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So perplexity represents the number of sides of a fair die that when rolled, produces a sequence with the same entropy as your given probability distribution. Number of States OK, so now that we have an intuitive definition of perplexity, let's take a quick look at how it is affected by the number of states in a model.
In the Coursera NLP course , Dan Jurafsky calculates the following perplexity: Operator(1 in 4) Sales(1 in 4) Technical Support(1 in 4) 30,000 names(1 in 120,000 each) He says the Perplexity is 53...
Perplexity未来是否可以超越谷歌搜索可能有点难度，但是作者认为Perplexity走在正确的道路上，它的估值已经从一年之前的1.55亿美元增长到了10亿美元，仅仅一年的时间估值翻了十倍。从谷歌推出的SGE以及微软的Copilot的效果来看，是没有Perplexity的效果好的。至少目前我的默认搜索引擎已经换成Perplexity ...
Since $\log p(X)$ is intractable in the NVDM, we use the variational lower bound (which is an upper bound on perplexity) to compute the perplexity following Mnih & Gregor (2014).
28 nov. 2018 · While reading Laurens van der Maaten's paper about t-SNE we can encounter the following statement about perplexity: The perplexity can be interpreted as a smooth measure of the effective number of neighbors. The performance of SNE is fairly robust to changes in the perplexity, and typical values are between 5 and 50.
5 janv. 2023 · I am working on VQ-VAE experiments, and I have noticed that perplexity has been used as an evaluation measure for VQ codebook. Also, most of the work including codebook perplexity as a evaluation measure assumes higher perplexity better, though I feel higher perplexity is not very wanted in my intuition. For example, lower perplexity indicates ...
8 mars 2018 · Note that the perplexity of a distribution over N items can never be higher then N (in this case, the distribution is uniform). For t-SNE this means you need at least 101 points to be able to use perplexity 100. If you use a perplexity setting that is too high for the number of points (and have no assertion checking for that), the binary search for the right bandwidth will fail and the ...
28 mars 2019 · The larger the perplexity, the more non-local information will be retained in the dimensionality reduction result. When I use t-SNE on two of mine test datasets for dimensionality reduction, I observe that the clusters found by t-SNE will become consistently more well-defined with the increase of perplexity. Although this is a desirable outcome ...
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The perplexity, used by convention in language modeling, is monotonically decreasing in the likelihood of the test data, and is algebraicly equivalent to the inverse of the geometric mean per-word likelihood. A lower perplexity score indicates better generalization performance. I.e, a lower perplexity indicates that the data are more likely.
