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- intuition - What is perplexity? - Cross Validated
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
- 如何评价perplexity ai，会是未来搜索的趋势吗？ - 知乎
Perplexity AI 不是搜索的终点，但可能是我们逃离“信息垃圾场”的起点。 它就像是搜索引擎界的 GPT-4：懂你说什么，还知道去哪儿找答案。 当然，要是它哪天推出 Pro 会员，也别忘了上拼团看看有没有便宜团能拼，不然 AI 会用，钱包也得会养哈哈~
- Perplexity formula in the t-SNE paper vs. in the implementation
The perplexity formula in the official paper of t-SNE IS NOT the same as in its implementation In the implementation (MATLAB): % Function that computes the Gaussian kernel values given a vector of % squared Euclidean distances, and the precision of the Gaussian kernel
- 求通俗解释NLP里的perplexity是什么？ - 知乎
困惑度 Perplexity 是衡量语言模型好坏的指标，为了更好地理解其意义，首先有必要回顾熵的概念。 根据信息论与编码的知识，我们知道 熵代表着根据信息的概率分布对其编码所需要的最短平均编码长度。
- information theory - Calculating Perplexity - Cross Validated
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
- How to find the perplexity of a corpus - Cross Validated
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- Inferring the number of topics for gensims LDA - perplexity, CM, AIC . . .
Having negative perplexity apparently is due to infinitesimal probabilities being converted to the log scale automatically by Gensim, but even though a lower perplexity is desired, the lower bound value denotes deterioration (according to this), so the lower bound value of perplexity is deteriorating with a larger number of topics in my figures
- Perplexity calculation in variational neural topic models
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)
- Intuition behind perplexity parameter in t-SNE
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 What this effective number of neighbors would mean? Should I understand perplexity value as expected number of nearest neighbors to the point
- text mining - How to calculate perplexity of a holdout with Latent . . .
Perplexity is seen as a good measure of performance for LDA The idea is that you keep a holdout sample, train your LDA on the rest of the data, then calculate the perplexity of the holdout The perplexity could be given by the formula:
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