Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism
- Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)
- Attention Mechanism/Model
- 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
- 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
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
