Unverified50% confidenceFactExact time
ERM假设训练样本点本身是风险积分的离散近似，而VRM主张在每个样本点周围定义邻域分布并在该邻域上计算期望损失
1
Sources
50%
Confidence
Long-term
Relevance
7/15/2026
First Seen
Sources
Related Claims
UnverifiedVRM主张在每个样本点周围定义一个邻域分布，并在该邻域上计算期望损失，是对经验风险最小化（ERM）框架的扩展84% similarUnverifiedDuring training, LLMs optimize parameters by maximizing the prediction probability of the next token, meaning the model learns the statistical distribution of the training corpus rather than human aesthetic judgment.70% similarUnverifiedIn scenarios with insufficient training data coverage, LLMs generate outputs that appear plausible but may be incorrect based on statistical patterns, rather than acknowledging uncertainty.68% similarUnverified变分自编码器（VAE）和扩散模型的训练目标建立在变分推断和对数似然最大化的概率框架之上67% similar
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