Developed MLP-Based Autoencoder for Census Data

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Developed MLP-Based Autoencoder for Census Data

  • Day: 2025-02-27
  • Time: 00:15 to 01:15
  • Project: Teaching
  • Workspace: WP 1: Strategic / Growth & Development
  • Status: In Progress
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Autoencoder, MLP, Census Data, Thesis, Deep Learning

Description

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.

Evidence

  • source_file=2025-02-27.sessions.jsonl, line_number=6, event_count=0, session_id=8f4ddf9387caaf8a6135836189344cb1983ced17f8ecf69247ef76d38dac4835
  • event_ids: []

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