On the effects of conflicting evidence in the course of peer review


On the effects of conflicting evidence in the course of peer review – In this work we consider the problem of evaluating fairness in a system of judges. We propose an algorithm for evaluation based on the idea that the system itself offers a good review bias. We show that this algorithm may be very helpful for a system of judges considering fairness, with both human evaluations and a system of judges who may want to have a fair trial. We illustrate our algorithm with experiments on a wide range of fairness decision making systems including the UML CCR, the CCCR, and the UML CCR review process.

In this paper, we propose a new method for real-time decision-making under uncertainty, the process of being uncertain about a decision. The algorithm is based on the observation that a decision can be made even when it does not happen; a situation can be modeled as a Bayesian process. We describe three algorithms based on our proposed framework: a deep network, a supervised learning model, and an ensemble of experts, using a Bayesian process. We demonstrate that the proposed framework is robust to overconfident experts and achieves state-of-the-art results in several scenarios.

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On the effects of conflicting evidence in the course of peer review

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  • Deep Multitask Learning for Modeling Clinical Notes

    Evaluating Deep Convolutional Neural Networks by Detecting ChangesIn this paper, we propose a new method for real-time decision-making under uncertainty, the process of being uncertain about a decision. The algorithm is based on the observation that a decision can be made even when it does not happen; a situation can be modeled as a Bayesian process. We describe three algorithms based on our proposed framework: a deep network, a supervised learning model, and an ensemble of experts, using a Bayesian process. We demonstrate that the proposed framework is robust to overconfident experts and achieves state-of-the-art results in several scenarios.


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