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Found 3 papers, 1 papers with code
Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance
In this paper, we propose the first unified interpretation for existing zero-shot methods from the perspective of approximating the conditional posterior mean for the reverse diffusion process of conditional sampling.
Drill the Cork of Information Bottleneck by Inputting the Most Important Data
As a promising tool to better understand the learning dynamic of minibatch SGD, the information bottleneck (IB) theory claims that the optimization process consists of an initial fitting phase and the following compression phase.
Accelerating Minibatch Stochastic Gradient Descent using Typicality Sampling
Machine learning, especially deep neural networks, has been rapidly developed in fields including computer vision, speech recognition and reinforcement learning.
