11 papers
An overview of condensation phenomenon in deep learning
Zhi-Qin John Xu, Yaoyu Zhang, Zhangchen Zhou
In this paper, we provide an overview of a common phenomenon, condensation, observed during the nonlinear training of neural networks: During the nonlinear training of neural netwo…
Probability Signature: Bridging Data Semantics and Embedding Structure in Language Models
Junjie Yao, Zhi-Qin John Xu
The embedding space of language models is widely believed to capture the semantic relationships; for instance, embeddings of digits often exhibit an ordered structure that correspo…
Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism
Zhiwei Wang, Yunji Wang, Zhongwang Zhang +7
Large language models have consistently struggled with complex reasoning tasks, such as mathematical problem-solving. Investigating the internal reasoning mechanisms of these model…
Solving multiscale dynamical systems by deep learning
Junjie Yao, Yuxiao Yi, Liangkai Hang +5
Multiscale dynamical systems, modeled by high-dimensional stiff ordinary differential equations (ODEs) with wide-ranging characteristic timescales, arise across diverse fields of s…
An Analysis for Reasoning Bias of Language Models with Small Initialization
Junjie Yao, Zhongwang Zhang, Zhi-Qin John Xu
Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigate…
Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks
Zhiwei Bai, Tao Luo, Zhi-Qin John Xu +1
Understanding the relation between deep and shallow neural networks is extremely important for the theoretical study of deep learning. In this work, we discover an embedding princi…