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Found 5 papers, 5 papers with code
Fast Training of Diffusion Models with Masked Transformers
For masked training, we introduce an asymmetric encoder-decoder architecture consisting of a transformer encoder that operates only on unmasked patches and a lightweight transformer decoder on full patches.
Fast Sampling of Diffusion Models via Operator Learning
Diffusion models have found widespread adoption in various areas.
Langevin Monte Carlo for Contextual Bandits
Existing Thompson sampling-based algorithms need to construct a Laplace approximation (i. e., a Gaussian distribution) of the posterior distribution, which is inefficient to sample in high dimensional applications for general covariance matrices.
Physics-Informed Neural Operator for Learning Partial Differential Equations
Specifically, in PINO, we combine coarse-resolution training data with PDE constraints imposed at a higher resolution.
Implicit competitive regularization in GANs
We show that opponent-aware modelling of generator and discriminator, as present in competitive gradient descent (CGD), can significantly strengthen ICR and thus stabilize GAN training without explicit regularization.
