5 papers
Diffusion Based Unpaired Data Learning for Inverse Problems
Chenglong Bao, Yiming Dang, Chenguang Duan +2
Data is important in many deep learning-based inverse problem solvers. However, obtaining sufficient paired data in many scenarios remains highly challenging, while unpaired data i…
Approximation of Analytic Functions by ReLU Neural Networks with Adjustable Depth and Width
Yanming Lai, Defeng Sun, Yang Wang
In contrast to most studies on neural network approximation theory that characterize results through a single parameter, such as the total number of network parameters, \cite{shen2…
Beyond the Prompt in Large Language Models: Comprehension, In-Context Learning, and Chain-of-Thought
Yuling Jiao, Yanming Lai, Huazhen Lin +3
Large Language Models (LLMs) have demonstrated remarkable proficiency across diverse tasks, exhibiting emergent properties such as semantic prompt comprehension, In-Context Learnin…
Approximation Bounds for Transformer Networks with Application to Regression
Yuling Jiao, Yanming Lai, Defeng Sun +2
We explore the approximation capabilities of Transformer networks for Hölder and Sobolev functions, and apply these results to address nonparametric regression estimation with depe…
Bringing Generative Learning to Representation Learning: Self-Supervised Transfer Learning as Distribution Matching
Yuling Jiao, Wensen Ma, Defeng Sun +2
Most self-supervised learning objectives defend against collapse but leave the target representation law unspecified. We formulate representation learning as Distribution Matching…