Core Neural Network Innovations

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Core Neural Network Innovations

Ilya 30u30

  1. Recurrent Neural Network Regularization - Enhancement to LSTM units for better overfitting prevention.
  2. Pointer Networks - Novel architecture for solving problems with discrete token outputs.
  3. Deep Residual Learning for Image Recognition - Improvements for training very deep networks through residual learning.
  4. Identity Mappings in Deep Residual Networks - Enhancements to deep residual networks through identity mappings.
  5. Neural Turing Machines - Combining neural networks with external memory resources for enhanced algorithmic tasks.
  6. Attention Is All You Need - Introducing the Transformer architecture solely based on attention mechanisms.