Information about Test

  1. Relation network

    aimlexchange.com/search/wiki/page/Relation_network

    spatial, translation-invariant properties is explicitly part of convolutional neural networks (CNN). The data to be considered can be presented as a simple

  2. ImageNet

    aimlexchange.com/search/wiki/page/ImageNet

    one thousand non-overlapping classes. On 30 September 2012, a convolutional neural network (CNN) called AlexNet achieved a top-5 error of 15.3% in the ImageNet

  3. Vision processing unit

    aimlexchange.com/search/wiki/page/Vision_processing_unit

    suitability for running machine vision algorithms such as CNN (convolutional neural networks), SIFT (Scale-invariant feature transform) and similar. They

  4. Movidius

    aimlexchange.com/search/wiki/page/Movidius

    uses the Myriad 2. Vision processing unit MPSoC Coprocessor Convolutional neural network Newenham, Pamela. "Sean Mitchell and David Moloney, Movidius"

  5. Coding theory

    aimlexchange.com/search/wiki/page/Coding_theory

    the output of the system convolutional encoder, which is the convolution of the input bit, against the states of the convolution encoder, registers. Fundamentally

  6. Robot navigation

    aimlexchange.com/search/wiki/page/Robot_navigation

    Navigator. Some recent outdoor navigation algorithms are based on convolutional neural network and machine learning, and are capable of accurate turn-by-turn

  7. Kernel method

    aimlexchange.com/search/wiki/page/Kernel_method

    (SVM) in the 1990s, when the SVM was found to be competitive with neural networks on tasks such as handwriting recognition. The kernel trick avoids the

  8. Deep reinforcement learning

    aimlexchange.com/search/wiki/page/Deep_reinforcement_learning

    benefit of end-to-end reinforcement learning as well as that of convolutional neural networks. However, DRL failed on the game Montezuma's Revenge (DRL even

  9. Receptive field

    aimlexchange.com/search/wiki/page/Receptive_field

    also used in the context of artificial neural networks, most often in relation to convolutional neural networks (CNNs). When used in this sense, the term

  10. Perceptron

    aimlexchange.com/search/wiki/page/Perceptron

    caused the field of neural network research to stagnate for many years, before it was recognised that a feedforward neural network with two or more layers

Contents