Demos Applications Components Optimizers Experiments Datasets

Experiments

Java 8 Neural Networks with CuDNN and Aparapi


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Experiments

These are various research-related notebooks:

  1. com.simiacryptus.mindseye.labs.matrix.OptimizerComparison

    Description: The type Optimizer comparison.

    1. Research

      Description: The type Compare qqn.

      Status: OK

  2. com.simiacryptus.mindseye.test.data.CIFAR10

    Description: Mirrored from https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html For more information, and for citation, please see:Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, 2009. https:\/\/www.cs.toronto.edu\/~kriz\/learning-features-2009-TR.pdf

    1. OWL_QN

      Description: The type Owl qn.

      Status: OK

    2. QQN

      Description: The type Qqn.

      Status: OK

    3. SGD

      Description: The type Sgd.

      Status: OK

  3. com.simiacryptus.mindseye.test.data.Caltech101

    Description: Caltech 101 Images When using, please cite: L. Fei-Fei, R. Fergus and P. Perona. Learning generative visual modelsfrom few training examples: an incremental Bayesian approach tested on 101 object categories. IEEE. CVPR 2004,Workshop on Generative-Model Based Vision. 2004 For more information see http:\/\/www.vision.caltech.edu\/Image_Datasets\/Caltech101\/

    1. QQN

      Description: The type Qqn.

      Status: ArrayIndexOutOfBoundsException

  4. com.simiacryptus.mindseye.test.data.MNIST

    Description: References: [LeCun et al., 1998a] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied todocument recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998.See Also: http:\/\/yann.lecun.com\/exdb\/mnist\/

    1. OWL_QN

      Description: The type Owl qn.

      Status: OK

    2. QQN

      Description: The type Qqn.

      Status: OK

    3. SGD

      Description: The type Sgd.

      Status: OK