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  • Deep Evidential Regression - arXiv. org
    Abstract Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust, and efcient measures of uncertainty are crucial In this paper, we propose a novel method for training non-Bayesian NNs to estimate a continuous target as well as its associated evidence in order
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    Abstract Non-autoregressive Transformer (NAT) is a fam-ily of text generation models, which aims to re-duce the decoding latency by predicting the whole sentences in parallel However, such latency re-duction sacrifices the ability to capture left-to-right dependencies, thereby making NAT learning very challenging In this paper, we present theo-
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    Eine ausführliche und umfassende Anamnese stellt einen der wichtigsten Aspekte in der Diagnostik interstitieller Lungenerkrankungen dar Sie umfasst nicht nur akute Beschwerden, sondern auch
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    Abstract Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems Also, conventional networks fix the architecture before training starts; as a result, training cannot improve the architecture To address these limitations, we describe a method to reduce the
  • A FIRST COURSE - GitHub
    plications of abstract algebra have increased the numbers of interested students, many of whom are working in other disciplines I have rewritten this text for two audiences This new edition can serve as a text for those who wish to continue teaching the currently popular arrangement of introducing both groups and rings in the first semester





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