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Tag: pruning

  • Sparse Additive Model Pruning for Order-Based Causal Structure Learning

    Sparse Additive Model Pruning for Order-Based Causal Structure Learning arXiv:2602.15306v1 Announce Type: new Abstract: Causal structure learning, also known as causal discovery, aims to estimate causal relationships between variables as a form of a causal directed acyclic graph (DAG) from observational data. One of the major frameworks is the order-based approach that first estimates a…

  • General Pruning Criteria for Fast SBL

    General Pruning Criteria for Fast SBL arXiv:2509.21572v1 Announce Type: new Abstract: Sparse Bayesian learning (SBL) associates to each weight in the underlying linear model a hyperparameter by assuming that each weight is Gaussian distributed with zero mean and precision (inverse variance) equal to its associated hyperparameter. The method estimates the hyperparameters by marginalizing out the…

  • Fairness Pruning: Precision Surgery to Reduce Bias in LLMs

    Fairness Pruning: Precision Surgery to Reduce Bias in LLMs From unjustified shootings to neutral stories: how to fix toxic narratives with selective pruning The post Fairness Pruning: Precision Surgery to Reduce Bias in LLMs appeared first on Towards Data Science. Pere Martra Go to original source

  • Model Compression: Make Your Machine Learning Models Lighter and Faster

    Model Compression: Make Your Machine Learning Models Lighter and Faster Introduction Whether you’re preparing for interviews or building Machine Learning systems at your job, model compression has become a must-have skill. In the era of LLMs, where models are getting larger and larger, the challenges around compressing these models to make them more efficient, smaller,…

  • Hyperflows: Pruning Reveals the Importance of Weights

    Hyperflows: Pruning Reveals the Importance of Weights arXiv:2504.05349v1 Announce Type: new Abstract: Network pruning is used to reduce inference latency and power consumption in large neural networks. However, most existing methods struggle to accurately assess the importance of individual weights due to their inherent interrelatedness, leading to poor performance, especially at extreme sparsity levels. We…

  • Data Pruning MNIST: How I Hit 99% Accuracy Using Half the Data

    Data Pruning MNIST: How I Hit 99% Accuracy Using Half the Data How much data does AI really need? TLDR: Data-centric AI can create more efficient and accurate models. I experimented with data pruning on MNIST¹ to classify handwritten digits. Best runs for “furthest-from-centroid” selection compared to full dataset. Image by author. What if I told you…