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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]
Plugging these two equations into the training loop turn it into the dual perceptron algorithm. Finally, we can replace the dot product in the dual perceptron by an arbitrary kernel function, to get the effect of a feature map Φ without computing Φ(x) explicitly for any samples. Doing this yields the kernel perceptron algorithm: [4]
Mark I Perceptron machine, the first implementation of the perceptron algorithm. It was connected to a camera with 20×20 cadmium sulfide photocells to make a 400-pixel image. The main visible feature is the sensory-to-association plugboard, which sets different combinations of input features.
The perceptron uses the Heaviside step function as the activation function (), and that means that ′ does not exist at zero, and is equal to zero elsewhere, which makes the direct application of the delta rule impossible.
Other types of gates, namely AND gates and OR gates, can be constructed using a majority gate with fixed polarization on one of its inputs. A NOT gate, on the other hand, is fundamentally different from the majority gate, as shown in Figure 6. The key to this design is that the input is split and both resulting inputs impinge obliquely on the ...
For n = 3 the ternary median operator can be expressed using conjunction and disjunction as xy + yz + zx. For an arbitrary n there exists a monotone formula for majority of size O(n 5.3). This is proved using probabilistic method. Thus, this formula is non-constructive. [3] Approaches exist for an explicit formula for majority of polynomial size:
In 2001, [26] the first perceptron predictor was presented that was feasible to implement in hardware. The first commercial implementation of a perceptron branch predictor was in AMD's Piledriver microarchitecture. [27] The main advantage of the neural predictor is its ability to exploit long histories while requiring only linear resource growth.
An inverter (NOT) gate is logically reversible because it can be undone. The NOT gate may however not be physically reversible, depending on its implementation. The exclusive or (XOR) gate is irreversible because its two inputs cannot be unambiguously reconstructed from its single output, or alternatively, because information erasure is not ...