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Showing 1 to 4 of 4 for “"Machine Learning for Systems"”.

  1. Strong and weak principles of Bayesian machine learning for systems neuroscience

    … of behaviour, while also pushing the need for new techniques to analyse and model these large-scale datasets. Inspiration for such tools can be found in the Bayesian machine learning literature, which provides a set of principled techniques that allow us to perform inference in complex …

    cambridge Repository record for Strong and weak principles of Bayesian machine learning for systems neuroscience (opens in a new tab)

  2. Learned String Index Structures for In-Memory Databases

    Within the field of machine learning for systems, learning-based methods have brought new perspective to indexing by reframing it as a cumulative distribution function (CDF) modeling problem. The burgeoning field, despite its nascence, has brought with it many opportunities and efficiencies. …

    mit Repository record for Learned String Index Structures for In-Memory Databases (opens in a new tab)

  3. A framework for intelligence augmented computing systems

    Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01

    uiuc Repository record for A framework for intelligence augmented computing systems (opens in a new tab)

  4. Design and implementation of learning-based storage systems: a holistic approach

    Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms

    uiuc Repository record for Design and implementation of learning-based storage systems: a holistic approach (opens in a new tab)