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swethatanamala.github.io | ||
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coen.needell.org
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| | | | | In my last post on computer vision and memorability, I looked at an already existing model and started experimenting with variations on that architecture. The most successful attempts were those that use Residual Neural Networks. These are a type of deep neural network built to mimic specific visual structures in the brain. ResMem, one of the new models, uses a variation on ResNet in its architecture to leverage that optical identification power towards memorability estimation. | |
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nlp.seas.harvard.edu
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| | | | | The Annotated Transformer | |
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dennybritz.com
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| | | | | Recurrent Neural Networks (RNNs) are popular models that have shown great promise in manyNLP tasks. | |
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wtfleming.github.io
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| | | [AI summary] This post discusses achieving 99.1% accuracy in binary image classification of cats and dogs using an ensemble of ResNet models with PyTorch. | ||