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  2. Mamba (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Mamba_(deep_learning...

    To enable handling long data sequences, Mamba incorporates the Structured State Space sequence model (S4). [2] S4 can effectively and efficiently model long dependencies by combining continuous-time, recurrent, and convolutional models. These enable it to handle irregularly sampled data, unbounded context, and remain computationally efficient ...

  3. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...

  4. 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 ]

  5. Longest common subsequence - Wikipedia

    en.wikipedia.org/wiki/Longest_common_subsequence

    They match, so G is appended to the upper left sequence, LCS(R 0, C 1), which is (ε), giving (εG), which is (G). For LCS(R 1, C 3), G and C do not match. The sequence above is empty; the one to the left contains one element, G. Selecting the longest of these, LCS(R 1, C 3) is (G). The arrow points to the left, since that is the longest of the ...

  6. Long short-term memory - Wikipedia

    en.wikipedia.org/wiki/Long_short-term_memory

    In theory, classic RNNs can keep track of arbitrary long-term dependencies in the input sequences. The problem with classic RNNs is computational (or practical) in nature: when training a classic RNN using back-propagation, the long-term gradients which are back-propagated can "vanish", meaning they can tend to zero due to very small numbers creeping into the computations, causing the model to ...

  7. Our 20 Most Popular Chicken Dinners of 2024 - AOL

    www.aol.com/20-most-popular-chicken-dinners...

    These flavorful chicken dinner recipes highlight one of the most beloved protein sources and have been popular among EatingWell readers in 2024.

  8. Recurrent neural network - Wikipedia

    en.wikipedia.org/wiki/Recurrent_neural_network

    Recurrent neural networks (RNNs) are a class of artificial neural network commonly used for sequential data processing. Unlike feedforward neural networks, which process data in a single pass, RNNs process data across multiple time steps, making them well-adapted for modelling and processing text, speech, and time series.

  9. Border agents brace for shutdown, work without pay during ...

    www.aol.com/border-agents-brace-shutdown-without...

    EL PASO, Texas – If the federal government shuts down Friday, U.S. border crossings will stay open and border agents will keep working through the holidays – without pay, at least temporarily. ...