Variational Schr\" odinger Diffusion Models
Schr\" odinger bridge (SB) has emerged as the go-to method for optimizing transportation
plans in diffusion models. However, SB requires estimating the intractable forward score …
plans in diffusion models. However, SB requires estimating the intractable forward score …
Neural Shr\" odinger Bridge Matching for Pansharpening
Recent diffusion probabilistic models (DPM) in the field of pansharpening have been
gradually gaining attention and have achieved state-of-the-art (SOTA) performance. In this …
gradually gaining attention and have achieved state-of-the-art (SOTA) performance. In this …
Recurrent Interpolants for Probabilistic Time Series Prediction
Sequential models like recurrent neural networks and transformers have become standard
for probabilistic multivariate time series forecasting across various domains. Despite their …
for probabilistic multivariate time series forecasting across various domains. Despite their …
Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics
Replica exchange stochastic gradient Langevin dynamics (reSGLD) is an effective sampler
for non-convex learning in large-scale datasets. However, the simulation may encounter …
for non-convex learning in large-scale datasets. However, the simulation may encounter …
Schr\"{o} dinger Bridge with Quadratic State Cost is Exactly Solvable
Schr\" odinger bridge is a diffusion process that steers a given distribution to another in a
prescribed time while minimizing the effort to do so. It can be seen as the stochastic …
prescribed time while minimizing the effort to do so. It can be seen as the stochastic …