Information about Test

  1. Feature learning

    are learned using labeled input data. Examples include supervised neural networks, multilayer perceptron and (supervised) dictionary learning. In unsupervised

  2. Attention

    Q, Hua G, Zheng N (2018). "Attention-Based Temporal Weighted Convolutional Neural Network for Action Recognition". IFIP Advances in Information and Communication

  3. CIFAR-10

    try different algorithms to see what works. Various kinds of convolutional neural networks tend to be the best at recognizing the images in CIFAR-10. CIFAR-10

  4. Recursive neural network

    include Graph Neural Network (GNN), Neural Network for Graphs (NN4G), and more recently convolutional neural networks for graphs. Goller, C.; Küchler, A

  5. Google Translate

    languages, with the release of a new implementation that utilizes convolutional neural networks, and also enhanced the speed and quality of Conversation Mode

  6. Long short-term memory

    artificial recurrent neural network (RNN) architecture used in the field of deep learning. Unlike standard feedforward neural networks, LSTM has feedback

  7. Deep Image Prior

    type of convolutional neural network used to enhance a given image with no prior training data other than the image itself. A neural network is randomly

  8. Yann LeCun

    recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets. He is also one of the main creators

  9. Support vector machine

    for Multiclass Support Vector Machines" (PDF). IEEE Transactions on Neural Networks. 13 (2): 415–25. doi:10.1109/72.991427. PMID 18244442. Platt, John;

  10. SpaCy

    learning library Thinc. Using Thinc as its backend, spaCy features convolutional neural network models for part-of-speech tagging, dependency parsing, text categorization