Search Results for author:
Found 4 papers, 0 papers with code
Convergence of flow-based generative models via proximal gradient descent in Wasserstein space
Flow-based generative models enjoy certain advantages in computing the data generation and the likelihood, and have recently shown competitive empirical performance.
Convergence of score-based generative modeling for general data distributions
Score-based generative modeling (SGM) has grown to be a hugely successful method for learning to generate samples from complex data distributions such as that of images and audio.
Convergence for score-based generative modeling with polynomial complexity
Using our guarantee, we give a theoretical analysis of score-based generative modeling, which transforms white-noise input into samples from a learned data distribution given score estimates at different noise scales.
From syntactic structure to semantic relationship: hypernym extraction from definitions by recurrent neural networks using the part of speech information
The hyponym-hypernym relation is an essential element in the semantic network.
