Results 21 to 30 of about 1,700 (74)
Inaccurate Label Distribution Learning [PDF]
arXiv, 2023 Label distribution learning (LDL) trains a model to predict the relevance of a set of labels (called label distribution (LD)) to an instance. The previous LDL methods all assumed the LDs of the training instances are accurate. However, annotating highly accurate LDs for training instances is time-consuming and very expensive, and in reality the ...
arxiv
Bidirectional Loss Function for Label Enhancement and Distribution Learning [PDF]
arXiv, 2020 Label distribution learning (LDL) is an interpretable and general learning paradigm that has been applied in many real-world applications. In contrast to the simple logical vector in single-label learning (SLL) and multi-label learning (MLL), LDL assigns labels with a description degree to each instance. In practice, two challenges exist in LDL, namely,
arxiv
LDL: A Defense for Label-Based Membership Inference Attacks [PDF]
arXiv, 2022 The data used to train deep neural network (DNN) models in applications such as healthcare and finance typically contain sensitive information. A DNN model may suffer from overfitting. Overfitted models have been shown to be susceptible to query-based attacks such as membership inference attacks (MIAs). MIAs aim to determine whether a sample belongs to
arxiv
Algebraic study of receptor-ligand systems: a dose-response analysis [PDF]
arXiv, 2022 The study of a receptor-ligand system generally relies on the analysis of its dose-response (or concentration-effect) curve, which quantifies the relation between ligand concentration and the biological effect (or cellular response) induced when binding its specific cell surface receptor.
arxiv
Generic mechanism for generating a liquid-liquid phase transition [PDF]
Nature 409, 692-695 (2001), 2001 Recent experimental results indicate that phosphorus, a single-component system, can have two liquid phases: a high-density liquid (HDL) and a low-density liquid (LDL) phase. A first-order transition between two liquids of different densities is consistent with experimental data for a variety of materials, including single-component systems such as ...
arxiv +1 more source
Vector Space Morphology with Linear Discriminative Learning [PDF]
arXiv, 2021 This paper presents three case studies of modeling aspects of lexical processing with Linear Discriminative Learning (LDL), the computational engine of the Discriminative Lexicon model (Baayen et al., 2019). With numeric representations of word forms and meanings, LDL learns to map one vector space onto the other, without being informed about any ...
arxiv
Effect of probucol on LDL oxidation and atherosclerosis in LDL receptor-deficient mice [PDF]
Journal of Lipid Research, 1998 Probucol is a powerful inhibitor of atherosclerosis in a number of animal models. However, it is unknown whether this is due to the strong antioxidant protection of low density lipoprotein (LDL), to antioxidant effects in the artery wall, or to cellular effects not shared by other antioxidants. To investigate whether murine models are suitable to study
Bird, David A +5 more
openaire +5 more sources
TabMixer: Excavating Label Distribution Learning with Small-scale Features [PDF]
arXiv, 2022 Label distribution learning (LDL) differs from multi-label learning which aims at representing the polysemy of instances by transforming single-label values into descriptive degrees. Unfortunately, the feature space of the label distribution dataset is affected by human factors and the inductive bias of the feature extractor causing uncertainty in the ...
arxiv
Effects of coexpression of the LDL receptor and apoE on cholesterol metabolism and atherosclerosis in LDL receptor-deficient mice [PDF]
Journal of Lipid Research, 2001 The low density lipoprotein receptor (LDLR) plays a major role in regulation of plasma cholesterol levels as a ligand for apolipoprotein B-100 and apolipoprotein E (apoE). Consequently, LDLR-deficient mice fed a Western-type diet develop significant hypercholesterolemia and atherosclerosis.
Masa-aki Kawashiri +5 more
openaire +4 more sources
LDL: Line Distance Functions for Panoramic Localization [PDF]
arXiv, 2023 We introduce LDL, a fast and robust algorithm that localizes a panorama to a 3D map using line segments. LDL focuses on the sparse structural information of lines in the scene, which is robust to illumination changes and can potentially enable efficient computation.
arxiv