📅 2025-02-27 — Session: Developed MLP-Based Autoencoder for Census Data
🕒 00:15–01:15
🏷️ Labels: Autoencoder, MLP, Census Data, Thesis, Deep Learning
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal:
The session aimed to develop and refine an MLP-based autoencoder tailored for a large census dataset, focusing on architectural decisions and embedding dimension selection.
Key Activities:
- Architectural Decisions: Discussed key decisions for implementing a Multilayer Perceptron (MLP) based autoencoder, emphasizing batch normalization and mean squared error as the loss function.
- Embedding Dimensions: Explored considerations for selecting embedding dimensions for a 40M census dataset, considering intrinsic dimensionality and computational constraints.
- Visualization Techniques: Reflected on the application of autoencoders in visualizing population data, utilizing latent space for pattern recognition.
- Data Encoding Challenges: Addressed challenges in encoding mixed-type census data, highlighting the importance of encoding methods and generalizability.
- Thesis Development: Outlined a structured thesis proposal focusing on optimizing the MLP-based autoencoder for socioeconomic data, including core research questions and objectives.
Achievements:
- Established a comprehensive framework for the MLP-based autoencoder, including model selection, training, optimization, and evaluation strategies.
- Developed a timeline of key developments in autoencoders, enhancing the thesis’s historical context.
- Explored the relationship between PCA and autoencoders, providing insights into their extensions and relevance.
Pending Tasks:
- Further explore geospatial and temporal considerations in autoencoding census data, focusing on spatial dependencies and temporal trends.
- Expand the ‘Baseline MLP Autoencoder’ section of the thesis with detailed content and structure.