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Tag: loss

  • YOLOv1 Loss Function Walkthrough: Regression for All

    YOLOv1 Loss Function Walkthrough: Regression for All An explanation of how YOLOv1 measures the correctness of its object detection and classification predictions The post YOLOv1 Loss Function Walkthrough: Regression for All appeared first on Towards Data Science. Muhammad Ardi Go to original source

  • Hellinger loss function for Generative Adversarial Networks

    Hellinger loss function for Generative Adversarial Networks arXiv:2512.12267v1 Announce Type: new Abstract: We propose Hellinger-type loss functions for training Generative Adversarial Networks (GANs), motivated by the boundedness, symmetry, and robustness properties of the Hellinger distance. We define an adversarial objective based on this divergence and study its statistical properties within a general parametric framework. We…

  • Support Vector Machine Classifier with Rescaled Huberized Pinball Loss

    Support Vector Machine Classifier with Rescaled Huberized Pinball Loss arXiv:2511.22065v1 Announce Type: new Abstract: Support vector machines are widely used in machine learning classification tasks, but traditional SVM models suffer from sensitivity to outliers and instability in resampling, which limits their performance in practical applications. To address these issues, this paper proposes a novel rescaled…

  • Time dependent loss reweighting for flow matching and diffusion models is theoretically justified

    Time dependent loss reweighting for flow matching and diffusion models is theoretically justified arXiv:2511.16599v1 Announce Type: new Abstract: This brief note clarifies that, in Generator Matching (which subsumes a large family of flow matching and diffusion models over continuous, manifold, and discrete spaces), both the Bregman divergence loss and the linear parameterization of the generator…

  • A Deep Bayesian Nonparametric Framework for Robust Mutual Information Estimation

    A Deep Bayesian Nonparametric Framework for Robust Mutual Information Estimation arXiv:2503.08902v1 Announce Type: new Abstract: Mutual Information (MI) is a crucial measure for capturing dependencies between variables, but exact computation is challenging in high dimensions with intractable likelihoods, impacting accuracy and robustness. One idea is to use an auxiliary neural network to train an MI…

  • Near-Optimal Algorithms for Omniprediction

    Near-Optimal Algorithms for Omniprediction arXiv:2501.17205v1 Announce Type: new Abstract: Omnipredictors are simple prediction functions that encode loss-minimizing predictions with respect to a hypothesis class $H$, simultaneously for every loss function within a class of losses $L$. In this work, we give near-optimal learning algorithms for omniprediction, in both the online and offline settings. To begin,…

  • An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints

    An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints arXiv:2412.08060v1 Announce Type: new Abstract: We study Online Convex Optimization (OCO) with adversarial constraints, where an online algorithm must make repeated decisions to minimize both convex loss functions and cumulative constraint violations. We focus on a setting where the algorithm has access to predictions of…