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This research work focuses on comparing the existing approaches to explain the decisions of models trained using time-series data and proposing the best-fit method that generates explanations for a deep neural network. The proposed approach is used specifically for explaining LSTM networks for anomaly detection task in time-series data (satellite telemetry data).

Home Page: https://github.com/Jithsaavvy/Explaining-deep-learning-models-for-detecting-anomalies-in-time-series-data-RnD-project

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anomaly-detection lstm-neural-networks regression rnn satellite sequential-models telemetry-data tensorflow time-series

explaining-deep-learning-models-for-detecting-anomalies-in-time-series-data-rnd-project's Introduction

Hi 👋, I'm Jithin Sasikumar from Germany

Master's student in Autonomous Systems [ML/DL] & developer. Experienced in deep learning, speech technologies, NLP and MLOps.

🔭 I am really passionate about programming, Machine Learning, Deep Learning, ASR, NLP and software development.
🔭 I’m currently working on MLOps, Deep learning, Automatic Speech Recognition and Language modeling.
🔭 I'm always keen on amalgamating research, development, and deployment (obsessed with deploying models into production).
👨‍💻 All of my projects are available @ https://github.com/Jithsaavvy?tab=repositories
📫 Reach me @ [email protected]
📄 Know about my experiences @ https://www.linkedin.com/in/jithin-sasikumar/

Interests:

ASR | MLOps | NLP | Automated CI/CD for end-to-end ML Pipelines | Conversational AI | Deep Neural Networks (RNN, LSTM, CNN, End to End models, Attention models, Acoustic models) | Federated Learning

Skills:

Languages: Python | Groovy | Java (Intermediate) | HTML | CSS | YAML | C++ | SQL
ML/DL Frameworks: Tensorflow | Tensorflow Serving | Tensorflow Federated | Keras | Scikit-learn | Pytorch (Intermediate)
Cloud Technologies: AWS (Amazon S3, Amazon SageMaker, Amazon ECR, Amazon EC2) | Heroku
Container Orchestration: Kubernetes
Data Warehouse: Snowflake
Tools: Docker | MLflow | Poetry | Flask | Hydra | JFrog | Pytest | Jupyter Notebooks
CI/CD Tools & Version Control: Git | GitHub | GitHub Actions | GitLab | GitLab CI/CD
Workflow Orchestration: Apache Airflow
Build Automation: Gradle
Operating Systems: Linux | Windows

Connect with me:

explaining-deep-learning-models-for-detecting-anomalies-in-time-series-data-rnd-project's People

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nankezi1 adg4050 ripankundu akash-kadali lbaoxi jiangnguyen

explaining-deep-learning-models-for-detecting-anomalies-in-time-series-data-rnd-project's Issues

Questions about the results obtained by XAI method

Hello, I hope to exchange some questions with you. I found a strange phenomenon. For the same model, the same training sample and test sample, other operations are identical. Theoretically, the values obtained by using the XAI method (like Shap) to evaluate the interpretability of the model should be the same. However, I retrained a new model, and the interpretability values obtained are completely different from those obtained from the previous model. Does anyone know why this happens? The interpretability value is completely unstable, and the results cannot be reproduced. Unless I completely save this model after training it, and then reload this parameter, the results will be the same. Does anyone know why

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