Unverified50% confidenceFactExact time
The paper derives a lower bound on the probability that the SEED-MAX algorithm correctly selects features, showing that as the size of the training dataset increases, the probability that the algorithm correctly identifies the optimal feature subset can approach 1 indefinitely.
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10/14/2026
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UnverifiedThe paper proposes the SEED-MAX (Successive Empirical Excess Risk Difference Maximization) algorithm, which reformulates feature selection as a variant of the sequential nested model selection problem and identifies the most parsimonious yet most informative feature subset by recursively computing the empirical risk.77% similarUnverifiedGenerative models are inherently disadvantaged in the catalog coverage dimension, because high-frequency items' Semantic IDs are more thoroughly optimized in training data, and beam search tends to select higher-probability popular paths.73% similarUnverified即便固定所有随机种子，在不同硬件或并行度下，训练结果仍可能出现被大模型放大为显著能力差异的细微偏差72% similarUnverifiedIn zero-shot classification, the scores of all candidates sum to 1; the fewer the candidates, the larger the probability space available to each, so the threshold parameter needs to be calibrated individually based on the specific number of categories.69% similarUnverified深度学习中的随机种子控制权重初始化、数据加载批次打乱顺序、Dropout神经元随机失活以及优化器中的随机梯度估计等多个随机性来源68% similar
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