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Doing a Machine Learning PhD While Working in Japan

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  1. Hidden Technical Debt in Machine Learning Systems

    This paper introduces the concept of technical debt in machine learning systems, arguing that ML systems accrue unique and significant maintenance costs beyond traditional software engineering. It identifies several ML-specific risk factors like entanglement, hidden feedback loops, and data dependencies that erode system boundaries and increase long-term operational expenses.

    Hall of Famenips.cc25 minpaper
  2. D-JEPA: A Decision-Aligned Latent World Model

    D-JEPA is a latent world model designed to bridge the gap between predicted outcomes and actual decision success in robotics. It learns decision-relevant relationships from executed actions, improving action selection by aligning latent space geometry with real-world results.

    Hugging Face Daily Papersarxiv.org1 minpaper
  3. Local Support Learning

    Local Support Learning (LSL) is a framework designed to mitigate catastrophic forgetting in large pre-trained models by augmenting gradient-based training. It uses a weight adapter and a GMM-based gating function to localize updates to the current training distribution, retaining prior capabilities without needing old data.

    Hugging Face Daily Papersarxiv.org1 minpaper