We present theoretical convergence guarantees for ODE-based generative models,
specifically flow matching. We use a pre-trained autoencoder network to map high …

Soft sensing of yarn quality is critical for process monitoring and quality control in smart
manufacturing in the textile industry. However, current methods still suffer from limitations in …

Learning operators between infinitely dimensional spaces is an important learning task
arising in machine learning, imaging science, mathematical modeling and simulations, etc …

Neural scaling laws play a pivotal role in the performance of deep neural networks and have
been observed in a wide range of tasks. However, a complete theoretical framework for …

Autoencoders are neural networks widely used in unsupervised learning tasks such as
dimensionality reduction and feature extraction. This paper establishes nonasymptotic …

Autoencoders are neural networks widely used in unsupervised learning for dimensionality
reduction and feature extraction. This paper provides non-asymptotic guarantees for deep …