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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]
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.
It was the first implementation of an Artificial Intelligence (AI) machine. It differs from the Perceptron which is a software architecture proposed in 1943 by Warren McCulloch and Walter Pitts, [1] which was also employed in Mark I, and enhancements of which have continued to be an integral part of cutting edge AI technologies like the ...
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]
The Gamba perceptron machine was similar to the perceptron machine of Rosenblatt. Its input were images. The image is passed through binary masks (randomly generated) in parallel. Behind each mask is a photoreceiver that fires if the input, after masking, is bright enough. The second layer is made of standard perceptron units.
The gate can be represented with the plus sign (+) because it can be used for logical addition. [1] Equivalently, an OR gate finds the maximum between two binary digits, just as the AND gate finds the minimum. [2] Together with the AND gate and the NOT gate, the OR gate is one of three basic logic gates from which any Boolean circuit may
An AOI21 logic gate in CMOS using a complex gate (left) and standard gates (right) AND-OR-invert (AOI) and OAI gates can be readily implemented in CMOS circuitry. AOI gates are particularly advantaged in that the total number of transistors (or gates) is less than if the AND, NOT, and OR functions were implemented separately.
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.