📅 2025-02-27 — Session: Explored Autoencoders for Socioeconomic Modeling
🕒 01:30–02:00
🏷️ Labels: Autoencoders, Socioeconomic, Latent Variables, Census Data, Benchmarking
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore the application of autoencoders in socioeconomic modeling, focusing on understanding latent structures and their implications for analyzing census data.
Key Activities
- Exploration of Sparse Autoencoders: Investigated the connection between sparse autoencoders, latent variables, and generative modeling in the context of census data.
- Understanding Log-Likelihood: Delved into the relationship between autoencoders and probabilistic generative models, focusing on log-likelihood decomposition.
- Socioeconomic Statistics: Discussed the use of log-likelihood decomposition in estimating socioeconomic statistics like the Gini index.
- Benchmarking Plan: Outlined a plan for benchmarking the reconstruction of socioeconomic traits using learned representations.
- Data Integration: Planned the integration of census and survey data for training autoencoders, emphasizing income prediction and structured latent spaces.
Achievements
- Clarified the role of latent structures in socioeconomic analysis using autoencoders.
- Developed a comprehensive plan for benchmarking socioeconomic trait reconstruction.
Pending Tasks
- Implement the outlined benchmarking plan to evaluate model performance.
- Integrate census and survey data for practical autoencoder training.