Search Results for author:
Found 8 papers, 3 papers with code
Towards efficient quantum algorithms for diffusion probability models
As such, this work represents one of the most direct and pragmatic applications of quantum algorithms to large-scale machine learning models, presumably talking substantial steps towards demonstrating the practical utility of quantum computing.
Nested Diffusion Models Using Hierarchical Latent Priors
Our approach employs a series of diffusion models to progressively generate latent variables at different semantic levels.
Embed and Emulate: Contrastive representations for simulation-based inference
E&E learns a low-dimensional latent embedding of the data (i. e., a summary statistic) and a corresponding fast emulator in the latent space, eliminating the need to run expensive simulations or a high dimensional emulator during inference.
Residual Connections Harm Generative Representation Learning
We show that introducing a weighting factor to reduce the influence of identity shortcuts in residual networks significantly enhances semantic feature learning in generative representation learning frameworks, such as masked autoencoders (MAEs) and diffusion models.
Training neural operators to preserve invariant measures of chaotic attractors
In this paper, we propose an alternative framework designed to preserve invariant measures of chaotic attractors that characterize the time-invariant statistical properties of the dynamics.
Deep Stochastic Mechanics
This paper introduces a novel deep-learning-based approach for numerical simulation of a time-evolving Schr\"odinger equation inspired by stochastic mechanics and generative diffusion models.
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification
This paper explores learning emulators for parameter estimation with uncertainty estimation of high-dimensional dynamical systems.
Pure Exploration in Kernel and Neural Bandits
To overcome the curse of dimensionality, we propose to adaptively embed the feature representation of each arm into a lower-dimensional space and carefully deal with the induced model misspecification.
