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2 月 24 日

1 緒論ppt
2 機器學習概述ppt
3 線性模型ppt
4 前饋神經網絡ppt
5 卷積神經網絡ppt
6 循環神經網絡ppt
7 網絡優化與正則化ppt
8 注意力機製與外部記憶ppt
9 無監督學習ppt
10 模型獨立的學習方式ppt
11 概率圖模型ppt
12 深度信念網絡ppt
13 深度生成模型ppt
14 深度強化學習ppt
15 序列生成模型ppt

### 最新論文

Let $X_1,\dots,X_n$ be independent centered random vectors in $\mathbb{R}^d$. This paper shows that, even when $d$ may grow with $n$, the probability $P(n^{-1/2}\sum_{i=1}^nX_i\in A)$ can be approximated by its Gaussian analog uniformly in hyperrectangles $A$ in $\mathbb{R}^d$ as $n\to\infty$ under appropriate moment assumptions, as long as $(\log d)^5/n\to0$. This improves a result of Chernozhukov, Chetverikov & Kato [Ann. Probab. 45 (2017) 2309-2353] in terms of the dimension growth condition. When $n^{-1/2}\sum_{i=1}^nX_i$ has a common factor across the components, this condition can be further improved to $(\log d)^3/n\to0$. The corresponding bootstrap approximation results are also developed. These results serve as a theoretical foundation of simultaneous inference for high-dimensional models.