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  2. Long short-term memory - Wikipedia

    en.wikipedia.org/wiki/Long_short-term_memory

    The Long Short-Term Memory (LSTM) cell can process data sequentially and keep its hidden state through time. Long short-term memory (LSTM) [1] is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem [2] commonly encountered by traditional RNNs.

  3. Gating mechanism - Wikipedia

    en.wikipedia.org/wiki/Gating_mechanism

    The gated recurrent unit (GRU) simplifies the LSTM. [3] Compared to the LSTM, the GRU has just two gates: a reset gate and an update gate. GRU also merges the cell state and hidden state. The reset gate roughly corresponds to the forget gate, and the update gate roughly corresponds to the input gate. The output gate is removed.

  4. Recurrent neural network - Wikipedia

    en.wikipedia.org/wiki/Recurrent_neural_network

    The fundamental building block of RNNs is the recurrent unit, which maintains a hidden state—a form of memory that is updated at each time step based on the current input and the previous hidden state. This feedback mechanism allows the network to learn from past inputs and incorporate that knowledge into its current processing.

  5. Gated recurrent unit - Wikipedia

    en.wikipedia.org/wiki/Gated_recurrent_unit

    Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. [1] The GRU is like a long short-term memory (LSTM) with a gating mechanism to input or forget certain features, [2] but lacks a context vector or output gate, resulting in fewer parameters than LSTM. [3]

  6. Residual neural network - Wikipedia

    en.wikipedia.org/wiki/Residual_neural_network

    He and Schmidhuber later designed the LSTM architecture to solve this problem, [4] [21] which has a "cell state" that can function as a generalized residual connection. The highway network (2015) [22] [23] applied the idea of an LSTM unfolded in time to feedforward neural networks, resulting in the highway network. ResNet is equivalent to an ...

  7. Attention Is All You Need - Wikipedia

    en.wikipedia.org/wiki/Attention_Is_All_You_Need

    A key breakthrough was LSTM (1995), [note 1] a RNN which used various innovations to overcome the vanishing gradient problem, allowing efficient learning of long-sequence modelling. One key innovation was the use of an attention mechanism which used neurons that multiply the outputs of other neurons, so-called multiplicative units . [ 11 ]

  8. Hochul asks regulators to reject utility rate hikes - AOL

    www.aol.com/news/hochul-asks-regulators-reject...

    Last week, Con Ed announced it was asking state regulators to hike its electric bills by an average of 11.4% and gas bills by 13.3% starting on Jan. 1, 2026, citing the need to raise revenue to ...

  9. L4 microkernel family - Wikipedia

    en.wikipedia.org/wiki/L4_microkernel_family

    An operating system based on a microkernel like L4 provides services as servers in user space that monolithic kernels like Linux or older generation microkernels include internally. For example, to implement a secure Unix-like system, servers must provide the rights management that Mach included inside the kernel.