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  2. 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 ...

  3. 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.

  4. Residual neural network - Wikipedia

    en.wikipedia.org/wiki/Residual_neural_network

    Examples include: [17] [18] Lang and Witbrock (1988) [19] trained a fully connected feedforward network where each layer skip-connects to all subsequent layers, like the later DenseNet (2016). In this work, the residual connection was the form x ↦ F ( x ) + P ( x ) {\displaystyle x\mapsto F(x)+P(x)} , where P {\displaystyle P} is a randomly ...

  5. Feedforward neural network - Wikipedia

    en.wikipedia.org/wiki/Feedforward_neural_network

    Simplified example of training a neural network for object detection: The network is trained on multiple images depicting either starfish or sea urchins, which are correlated with "nodes" that represent visual features. The starfish match with a ringed texture and a star outline, whereas most sea urchins match with a striped texture and oval shape.

  6. 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]

  7. Feature learning - Wikipedia

    en.wikipedia.org/wiki/Feature_learning

    With analogous results in masked prediction [42] and clustering, [43] video representation learning approaches are often similar to image techniques but must utilize the temporal sequence of video frames as an additional learned structure. Examples include VCP, which masks video clips and trains to choose the correct one given a set of clip ...

  8. How Democrats Are Faring In First Tests Of The Trump Backlash

    data.huffingtonpost.com/2017/special-elections

    Between now and November 2017, there will be special elections for 19 more state legislature seats, four U.S. House seats and one U.S. Senate seat. Some Democratic candidates in U.S. House races are generating excitement, including the Bernie Sanders-backing banjo player Rob Quist in Montana and 30-year-old documentary filmmaker Jon Ossoff in ...

  9. Time delay neural network - Wikipedia

    en.wikipedia.org/wiki/Time_delay_neural_network

    Video has a temporal dimension that makes a TDNN an ideal solution to analysing motion patterns. An example of this analysis is a combination of vehicle detection and recognizing pedestrians. [ 15 ] When examining videos, subsequent images are fed into the TDNN as input where each image is the next frame in the video.