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# If the hummingbird element is 1, which means spot-on correct classification, then the cross entropy loss for that classification is zero.<ref name="ref_02705444" />
 
# If the hummingbird element is 1, which means spot-on correct classification, then the cross entropy loss for that classification is zero.<ref name="ref_02705444" />
 
# Cross Entropy Measures of Bipolar and Interval Bipolar Neutrosophic Sets and ...<ref name="ref_75951430">[https://books.google.co.kr/books?id=jIpuDwAAQBAJ&pg=PA2&lpg=PA2&dq=Cross+entropy&source=bl&ots=TWBqDWPAuV&sig=ACfU3U1SeIiFnEEAa_xe5pX9lHzg5jZ8_w&hl=en&sa=X&ved=2ahUKEwjvqJ6D3uPtAhUaHXAKHbd4Ch84HhDoATAIegQIBxAC Cross Entropy Measures of Bipolar and Interval Bipolar Neutrosophic Sets and ...]</ref>
 
# Cross Entropy Measures of Bipolar and Interval Bipolar Neutrosophic Sets and ...<ref name="ref_75951430">[https://books.google.co.kr/books?id=jIpuDwAAQBAJ&pg=PA2&lpg=PA2&dq=Cross+entropy&source=bl&ots=TWBqDWPAuV&sig=ACfU3U1SeIiFnEEAa_xe5pX9lHzg5jZ8_w&hl=en&sa=X&ved=2ahUKEwjvqJ6D3uPtAhUaHXAKHbd4Ch84HhDoATAIegQIBxAC Cross Entropy Measures of Bipolar and Interval Bipolar Neutrosophic Sets and ...]</ref>
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# Trained with the standard cross entropy loss, deep neural networks can achieve great performance on correctly labeled data.<ref name="ref_c0a98df0">[https://www.ijcai.org/Proceedings/2020/305 Can Cross Entropy Loss Be Robust to Label Noise?]</ref>
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# Although most of the robust loss functions stem from Categorical Cross Entropy (CCE) loss, they fail to embody the intrinsic relationships between CCE and other loss functions.<ref name="ref_c0a98df0" />
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# In this paper, we propose a general framework dubbed Taylor cross entropy loss to train deep models in the presence of label noise.<ref name="ref_c0a98df0" />
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# Cross entropy measure is a widely used alternative of squared error.<ref name="ref_0c69f3d5">[https://deepnotes.io/softmax-crossentropy Classification and Loss Evaluation - Softmax and Cross Entropy Loss]</ref>
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# Cross Entropy Loss with Softmax function are used as the output layer extensively.<ref name="ref_0c69f3d5" />
 
===소스===
 
===소스===
 
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==메타데이터==
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===위키데이터===
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* ID :  [https://www.wikidata.org/wiki/Q1685498 Q1685498]
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===Spacy 패턴 목록===
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* [{'LOWER': 'cross'}, {'LEMMA': 'entropy'}]

2021년 2월 17일 (수) 00:38 기준 최신판

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  1. However, in principle the cross entropy loss can be calculated - and optimised - when this is not the case.[1]
  2. Conversely, a more accurate algorithm which predicts a probability of pneumonia of 98% gives a lower cross entropy of 0.02.[2]
  3. One such loss ListNet's which measures the cross entropy between a distribution over documents obtained from scores and another from ground-truth labels.[3]
  4. In fact, we establish an analytical connection between softmax cross entropy and two popular ranking metrics in a learning-to-rank setup with binary relevance labels.[3]
  5. Cross entropy uses the idea that we discussed on entropy.[4]
  6. Cross entropy measures entropy between two probability distributions.[4]
  7. B. So how do we correlate Cross Entropy to entropy when working with two distributions?[4]
  8. If the predicted values are the same as actual values, then Cross entropy is equal to entropy.[4]
  9. First we will use a multiclass classification problem to understand the relationship between log likelihood and cross entropy.[5]
  10. Maximizing the (log) likelihood is equivalent to minimizing the binary cross entropy.[5]
  11. After that aside on maximum likelihood estimation, let’s delve more into the relationship between negative log likelihood and cross entropy.[5]
  12. Therefore, the parameters that minimize the KL divergence are the same as the parameters that minimize the cross entropy and the negative log likelihood![5]
  13. The cross entropy loss is the negative of the first, multiplied by the logarithm of the second.[6]
  14. This is almost an anticlimax: the cross entropy loss ends up being the negative logarithm of a single element in ŷ.[6]
  15. You might be surprised to learn that the cross entropy loss depends on a single element of ŷ.[6]
  16. If the hummingbird element is 1, which means spot-on correct classification, then the cross entropy loss for that classification is zero.[6]
  17. Cross Entropy Measures of Bipolar and Interval Bipolar Neutrosophic Sets and ...[7]
  18. Trained with the standard cross entropy loss, deep neural networks can achieve great performance on correctly labeled data.[8]
  19. Although most of the robust loss functions stem from Categorical Cross Entropy (CCE) loss, they fail to embody the intrinsic relationships between CCE and other loss functions.[8]
  20. In this paper, we propose a general framework dubbed Taylor cross entropy loss to train deep models in the presence of label noise.[8]
  21. Cross entropy measure is a widely used alternative of squared error.[9]
  22. Cross Entropy Loss with Softmax function are used as the output layer extensively.[9]

소스

  1. ↑ Cross-entropy loss explanation
  2. ↑ Radiology Reference Article
  3. ↑ 3.0 3.1 An Analysis of the Softmax Cross Entropy Loss for Learning-to-Rank with Binary Relevance – Google Research
  4. ↑ 4.0 4.1 4.2 4.3 What is Cross Entropy for Dummies?
  5. ↑ 5.0 5.1 5.2 5.3 Connections: Log Likelihood, Cross Entropy, KL Divergence, Logistic Regression, and Neural Networks
  6. ↑ 6.0 6.1 6.2 6.3 Grokking the Cross Entropy Loss
  7. ↑ Cross Entropy Measures of Bipolar and Interval Bipolar Neutrosophic Sets and ...
  8. ↑ 8.0 8.1 8.2 Can Cross Entropy Loss Be Robust to Label Noise?
  9. ↑ 9.0 9.1 Classification and Loss Evaluation - Softmax and Cross Entropy Loss

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Spacy 패턴 목록

  • [{'LOWER': 'cross'}, {'LEMMA': 'entropy'}]
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