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Tag: representations

  • Disentangled representations via score-based variational autoencoders

    Disentangled representations via score-based variational autoencoders arXiv:2512.17127v1 Announce Type: new Abstract: We present the Score-based Autoencoder for Multiscale Inference (SAMI), a method for unsupervised representation learning that combines the theoretical frameworks of diffusion models and VAEs. By unifying their respective evidence lower bounds, SAMI formulates a principled objective that learns representations through score-based guidance of…

  • POSET Representations in Python Can Have a Huge Impact on Business

    POSET Representations in Python Can Have a Huge Impact on Business Discover how POSET indicators transform data into coherent scoring systems, enabling meaningful comparisons while preserving the data’s multi-dimensional semantic structure. The post POSET Representations in Python Can Have a Huge Impact on Business appeared first on Towards Data Science. Andrea D’Agostino Go to original…

  • Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks

    Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks arXiv:2504.06470v1 Announce Type: new Abstract: Ensuring fairness in machine learning is a critical and challenging task, as biased data representations often lead to unfair predictions. To address this, we propose Deep Fair Learning, a framework that integrates nonlinear sufficient dimension reduction with deep…