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Showing 1 to 4 of 4 for “"Sparse classification"”.

  1. Towards an Artificial Neuroscience: Analytics for Language Model Interpretability

    … systems modeling. In Chapter 2, we adapt optimal sparse classification methods to neural network probing, allowing us to study how concepts are represented across multiple neurons. This sparse probing technique reveals both monosemantic neurons (dedicated to single concepts) and polysemantic …

    mit Repository record for Towards an Artificial Neuroscience: Analytics for Language Model Interpretability (opens in a new tab)

  2. Sparse Learning using Discrete Optimization: Scalable Algorithms and Statistical Insights

    … learning and high-dimensional statistics. While sparse learning problems can be naturally modeled using discrete optimization, computational challenges have historically shifted the focus towards alternatives based on continuous optimization and heuristics. Recently, growing evidence suggests …

    mit Repository record for Sparse Learning using Discrete Optimization: Scalable Algorithms and Statistical Insights (opens in a new tab)

  3. Sparsity in Machine Learning: Theory and Applications

    … It offers a rigorous framework to build sparse models and has proved to provide more accurate and sparse models than other approaches including the ones using sparsity-inducing regularization norms. This thesis focuses on the application of integer optimization to address sparsity …

    mit Repository record for Sparsity in Machine Learning: Theory and Applications (opens in a new tab)

  4. Computational seismic interpretation using attention models, texture dissimilarity, and learning

    The exploration of oil and gas is a vital part of today's increasing power demands to meet the energy we need to power our homes, businesses, and transportation. Oil and gas explorers use seismic surveys, both onshore and offshore, to produce detailed images of the various rock types, layers, and …

    gatech Repository record for Computational seismic interpretation using attention models, texture dissimilarity, and learning (opens in a new tab)