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Found 14 papers, 2 papers with code
SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
We present Scalable Interpolant Transformers (SiT), a family of generative models built on the backbone of Diffusion Transformers (DiT).
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
The time-dependent probability density function of the stochastic interpolant is shown to satisfy a first-order transport equation as well as a family of forward and backward Fokker-Planck equations with tunable diffusion coefficient.
A continuous-time analysis of distributed stochastic gradient
We analyze the effect of synchronization on distributed stochastic gradient algorithms.
Implicit Regularization and Momentum Algorithms in Nonlinearly Parameterized Adaptive Control and Prediction
Stable concurrent learning and control of dynamical systems is the subject of adaptive control.
The role of optimization geometry in single neuron learning
Recent numerical experiments have demonstrated that the choice of optimization geometry used during training can impact generalization performance when learning expressive nonlinear model classes such as deep neural networks.
Learning Stability Certificates from Data
Many existing tools in nonlinear control theory for establishing stability or safety of a dynamical system can be distilled to the construction of a certificate function that guarantees a desired property.
Regret Bounds for Adaptive Nonlinear Control
We study the problem of adaptively controlling a known discrete-time nonlinear system subject to unmodeled disturbances.
Nonparametric adaptive control and prediction: theory and randomized algorithms
A key assumption in the theory of nonlinear adaptive control is that the uncertainty of the system can be expressed in the linear span of a set of known basis functions.
Adversarially Robust Stability Certificates can be Sample-Efficient
Motivated by bridging the simulation to reality gap in the context of safety-critical systems, we consider learning adversarially robust stability certificates for unknown nonlinear dynamical systems.
Probability flow solution of the Fokker-Planck equation
The method of choice for integrating the time-dependent Fokker-Planck equation in high-dimension is to generate samples from the solution via integration of the associated stochastic differential equation.
Deep learning probability flows and entropy production rates in active matter
We show that a single instance of our network trained on a system of 4096 particles at one packing fraction can generalize to other regions of the phase diagram, including systems with as many as 32768 particles.
Stochastic interpolants with data-dependent couplings
In this work, using the framework of stochastic interpolants, we formalize how to \textit{couple} the base and the target densities, whereby samples from the base are computed conditionally given samples from the target in a way that is different from (but does preclude) incorporating information about class labels or continuous embeddings.
Multimarginal generative modeling with stochastic interpolants
Given a set of $K$ probability densities, we consider the multimarginal generative modeling problem of learning a joint distribution that recovers these densities as marginals.
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
We propose a framework for probabilistic forecasting of dynamical systems based on generative modeling.
