1 citations · 1 across the 6 of their papers we have counts for
6 papers
Drawback of Enforcing Equivariance and its Compensation via the Lens of Expressive Power
Yuzhu Chen, Tian Qin, Xinmei Tian +2
Equivariant neural networks encode the intrinsic symmetry of data as an inductive bias, which has achieved impressive performance in wide domains. However, the understanding to the…
A Theoretical Perspective: How to Prevent Model Collapse in Self-consuming Training Loops
Shi Fu, Yingjie Wang, Yuzhu Chen +2
High-quality data is essential for training large generative models, yet the vast reservoir of real data available online has become nearly depleted. Consequently, models increasin…
HRP: High-Rank Preheating for Superior LoRA Initialization
Yuzhu Chen, Yingjie Wang, Shi Fu +4
This paper studies the crucial impact of initialization in Low-Rank Adaptation (LoRA). Through theoretical analysis, we demonstrate that the fine-tuned result of LoRA is highly sen…
Towards Theoretical Understandings of Self-Consuming Generative Models
Shi Fu, Sen Zhang, Yingjie Wang +2
This paper tackles the emerging challenge of training generative models within a self-consuming loop, wherein successive generations of models are recursively trained on mixtures o…
Good Questions Help Zero-Shot Image Reasoning
Kaiwen Yang, Tao Shen, Xinmei Tian +4
Aligning the recent large language models (LLMs) with computer vision models leads to large vision-language models (LVLMs), which have paved the way for zero-shot image reasoning t…
Optical Quantum Sensing for Agnostic Environments via Deep Learning
Zeqiao Zhou, Yuxuan Du, Xu-Fei Yin +3
Optical quantum sensing promises measurement precision beyond classical sensors termed the Heisenberg limit (HL). However, conventional methodologies often rely on prior knowledge…