Welcome to NEOSENS, a research project dedicated to evaluating the sensitivity of various machine learning and deep learning algorithms, Quantum Long Short-Term Memory (QLSTM), and Liquid Neural Networks (LNN) to subtle input perturbations. Our primary goal is to assess the robustness of these paradigms in real-world applications, including surgical robotics, self-driving cars where small input noise should not compromise attention mechanisms, taking inspiration from the resilience of biological systems, like the human brain.
- Comparative Analysis: Conduct an extensive comparative analysis of AI architectures, including Long Short-Term Memory (LSTM), Quantum Long Short-Term Memory (QLSTM), Liquid Neural Networks (LNN), and Simple Neural Networks, in practical contexts.
- Sensitivity Evaluation: Investigate how these architectures respond to subtle input perturbations, resembling challenges encountered in self-driving cars and surgical robotics.
- Attention Mechanisms: Assess the efficacy of attention mechanisms in preserving focus and attention amidst noise, inspired by the intricate neurological attention systems.
- Application-Oriented Insights: Generate practical insights into which architecture demonstrates superior noise tolerance without compromising attention mechanisms, making them ideal for safety-critical applications.
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Set up a virtual environment with conda
conda create -n neosens python=3.7 conda activate neosens
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Clone the repository
git clone https://github.com/jaywyawhare/NEOSENS.git
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Install the requirements
pip install -r requirements.txt
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Explore the codebase in the code/ directory to access data preprocessing, model implementations, and evaluation scripts.
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To run experiments and evaluate AI architectures, refer to specific scripts and documentation within the code/ directory.
This project is licensed under the DBaJ-NC-CFL License - see the LICENSE file for details.