Search Results for author:
Found 16 papers, 9 papers with code
React-OT: Optimal Transport for Generating Transition State in Chemical Reactions
The RMSD and barrier height error is further improved by roughly 25% through pretraining React-OT on a large reaction dataset obtained with a lower level of theory, GFN2-xTB.
Augmented Bridge Matching
In this paper, we highlight that while flow and bridge matching processes preserve the information of the marginal distributions, they do \emph{not} necessarily preserve the coupling information unless additional, stronger optimality conditions are met.
Generalized Schrödinger Bridge Matching
Modern distribution matching algorithms for training diffusion or flow models directly prescribe the time evolution of the marginal distributions between two boundary distributions.
Mirror Diffusion Models for Constrained and Watermarked Generation
In this work, we propose Mirror Diffusion Models (MDM), a new class of diffusion models that generate data on convex constrained sets without losing any tractability.
Improving Generative Model-based Unfolding with Schrödinger Bridges
Machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements.
Improved sampling via learned diffusions
Recently, a series of papers proposed deep learning-based approaches to sample from unnormalized target densities using controlled diffusion processes.
I$^2$SB: Image-to-Image Schrödinger Bridge
We propose Image-to-Image Schr\"odinger Bridge (I$^2$SB), a new class of conditional diffusion models that directly learn the nonlinear diffusion processes between two given distributions.
Deep Generalized Schrödinger Bridge
In this work, we aim at solving a challenging class of MFGs in which the differentiability of these interacting preferences may not be available to the solver, and the population is urged to converge exactly to some desired distribution.
Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory
However, it remains unclear whether the optimization principle of SB relates to the modern training of deep generative models, which often rely on constructing log-likelihood objectives. This raises questions on the suitability of SB models as a principled alternative for generative applications.
Ranked #44 on
Image Generation
on CIFAR-10
Second-Order Neural ODE Optimizer
We propose a novel second-order optimization framework for training the emerging deep continuous-time models, specifically the Neural Ordinary Differential Equations (Neural ODEs).
Dynamic Game Theoretic Neural Optimizer
The connection between training deep neural networks (DNNs) and optimal control theory (OCT) has attracted considerable attention as a principled tool of algorithmic design.
Variational Inference MPC using Tsallis Divergence
In this paper, we provide a generalized framework for Variational Inference-Stochastic Optimal Control by using thenon-extensive Tsallis divergence.
A Differential Game Theoretic Neural Optimizer for Training Residual Networks
Connections between Deep Neural Networks (DNNs) training and optimal control theory has attracted considerable attention as a principled tool of algorithmic design.
DDPNOpt: Differential Dynamic Programming Neural Optimizer
Interpretation of Deep Neural Networks (DNNs) training as an optimal control problem with nonlinear dynamical systems has received considerable attention recently, yet the algorithmic development remains relatively limited.
Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective
In this article, we provide one possible way to align existing branches of deep learning theory through the lens of dynamical system and optimal control.
Learning End-to-end Multimodal Sensor Policies for Autonomous Navigation
Multisensory polices are known to enhance both state estimation and target tracking.
