Neural Information Processing Systems http://nips.cc/

In this paper, we present DoSSR, a Do main S hift diffusion-based SR model that capitalizes on the generative powers of pretrained diffusion models while significantly enhancing efficiency by initiating the diffusion process with low-resolution (LR) images. At the core of our approach is a domain shift equation that integrates seamlessly with existing diffusion models. This integration not only improves the use of diffusion prior but also boosts inference efficiency. Moreover, we advance our method by transitioning the discrete shift process to a continuous formulation, termed as DoS-SDEs. This advancement leads to the fast and customized solvers that further enhance sampling efficiency.

People are relying on AI agents to assist them with various tasks.

However,theoretical and empirical evidence both suggest that there is a maximum mini-batch size beyond which the number of iterations required toconvergestops decreasing, andgeneralization error begins toincrease [Maetal.,2017,Lietal., 2014, Golmant et al., 2018, Shallue et al., 2018, Keskar et al., 2016, Hoffer et al., 2017]. In this paper, we aim instead to decrease the communication cost per worker.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Existing regression models tend to fall short in both accuracy and uncertainty estimation when the label distribution is imbalanced. In this paper, we propose a probabilistic deep learning model, dubbed variational imbalanced regression (VIR), which not only performs well in imbalanced regression but naturally produces reasonable uncertainty estimation as a byproduct. Different from typical variational autoencoders assuming I.I.D. representations (a data point's representation is not directly affected by other data points), our VIR borrows data with similar regression labels to compute the latent representation's vari-ational distribution; furthermore, different from deterministic regression models producing point estimates, VIR predicts the entire normal-inverse-gamma distributions and modulates the associated conjugate distributions to impose probabilistic reweighting on the imbalanced data, thereby providing better uncertainty estimation.