In this repository, I investigate the role of Residual Connections in Transformers, with a primary focus on analyzing gradient flow behavior in deep architectures.

Title: Residual Connections and Gradient Flow Stability in Deep Transformers

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Sections

1. Hypothesis
2. Experimental Setup
3. Metrics Measured
4. Results (Plots)
5. Interpretation
6. Future Work

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This work will include detailed mathematical analysis, accompanied by clear visualizations of gradient flow and comparative studies.

Planned Additions

• Layer-wise gradient norm plots
• Comparative analysis (with vs. without residual connections)
• Learning rate stability analysis
• Dedicated mathematical explanation section
• A research paper intended for submission to arXiv

Core Problem Statement I'm Working On:

How do residual connections influence the stability of optimization in deep Transformers, and what measurable impact do they have on gradient propagation across increasing network depth?