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== 노트 ==
 
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===위키데이터===
 
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* ID :  [https://www.wikidata.org/wiki/Q1685498 Q1685498]
 
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===말뭉치===
 
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# However, in principle the cross entropy loss can be calculated - and optimised - when this is not the case.<ref name="ref_2f6e457e">[https://datascience.stackexchange.com/questions/20296/cross-entropy-loss-explanation Cross-entropy loss explanation]</ref>
 
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# Conversely, a more accurate algorithm which predicts a probability of pneumonia of 98% gives a lower cross entropy of 0.02.<ref name="ref_f7eadb48">[https://radiopaedia.org/articles/cross-entropy-1 Radiology Reference Article]</ref>
 
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# One such loss ListNet's which measures the cross entropy between a distribution over documents obtained from scores and another from ground-truth labels.<ref name="ref_c5f76eb8">[https://research.google/pubs/pub48321/ An Analysis of the Softmax Cross Entropy Loss for Learning-to-Rank with Binary Relevance – Google Research]</ref>
 
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# 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.<ref name="ref_c5f76eb8" />
 
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# Cross entropy uses the idea that we discussed on entropy.<ref name="ref_5a49710e">[https://www.mygreatlearning.com/blog/cross-entropy-explained/ What is Cross Entropy for Dummies?]</ref>
 
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# Cross entropy measures entropy between two probability distributions.<ref name="ref_5a49710e" />
 
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# B. So how do we correlate Cross Entropy to entropy when working with two distributions?<ref name="ref_5a49710e" />
 
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# If the predicted values are the same as actual values, then Cross entropy is equal to entropy.<ref name="ref_5a49710e" />
 
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# First we will use a multiclass classification problem to understand the relationship between log likelihood and cross entropy.<ref name="ref_f670b340">[https://glassboxmedicine.com/2019/12/07/connections-log-likelihood-cross-entropy-kl-divergence-logistic-regression-and-neural-networks/ Connections: Log Likelihood, Cross Entropy, KL Divergence, Logistic Regression, and Neural Networks]</ref>
 
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# Maximizing the (log) likelihood is equivalent to minimizing the binary cross entropy.<ref name="ref_f670b340" />
 
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# After that aside on maximum likelihood estimation, let’s delve more into the relationship between negative log likelihood and cross entropy.<ref name="ref_f670b340" />
 
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# Therefore, the parameters that minimize the KL divergence are the same as the parameters that minimize the cross entropy and the negative log likelihood!<ref name="ref_f670b340" />
 
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# The cross entropy loss is the negative of the first, multiplied by the logarithm of the second.<ref name="ref_02705444">[https://levelup.gitconnected.com/grokking-the-cross-entropy-loss-cda6eb9ec307 Grokking the Cross Entropy Loss]</ref>
 
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# This is almost an anticlimax: the cross entropy loss ends up being the negative logarithm of a single element in ŷ.<ref name="ref_02705444" />
 
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# You might be surprised to learn that the cross entropy loss depends on a single element of ŷ.<ref name="ref_02705444" />
 
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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" />
 
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# 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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===소스===
 
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<references />
 
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== 노트 ==
 
== 노트 ==
  

2020년 12월 25일 (금) 19:52 판

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위키데이터

말뭉치

  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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