Huang, Gao

Densely Connected Convolutional Networks

Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2016) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 48,960

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 48,566

Learning Efficient Convolutional Networks through Network Slimming

Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., & Zhang, C. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 2,761

Deep Networks with Stochastic Depth

Huang, G., Sun, Y., Liu, Z., Sedra, D., & Weinberger, K. Q. (2016) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsStochastic Gradient Optimization Techniques

Cited by 2,104

CondenseNet: An Efficient DenseNet Using Learned Group Convolutions

Huang, G., Liu, S., Maaten, L. V. D., & Weinberger, K. Q. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 904

Dynamic Neural Networks: A Survey

Han, Y., Huang, G., Song, S., Yang, L., Wang, H., & Wang, Y. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 848

ExpeL: LLM Agents Are Experiential Learners

Zhao, A., Huang, D., Xu, Q., Lin, M., Liu, Y.-J., & Huang, G. (2023) · arXiv

AI-based Problem Solving and PlanningDomain Adaptation and Few-Shot Learning

Cited by 179

Expel: LLM agents are experiential learners

Zhao, A., Huang, D., Xu, Q., Lin, M., Liu, Y.-J., & Huang, G. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence

AI-based Problem Solving and PlanningDomain Adaptation and Few-Shot Learning

Cited by 153

Cross-Iteration Batch Normalization

Yao, Z., Cao, Y., Zheng, S., Huang, G., & Lin, S. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 112

CondenseNet V2: Sparse Feature Reactivation for Deep Networks

Yang, L., Jiang, H., Cai, R., Wang, Y., Song, S., Huang, G., & Tian, Q. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 71

Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory

Zhu, X., Chen, Y., Tian, H., Tao, C., Su, W., Yang, C., Huang, G., Li, B., Lu, L., Wang, X., et al. (2023) · arXiv

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 33

Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning

Wang, S., Yu, L., Gao, C., Zheng, C., Liu, S., Lu, R., Dang, K., Chen, X., Yang, J., Zhang, Z., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 14

How Far is Video Generation from World Model: A Physical Law Perspective

Kang, B., Yue, Y., Lu, R., Lin, Z., Zhao, Y., Wang, K., Huang, G., & Feng, J. (2024) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 7

Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning

Yue, Y., Kang, B., Xu, Z., Huang, G., & Yan, S. (2022) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 4