Robustness of linear mixed‐effects models to violations of distributional assumptions
Methods in Ecology and Evolution, 2020 Linear mixed‐effects models are powerful tools for analysing complex datasets with repeated or clustered observations, a common data structure in ecology and evolution. Mixed‐effects models involve complex fitting procedures and make several assumptions,
Holger Schielzeth +2 more
exaly +2 more sources
Gender equality in climate policy and practice hindered by assumptions
Nature Climate Change, 2021 Jacqueline D Lau +2 more
exaly +2 more sources
USING STABLE ISOTOPES TO ESTIMATE TROPHIC POSITION: MODELS, METHODS, AND ASSUMPTIONS
Ecology, 2002 David M Post
exaly +2 more sources
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions [PDF]
International Conference on Learning Representations, 2022 We provide theoretical convergence guarantees for score-based generative models (SGMs) such as denoising diffusion probabilistic models (DDPMs), which constitute the backbone of large-scale real-world generative models such as DALL$\cdot$E 2.
Sitan Chen +5 more
semanticscholar +1 more source
Foundations and Assumptions of Spiritual Journeying of Baḥya Ben Joseph ibn Paḳūda in Al-Hidāyah ilá farāʼīḍ al-qulūb [PDF]
مطالعات ادیان و عرفان تطبیقی, 2022 Undoubtedly, the conviction of a mystic's Spiritual Journeying is the result of a strong foundation of his epistemological Principles and assumptions. The findings of this study, which is based on library studies and with a descriptive-analytical method,
Seyed ali Mostajaboldavati +1 more
doaj +1 more source
Editing Implicit Assumptions in Text-to-Image Diffusion Models [PDF]
IEEE International Conference on Computer Vision, 2023 Text-to-image diffusion models often make implicit assumptions about the world when generating images. While some assumptions are useful (e.g., the sky is blue), they can also be outdated, incorrect, or reflective of social biases present in the training
Hadas Orgad +2 more
semanticscholar +1 more source
Convergence of Adam Under Relaxed Assumptions [PDF]
Neural Information Processing Systems, 2023 In this paper, we provide a rigorous proof of convergence of the Adaptive Moment Estimate (Adam) algorithm for a wide class of optimization objectives.
Haochuan Li, A. Jadbabaie, A. Rakhlin
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Reliability in Distribution Modeling—A Synthesis and Step-by-Step Guidelines for Improved Practice
Frontiers in Ecology and Evolution, 2021 Information about the distribution of a study object (e.g., species or habitat) is essential in face of increasing pressure from land or sea use, and climate change. Distribution models are instrumental for acquiring such information, but also encumbered
Anders Bryn +6 more
doaj +1 more source
A Continuous Process for Validation, Verification, and Accreditation of Simulation Models
Mathematics, 2023 A simulation model, and more generically, a model, is founded on its assumptions. Assurance of the model’s correctness and correct use is needed to achieve accreditation.
Pau Fonseca i Casas
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Algorithmic Fairness: Choices, Assumptions, and Definitions
Annual Review of Statistics and Its Application, 2021 A recent wave of research has attempted to define fairness quantitatively. In particular, this work has explored what fairness might mean in the context of decisions based on the predictions of statistical and machine learning models. The rapid growth of
Shira Mitchell +4 more
semanticscholar +1 more source