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The LogSumExp (LSE) (also called RealSoftMax or multivariable softplus) function is a smooth maximum – a smooth approximation to the maximum function, mainly used by machine learning algorithms. It is defined as the logarithm of the sum of the exponentials of the arguments:
Measurement, Properties & Log-sum-exp trick for log-domain calculations
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| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| LogSumExp | is a | softmax function.The convex conjugate of LogSumExp is the negative entropy. log-sum-exp trick for log-domain calculationsThe LSE function is often encountered when the usual ari… | 0.90 | text |
| IT | instance of | Many math libraries | 0.80 | text |
| LogSumExp | related to Properties | The LogSumExp | 0.60 | section |
| LogSumExp | related to Properties | It | 0.60 | section |
| LogSumExp | related to Properties | LSE | 0.60 | section |
| LogSumExp | related to Properties | The | 0.60 | section |
| LogSumExp | related to Properties | Proof | 0.60 | section |
| LogSumExp | related to Properties | Let | 0.60 | section |
| LogSumExp | related to Properties | Then | 0.60 | section |
| LogSumExp | related to Properties | Applying | 0.60 | section |
| LogSumExp | related to Properties | In | 0.60 | section |
| LogSumExp | related to Properties | Consider | 0.60 | section |
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