Kaizhu Huang
Biographic Data
| ID | 10054939 |
|---|---|
| NAME | Kaizhu Huang |
| GIVEN NAMES | Kaizhu |
| FAMILY NAME | Huang |
| SIGNATURE | HUANG K |
| AFFILIATIONS | Duke Kunshan University |
| ORCID | 0000-0002-3034-9639 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Tpgca
Multiagent reinforcement learning (MARL) methods have good application performances and prospects in cooperative tasks. To improve the capability of agent policy learning in new scenarios, some methods transfer the learned policy knowledge to new scenarios. However, most methods only focus on the knowledge transfer of individual agent policies, neglecting the credit assignment among agents in cooperative tasks, which results in a transfer bias of…
Interpreting Black-Box Models
Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep Learning (DL) models. Majority of these models are inherently complex and lacks explanations of the decision making process causing these models to be termed as 'Black-Box'. One of the major bottleneck…
No prominent works on this page.
Interpreting Black-Box Models
Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep Learning (DL) models. Majority of these models are inherently complex and lacks explanations of the decision making process causing these models to be termed as 'Black-Box'. One of the major bottleneck…
Tpgca
Multiagent reinforcement learning (MARL) methods have good application performances and prospects in cooperative tasks. To improve the capability of agent policy learning in new scenarios, some methods transfer the learned policy knowledge to new scenarios. However, most methods only focus on the knowledge transfer of individual agent policies, neglecting the credit assignment among agents in cooperative tasks, which results in a transfer bias of…
Explainable Artificial Intelligence (XAI (2 works) · Artificial Intelligence (1 work) · Black box (1 work) · Computer Science (1 work) · Computer security (1 work) · Data science (1 work) · Decomposition (1 work) · Domain Adaptation and Few-Shot Learning (1 work) · Field (mathematics) (1 work) · Imbalanced Data Classification Techniques (1 work)