Unverified50% confidenceFactExact time
The hierarchical Gaussian process introduces a group-level structure on top of the GP: each post-stratification cell has its own group effect, while the group effects are related to one another through a shared kernel structure.
1
Sources
50%
Confidence
Long-term
Relevance
10/15/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThis study proposes a survey-weighting framework based on hierarchical Gaussian process (HGP) regression, using kernel smoothing to share information across related units, and derives local equivalent weights.65% similarUnverifiedSemi-autoregressive generative models generate a block of tokens in parallel at each step, with blocks still generated in autoregressive order, seeking a balance between parallelism and sequential dependency.63% similarUnverifiedHybrid inference (offloading) refers to placing some layers of a model in GPU memory and the remaining layers in system memory.63% similarUnverified张量并行将模型单层的权重矩阵切分到多块GPU上并行计算,通过All-Reduce通信汇聚结果63% similarUnverified大型旗舰模型推理需要多张GPU进行张量并行(按列/行切分权重矩阵)或流水线并行(不同层分布到不同GPU节点)63% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1032986API
curl https://kongchang.com/api/v1/knowledge/claims/1032986MCP
get_claim(id=1032986)