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Found 18 papers, 5 papers with code
Trivialized Momentum Facilitates Diffusion Generative Modeling on Lie Groups
However, our trivialization technique creates to a new momentum variable that stays in a simple $\textbf{fixed vector space}$.
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.
Quantum State Generation with Structure-Preserving Diffusion Model
This article considers the generative modeling of the (mixed) states of quantum systems, and an approach based on denoising diffusion model is proposed.
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.
Generative Modeling with Phase Stochastic Bridges
In this work, we introduce a novel generative modeling framework grounded in \textbf{phase space dynamics}, where a phase space is defined as {an augmented space encompassing both position and velocity.}
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.
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.
Deep Graphic FBSDEs for Opinion Dynamics Stochastic Control
Our approach relies on the probabilistic representation of the solution of the Hamilton-Jacobi-Bellman partial differential equation.
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 #51 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.
Large-Scale Multi-Agent Deep FBSDEs
We showcase superior performance of our framework over the state-of-the-art deep fictitious play algorithm on an inter-bank lending/borrowing problem in terms of multiple metrics.
Multi-agent Deep FBSDE Representation For Large Scale Stochastic Differential Games
In this paper we present a deep learning framework for solving large-scale multi-agent non-cooperative stochastic games using fictitious play.
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.
Feynman-Kac Neural Network Architectures for Stochastic Control Using Second-Order FBSDE Theory
We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellman partial differential equations.
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 Nonlinear Stochastic Optimal Control for Systems with Multiplicative Uncertainties
We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellman partial differential equations.
Deep 2FBSDEs For Systems With Control Multiplicative Noise
We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellmanpartial differential equations.
