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Below I am using perplexity=50, max_iter=2000, early_exag_coeff=12, stop_lying_iter=1000). Here is what I get, on the left unlabeled, and on the right colored according to the ground truth: I would argue that the unlabeled t-SNE representation does suggest 10 clusters.
②使用上述代码，如果正常的话，则会得到随着主题数k增加而减小的perplexity曲线。 但是我却得到了随主题数增加，一直递增的困惑度曲线图 (在有限的k值范围内)，甚至当我把k值设置到80，100，150时困惑度仍然没有减小，大概如下图。
With respect to the unsupervised learning (like clustering), are there any metrics to evaluate performance?
Perplexity可以粗略的理解为“对于一篇文章，我们的LDA模型有多 不确定 它是属于某个topic的”。 topic越多，Perplexity越小，但是越容易 overfitting。 我们利用Model Selection找到Perplexity又好，topic个数又少的topic数量。 可以画出Perplexity vs num of topics曲线，找到满足要求的 ...
I came across term perplexity which refers to the log-averaged inverse probability on unseen data. Wikipedia article on perplexity does not give an intuitive meaning for the same. This perplexity
这几天发现每次开机，浏览器都会自动打开一个叫“博商东方”的VPN网站。 找了一下，估计是俄区破解版游戏搞的，给电脑设立定时任务，每次开机都会添加一次注册表，打开这个网页。删除注册表和定时任务应该有用。 1.win+r ，搜索taskschd.msc，这里应该能找到一个名称为你电脑名的任务，查看 ...
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~ AI布道师Warren
I will cite the FAQ from t-SNE website. First for perplexity: How should I set the perplexity in t-SNE? The performance of t-SNE is fairly robust under different settings of the perplexity. The most appropriate value depends on the density of your data. Loosely speaking, one could say that a larger / denser dataset requires a larger perplexity.
最大的感受是，OpenAI把之前AI网络搜索的问题全都给解决了。论完成度高还是得看OpenAI。 Deep Research是让o3模型能够联网搜索、运行python代码工具。乍一看似乎和2023年年初就推出的new Bing、perplexity AI网络搜索并没有本质区别。 但实际上，我当时作为第一批用户使用new Bing遇到的问题，Deep Research基本上 ...
My understanding of perplexity is that it's related to the group sizes (number of neighbors). Using R (Rtsne) I tried using a perplexity of 5, 15, 30, and 50, they all have some good groups and a few that don't look quite right. How should I choose perplexity when the group sizes are so different?