Results 11 to 20 of about 2,333,745 (356)
Neural Computation, 2021 Active inference offers a first principle account of sentient behavior, from which special and important cases—for example, reinforcement learning, active learning, Bayes optimal inference, Bayes optimal design—can be derived. Active inference finesses the exploitation-exploration dilemma in relation to prior preferences by placing information gain on
Friston, K. +4 more
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Proceedings of the National Academy of Sciences, 2020 Significance Most statistical methods rely on certain mathematical conditions, known as regularity assumptions, to ensure their validity. Without these conditions, statistical quantities like P values and confidence intervals might not be valid.
Larry Wasserman +2 more
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A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference [PDF]
North American Chapter of the Association for Computational Linguistics, 2017 This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding.
Adina Williams +2 more
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The Quarterly Journal of Economics, 2018 Abstract Policy makers, firms, and researchers often choose among multiple options based on estimates. Sampling error in the estimates used to guide choice leads to a winner’s curse, since we are more likely to select a given option precisely when we overestimate its effectiveness.
Toru Kitagawa +2 more
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Membership Inference Attacks Against Machine Learning Models [PDF]
IEEE Symposium on Security and Privacy, 2016 We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if ...
R. Shokri +3 more
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Variational Inference: A Review for Statisticians [PDF]
arXiv.org, 2016 One of the core problems of modern statistics is to approximate difficult-to-compute probability densities. This problem is especially important in Bayesian statistics, which frames all inference about unknown quantities as a calculation involving the ...
D. Blei, A. Kucukelbir, Jon D. McAuliffe
semanticscholar +1 more source
, 2023 Patterns of microcircuitry suggest that the brain has an array of repeated canonical computational units. Yet neural representations are distributed, so the relevant computations may only be related indirectly to single-neuron transformations. It thus remains an open challenge how to define canonical distributed computations. We integrate normative and
Raju, Rajkumar Vasudeva +3 more
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A large annotated corpus for learning natural language inference [PDF]
Conference on Empirical Methods in Natural Language Processing, 2015 Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research
Samuel R. Bowman +3 more
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Inference and analysis of cell-cell communication using CellChat
Nature Communications, 2020 Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links.
Suoqin Jin +8 more
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2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2013 Lower bounds for the average probability of error of estimating a hidden variable X given an observation of a correlated random variable Y, and Fano's inequality in particular, play a central role in information theory. In this paper, we present a lower bound for the average estimation error based on the marginal distribution of X and the principal ...
Medard, Muriel +5 more
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