the general algorithm of backpropagation. A more computationally expensive online variant is called "Real-Time Recurrent Learning" or RTRL, [ 78 ] [ 79 ] which is an instance of automatic differentiation in ...

  7. Dynamic network analysis - Wikipedia

    en.wikipedia.org/wiki/Dynamic_network_analysis

    Dynamic networks are a function of time (modeled as a subset of the real numbers) to a set of graphs; for each time point there is a graph. This is akin to the definition of dynamical systems, in which the function is from time to an ambient space, where instead of ambient space time is translated to relationships between pairs of vertices. [1]

  8. Autopsy shows inmate who was beaten while handcuffed died by ...

    www.aol.com/news/autopsy-shows-york-inmate...

    A handcuffed inmate whose fatal beating by correctional officers last year sparked outrage died by homicide, according to findings of an autopsy report a lawyer for the man's family shared Wednesday.

  9. Dynamical system - Wikipedia

    en.wikipedia.org/wiki/Dynamical_system

    A discrete dynamical system, discrete-time dynamical system is a tuple (T, M, Φ), where M is a manifold locally diffeomorphic to a Banach space, and Φ is a function. When T is taken to be the integers, it is a cascade or a map. If T is restricted to the non-negative integers we call the system a semi-cascade. [14]