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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. Makridakis Competitions - Wikipedia

    en.wikipedia.org/wiki/Makridakis_Competitions

    The reluctance of most ANN researchers to participate at the time was due to the computationally intensive nature of ANN-based forecasting and the huge time series used for the competition. [1] In 2005, Crone, Nikolopoulos and Hibon organized the NN-3 Competition, using 111 of the time series from the M3-Competition (not the same data, because ...

  4. Recurrent neural network - Wikipedia

    en.wikipedia.org/wiki/Recurrent_neural_network

    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. [1] The building block of RNNs is the recurrent unit. This unit maintains a hidden state, essentially a form of memory, which is updated at ...

  5. Connectionist temporal classification - Wikipedia

    en.wikipedia.org/wiki/Connectionist_temporal...

    Connectionist temporal classification (CTC) is a type of neural network output and associated scoring function, for training recurrent neural networks (RNNs) such as LSTM networks to tackle sequence problems where the timing is variable.

  6. Echo state network - Wikipedia

    en.wikipedia.org/wiki/Echo_state_network

    In early studies, ESNs were shown to perform well on time series prediction tasks from synthetic datasets. [ 1 ] [ 17 ] Today, many of the problems that made RNNs slow and error-prone have been addressed with the advent of autodifferentiation (deep learning) libraries, as well as more stable architectures such as long short-term memory and ...

  7. Timeline of machine learning - Wikipedia

    en.wikipedia.org/wiki/Timeline_of_machine_learning

    Support-Vector Clustering [5] and other kernel methods [6] and unsupervised machine learning methods become widespread. [7] 2010s: Deep learning becomes feasible, which leads to machine learning becoming integral to many widely used software services and applications. Deep learning spurs huge advances in vision and text processing. 2020s

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    Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!

  9. Mamba (deep learning architecture) - Wikipedia

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

    Mamba [a] is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University to address some limitations of transformer models , especially in processing long sequences.