6 papers
Minimal Sufficient Representations for Self-interpretable Deep Neural Networks
Zhiyao Tan, Liu Li, Huazhen Lin
Deep neural networks (DNNs) achieve remarkable predictive performance but remain difficult to interpret, largely due to overparameterization that obscures the minimal structure req…
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…
Adv-SSL: Adversarial Self-Supervised Representation Learning with Theoretical Guarantees
Chenguang Duan, Yuling Jiao, Huazhen Lin +2
Learning transferable data representations from abundant unlabeled data remains a central challenge in machine learning. Although numerous self-supervised learning methods have bee…
Olica: Efficient Structured Pruning of Large Language Models without Retraining
Jiujun He, Huazhen Lin
Most existing structured pruning methods for Large Language Models (LLMs) require substantial computational and data resources for retraining to reestablish the corrupted correlati…
Deep Transfer Learning: Model Framework and Error Analysis
Yuling Jiao, Huazhen Lin, Yuchen Luo +1
This paper presents a framework for deep transfer learning, which aims to leverage information from multi-domain upstream data with a large number of samples to a single-domain…
Latent Schr{ö}dinger Bridge Diffusion Model for Generative Learning
Yuling Jiao, Lican Kang, Huazhen Lin +2
This paper aims to conduct a comprehensive theoretical analysis of current diffusion models. We introduce a novel generative learning methodology utilizing the Schr{ö}dinger bridg…