Unverified50% confidenceFactExact time
ZENO 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.
1
Sources
50%
Confidence
Long-term
Relevance
10/26/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedZENO (Zakai Equation Neural Operator) is a model that approximates the exact unnormalized filtering distribution by combining neural operators with physics-informed loss.76% similarUnverifiedZephon introduces a globally ordered sample index mechanism at the data loading layer; each training sample has a unique logical sequence number in the entire dataset, and the data loader maps the global sequence to each rank and worker process according to the current parallel configuration.67% similarUnverified可微分编程处理不可微离散结构的主要技术路线包括直通估计器、Gumbel-Softmax重参数化和神经网络引导的束搜索67% similarUnverifiedZENO 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).67% similarUnverifiedSPLADE 是一种学习型稀疏检索模型，通过神经网络为文档生成稀疏向量表示，可在倒排索引上高效运行66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1098132API
curl https://kongchang.com/api/v1/knowledge/claims/1098132MCP
get_claim(id=1098132)