For 10 points each, name these computational models often used in deep learning, named for their similarity to the structure of the human brain.
In 2014, Srivastava et. al introduced the idea of randomly removing components of these devices to improve their performance, a technique known as dropout. Some of these devices involve passing a filter over various parts of the input, and others have internal directed cycles which function as memory. Those are the convolutional and recurrent types of these devices, respectively. The McCulloch-Pitts model provided the inspiration for these devices, and they are the subject of an algorithm that (*) iteratively uses the chain rule to compute a loss, the backpropagation algorithm. These devices consist of layers of hidden units stacked in between an input and output layer, with an activation function between each layer. For 10 points each, name these computational models often used in deep learning, named for their similarity to the structure of the human brain.
In 2014, Srivastava et. al introduced the idea of randomly removing components of these devices to improve their performance, a technique known as dropout.
neural networks