Enhance Deep Learning Code for Outlier Detection by Integrating LLMs

I’m looking for an experienced developer/researcher in deep learning, outlier detection, and large language models (LLMs) to assist with enhancing a codebase from an article. The article focuses on Deep Learning for Outlier Detection on Tabular and Image Data, and I’d like to integrate LLMs into the existing approach to expand its capabilities.

Project Scope:

Review the provided article and its accompanying code.
Understand the existing deep learning methods used for outlier detection on tabular and image data.
Enhance the code by integrating features leveraging LLMs. Potential directions could include:
Using LLMs to detect outliers by analyzing context within textual metadata.
Creating a natural language interface for explaining anomalies detected in tabular or image data.
Any other innovative approach that combines LLMs and deep learning for outlier detection.
Deliverables:

Modified code with LLM integration.
Documentation explaining the modifications and how LLMs enhance the detection process.
A brief presentation summarizing the approach, changes, and results.
Requirements:

Strong knowledge of Python, TensorFlow/PyTorch, and LLM frameworks like OpenAI’s GPT or Hugging Face Transformers.
Experience in outlier detection on tabular and image data.
Ability to write clear, concise documentation.

I’ll provide the article link and other relevant materials once the project is awarded.

Looking forward to collaborating with talented professionals passionate about cutting-edge AI techniques!