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非线性回归与神经网络训练的局部贪心逼近AI 解读

A criterion for local estimation and approximation in nonlinear regres- sion and neural network training is introduced and motivated. $N$th-order greedy approximation for the regression (or target) function based on the criterion is shown to converge at rate $O(1/N^{1/2})$ in the nonsampling case.

固定边际二维列联表的均匀生成与 Diaconis–Efron 条件体积检验AI 解读

Two efficient Monte Carlo algorithms are proposed for uniformlygenerating two-way contingency tables with fixed margins. These permit some improvements on recent work of Diaconis, Efron and Gangolli, especially concerning estimates of the total number of such tables.