Genre
Appendix A Datasheet for Datasets
The negative prompt used is " unrealistic, bad anatomy, wrong anatomy, extra limb, missing limb,
Learning from Uncertain Data: From Possible Worlds to Possible Models
Exploring every possibility and training a model for each is impractical.
One-step differentiation of iterative algorithms
For iterative algorithms, implicit differentiation alleviates this issue but requires custom implementation of Jacobian evaluation. In this paper, we study one-step differentiation, also known as Jacobian-free backpropagation, a method as easy as automatic differentiation and as efficient as implicit differentiation for fast algorithms (e.g., superlinear
Function Space Bayesian Pseudocoreset for Bayesian Neural Networks
A Bayesian pseudocoreset is a compact synthetic dataset summarizing essential information of a large-scale dataset and thus can be used as a proxy dataset for scalable Bayesian inference. Typically, a Bayesian pseudocoreset is constructed by minimizing a divergence measure between the posterior conditioning on the pseudocoreset and the posterior conditioning on the full dataset. However, evaluating the divergence can be challenging, particularly for the models like deep neural networks having high-dimensional parameters.
Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time
Diffusion-based generative models, as first introduced by Sohl-Dickstein et al. (2015), have shown
