Explored Autoencoders for Socioeconomic Modeling

• Day: 2025-02-27
• Time: 01:30 to 02:00
• Project: Dev
• Workspace: WP 1: Strategic / Growth & Development
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Autoencoders, Socioeconomic, Latent Variables, Census Data, Benchmarking

Description

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.

Evidence

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