Unverified50% confidenceFactExact time
ZENO embeds the posterior distribution into an expectation-maximization (EM) framework, achieving parameter learning and state inference by alternating between the E step (estimating the posterior distribution using current parameters) and the M step (maximizing the complete-data log-likelihood to update parameters).
1
Sources
50%
Confidence
Long-term
Relevance
10/26/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedZENO trains the neural operator by minimizing the Zakai equation residual, and can learn the implicit filtering distribution without large amounts of labeled data and generalize to different initial conditions and parameter settings.67% similarUnverifiedRegression is used to predict continuous numerical values and is the most fundamental part of supervised learning.66% similarUnverifiedEmbedding模型微调通常采用对比学习范式,典型方法是构造查询-正例文档-负例文档三元组,通过InfoNCE或Triplet Loss等损失函数优化65% similarUnverifiedCompared with existing latent SDE inference methods, ZENO shows advantages in both accurately recovering the posterior distribution and learning SDE parameters, and is more precise especially in distribution shape and parameter consistency.64% similarUnverifiedMFlow is an adaptive parameterization method that learns to interpolate between x0-prediction and u-prediction along the principal directions of the data, and is learned end-to-end jointly with diffusion training.64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1098133API
curl https://kongchang.com/api/v1/knowledge/claims/1098133MCP
get_claim(id=1098133)