📅 2025-02-27 — Session: Developed MLP-Based Autoencoder for Census Data
🕒 00:15–01:15
🏷️ Labels: Autoencoder, MLP, Thesis, Census Data, Deep Learning
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to advance the development of a Multilayer Perceptron (MLP) based autoencoder tailored for a large-scale census dataset, focusing on architectural decisions, embedding dimension selection, and thesis proposal development.
Key Activities
- Outlined key architectural decisions for implementing an MLP-based autoencoder, emphasizing batch normalization and mean squared error as the loss function.
- Discussed embedding dimension selection for socioeconomic data, considering intrinsic dimensionality and computational constraints.
- Reflected on the use of autoencoders for population data visualization and the challenges in data encoding and representation.
- Developed a structured thesis proposal, including core research questions and objectives for optimizing the autoencoder.
- Expanded on the baseline MLP autoencoder section of the thesis, detailing model selection and training strategies.
- Reviewed a timeline of key developments in autoencoders and neural networks.
- Explored the relationship between PCA and autoencoders, and discussed geospatial and temporal considerations in autoencoding census data.
Achievements
- Established a comprehensive framework and proposal for the MLP-based autoencoder thesis.
- Identified key challenges and considerations in embedding and data representation.
- Developed a detailed plan for the thesis structure and content.
Pending Tasks
- Implement the proposed architectural adjustments for handling geospatial and temporal data in the autoencoder.
- Finalize the embedding dimension selection based on further analysis and testing.