Results 11 to 20 of about 6,758,662 (333)
Nature Medicine, 2023 This is a post hoc 18-month follow-up analysis of the CRB-401 trial, testing idecabtagene vicleucel (ide-cel, bb2121) in relapsed and refractory multiple myeloma, and reports sustained safety and clinical efficacy, which correlates with T cell phenotypes.
Yi Lin +21 more
semanticscholar +3 more sources
Lancet, The, 2018 Summary Background Coronary artery inflammation inhibits adipogenesis in adjacent perivascular fat. A novel imaging biomarker—the perivascular fat attenuation index (FAI)—captures coronary inflammation by mapping spatial changes of perivascular fat ...
E. Oikonomou +20 more
semanticscholar +3 more sources
Evaluation of post-hoc interpretability methods in time-series classification [PDF]
Nature Machine Intelligence, 2022 Post-hoc interpretability methods are critical tools to explain neural-network results. Several post-hoc methods have emerged in recent years but they produce different results when applied to a given task, raising the question of which method is the ...
Hugues Turbé +3 more
semanticscholar +1 more source
The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations [PDF]
arXiv.org, 2023 Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems. As models get larger, more ubiquitous, and pervasive in aspects of daily life, explainability is necessary to minimize ...
Vinitra Swamy, Jibril Frej, Tanja Käser
semanticscholar +1 more source
Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation [PDF]
International Conference on Learning Representations, 2022 We investigate whether three types of post hoc model explanations--feature attribution, concept activation, and training point ranking--are effective for detecting a model's reliance on spurious signals in the training data. Specifically, we consider the
J. Adebayo +3 more
semanticscholar +1 more source
Distributionally Robust Post-hoc Classifiers under Prior Shifts [PDF]
International Conference on Learning Representations, 2023 The generalization ability of machine learning models degrades significantly when the test distribution shifts away from the training distribution.
Jiaheng Wei +5 more
semanticscholar +1 more source
The boundary effect: Perceived post hoc accuracy of prediction intervals [PDF]
Judgment and Decision Making, 2018 Predictions of magnitudes (costs, durations, environmental events) are often given as uncertainty intervals (ranges). When are such forecasts judged to be correct?
Karl Halvor Teigen +2 more
doaj +3 more sources
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations [PDF]
Neural Information Processing Systems, 2022 A critical problem in the field of post hoc explainability is the lack of a common foundational goal among methods. For example, some methods are motivated by function approximation, some by game theoretic notions, and some by obtaining clean ...
Tessa Han +2 more
semanticscholar +1 more source
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts [PDF]
Conference on Fairness, Accountability and Transparency, 2022 Existing and planned legislation stipulates various obligations to provide information about machine learning algorithms and their functioning, often interpreted as obligations to “explain”.
Sebastian Bordt +3 more
semanticscholar +1 more source
Pitfalls in post hoc analyses of population receptive field data
NeuroImage, 2022 Data binning involves grouping observations into bins and calculating bin-wise summary statistics. It can cope with overplotting and noise, making it a versatile tool for comparing many observations.
Susanne Stoll +3 more
doaj +1 more source