Unverified50% confidenceConceptExact time
A generative distribution embedding (GDE) is a method for representing probability distributions with a parameterized generative model, learning a mapping from low-dimensional latent variables to the state space, while using an encoder to map observation histories to a distribution over latent variables.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedBETS uses a generative distribution embedding (GDE) to represent the belief at each node and updates it through the POMDP dynamics.72% similarUnverifiedMainstream generative models such as diffusion models and flow matching models are essentially probabilistic generators that produce new samples by sampling from data distributions.65% similarUnverifiedThe residual distribution is defined as follows: for tokens where the target model probability p(x) is greater than the draft model probability q(x), the residual probability is proportional to max(0, p(x)-q(x)); this mechanism ensures that the marginal distribution of the sampled results strictly equals the original distribution of the target model.64% similarVerifiedLLMs are essentially probability distribution estimators based on the Transformer architecture, and their output is a probability sampling over the next token.64% similarUnverifiedGaussian output distribution is the most fundamental form of predictive uncertainty quantification (UQ), where the model outputs distribution parameters (mean and variance) rather than a single scalar.63% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1028961API
curl https://kongchang.com/api/v1/knowledge/claims/1028961MCP
get_claim(id=1028961)