4 citations · 6 across the 8 of their papers we have counts for
4 papers · 1 filter
Learning Graphs through Continuous Information Entropy Fields
Hui Cong, Bo Sun, Ziheng Jiao +1
Graph theory is inherently descriptive, capturing what relationships exist but not why they arise, because it treats edges as primitive constructs. This paper proposes a new explan…
GRPO-RM: Fine-Tuning Representation Models via GRPO-Driven Reinforcement Learning
Yanchen Xu, Ziheng Jiao, Hongyuan Zhang +1
The Group Relative Policy Optimization (GRPO), a reinforcement learning method used to fine-tune large language models (LLMs), has proved its effectiveness in practical application…
Deep Manifold Learning with Graph Mining
Xuelong Li, Ziheng Jiao, Hongyuan Zhang +1
Admittedly, Graph Convolution Network (GCN) has achieved excellent results on graph datasets such as social networks, citation networks, etc. However, softmax used as the decision…
Non-Gradient Manifold Neural Network
Rui Zhang, Ziheng Jiao, Hongyuan Zhang +1
Deep neural network (DNN) generally takes thousands of iterations to optimize via gradient descent and thus has a slow convergence. In addition, softmax, as a decision layer, may i…