4 citations · 7 across the 9 of their papers we have counts for
10 papers
FidelityGPT: Correcting Decompilation Distortions with Retrieval Augmented Generation
Zhiping Zhou, Xiaohong Li, Ruitao Feng +5
Decompilation converts machine code into human-readable form, enabling analysis and debugging without source code. However, fidelity issues often degrade the readability and semant…
The Stabilizer Bootstrap of Quantum Machine Learning with up to 10000 qubits
Yuqing Li, Jinglei Cheng, Xulong Tang +3
Quantum machine learning is considered one of the flagship applications of quantum computers, where variational quantum circuits could be the leading paradigm both in the near-term…
Demystifying Lazy Training of Neural Networks from a Macroscopic Viewpoint
Yuqing Li, Tao Luo, Qixuan Zhou
In this paper, we advance the understanding of neural network training dynamics by examining the intricate interplay of various factors introduced by weight parameters in the initi…
Stochastic Modified Equations and Dynamics of Dropout Algorithm
Zhongwang Zhang, Yuqing Li, Tao Luo +1
Dropout is a widely utilized regularization technique in the training of neural networks, nevertheless, its underlying mechanism and its impact on achieving good generalization abi…
Understanding the Initial Condensation of Convolutional Neural Networks
Zhangchen Zhou, Hanxu Zhou, Yuqing Li +1
Previous research has shown that fully-connected networks with small initialization and gradient-based training methods exhibit a phenomenon known as condensation during training.…
Numerical Stability for Differential Equations with Memory
Guihong Wang, Yuqing Li, Tao Luo +3
In this work, we systematically investigate linear multi-step methods for differential equations with memory. In particular, we focus on the numerical stability for multi-step meth…
Phase Diagram of Initial Condensation for Two-layer Neural Networks
Zhengan Chen, Yuqing Li, Tao Luo +2
The phenomenon of distinct behaviors exhibited by neural networks under varying scales of initialization remains an enigma in deep learning research. In this paper, based on the ea…
Embedding Principle: a hierarchical structure of loss landscape of deep neural networks
Yaoyu Zhang, Yuqing Li, Zhongwang Zhang +2
We prove a general Embedding Principle of loss landscape of deep neural networks (NNs) that unravels a hierarchical structure of the loss landscape of NNs, i.e., loss landscape of…
Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks
Yuqing Li, Tao Luo, Chao Ma
In an attempt to better understand structural benefits and generalization power of deep neural networks, we firstly present a novel graph theoretical formulation of neural network…
Towards an Understanding of Residual Networks Using Neural Tangent Hierarchy (NTH)
Yuqing Li, Tao Luo, Nung Kwan Yip
Gradient descent yields zero training loss in polynomial time for deep neural networks despite non-convex nature of the objective function. The behavior of network in the infinite…