Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism

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• Recurrent Neural Network (RNN) Variants:
  • Long Short-Term Memory (LSTM)
  • Gated Recurrent Unit (GRU)
  • Bidirectional Long Short-Term Memory (BI-LSTM)
  • Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism
  • Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM)
  • Hopfield Network (HN)
• Attention Mechanism ...Transformer Model ...Generative Pre-trained Transformer (GPT)
• Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification | Peng Zhou, Wei Shi, Jun Tian, Zhenyu Qi, Bingchen Li, Hongwei Hao, Bo Xu
• Taming Recurrent Neural Networks for Better Summarization | Abigail See
• Case Studies
  • Risk, Compliance and Regulation
    • Cybersecurity
      • Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection | Pacific Northwest National Laboratory (PNNL)
      • Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams | Pacific Northwest National Laboratory (PNNL)
      • SafeKit | Pacific Northwest National Laboratory (PNNL) - GitHub
• Generative AI ... OpenAI's ChatGPT ... Perplexity ... Microsoft's BingAI ... You ...Google's Bard ... Baidu's Ernie

By incorporating attention variants into an RNN (language models) opportunities are created for model introspection and analysis without sacrificing performance. Attention-equipped LSTM models have been used to improve performance on complex sequence modeling tasks. Attention provides a dynamic weighted average of values from different points in a calculation during the processing of a sequence to provide long term context for downstream discriminative or generative prediction. Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection | Western Washington University and Pacific Northwest National Laboratory (PNNL