Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM)

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The AWD-LSTM has been dominating the state-of-the-art language modeling. All the top research papers on word-level models incorporate AWD-LSTMs. And it has shown great results on character-level models as well. ... It uses DropConnect and a variant of Average-SGD (NT-ASGD) along with several other well-known Regularization strategies. What makes the AWD-LSTM great? | Yashu Seth

The Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM) is a specific variant of the Long Short-Term Memory (LSTM) neural network architecture, commonly used in the field of artificial intelligence (AI) for tasks such as Natural Language Processing (NLP) and sequence modeling.

  • AWD-LSTM architecture: The AWD-LSTM architecture combines the concepts of LSTM, weight-dropping, and average-stochastic gradient descent to enhance the training and generalization capabilities of the model. LSTMs are a type of Recurrent Neural Network (RNN) that can effectively capture long-term dependencies in sequential data.
  • Weight-dropping: Weight-dropping is a technique applied to LSTM networks to regularize the model during training. It involves randomly dropping out (setting to zero) a fraction of the recurrent connections between LSTM cells during each training step. This dropout of connections helps prevent overfitting and encourages the model to generalize better.
  • Average-stochastic gradient descent: AWD-LSTM introduces the concept of average-stochastic gradient descent, which is a modification of the standard stochastic gradient descent optimization algorithm. Instead of updating the model parameters based on a single randomly sampled training example, average-stochastic gradient descent computes the average gradient over multiple randomly selected examples. This averaging reduces the effect of noise and variance in the gradients, leading to more stable updates and better convergence.
  • Benefits of AWD-LSTM: AWD-LSTM offers several benefits in AI applications. By utilizing LSTM architecture, it can effectively model and process sequential data with long-term dependencies. The weight-dropping technique helps regularize the model and improve its generalization capabilities. The use of average-stochastic gradient descent reduces the variance in the gradients and enhances the stability of the training process.
  • Applications: AWD-LSTM has been successfully applied to various Natural Language Processing (NLP) tasks, including language modeling, Sentiment Analysis, machine translation, and text generation. It has also shown promising results in other sequence modeling tasks, such as Speech Recognition and time series prediction.