Wang, Xiaolong

Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A. A., & Hardt, M. (2019) · arXiv

Domain Adaptation and Few-Shot LearningData Stream Mining Techniques

Cited by 393

Content‐based citation analysis: The next generation of citation analysis

Ding, Y., Zhang, G., Chambers, T., Song, M., Wang, X., & Zhai, C. (2014) · Journal of the Association for Information Science and Technology

scientometrics and bibliometrics researchWeb visibility and informetrics

Cited by 252

Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach

Liu, B., Fang, L., Liu, F., Wang, X., Chen, J., & Chou, K.-C. (2015) · PLOS ONE

Machine Learning in BioinformaticsRNA and protein synthesis mechanisms

Cited by 238

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration 0

O’Neill, A., Rehman, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., Mandlekar, A., Jain, A., et al. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA)

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 210

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Embodiment Collaboration, O'Neill, A., Rehman, A., Gupta, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., et al. (2023) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 111

A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction

Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., & Zhou, Y. (2020) · International Journal of Machine Learning and Cybernetics

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 64

Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf

Xu, Y., Wang, S., Li, P., Luo, F., Wang, X., Liu, W., & Liu, Y. (2023) · arXiv

Artificial Intelligence in GamesGame Theory and Applications

Cited by 32

Learning to (Learn at Test Time): RNNs with Expressive Hidden States

Sun, Y., Li, X., Dalal, K., Xu, J., Vikram, A., Zhang, G., Dubois, Y., Chen, X., Wang, X., Koyejo, S., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 18

Family history information extraction via deep joint learning

Shi, X., Jiang, D., Huang, Y., Wang, X., Chen, Q., Yan, J., & Tang, B. (2019) · BMC Medical Informatics and Decision Making

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 15

Test-Time Training on Video Streams

Wang, R., Sun, Y., Tandon, A., Gandelsman, Y., Chen, X., Efros, A. A., & Wang, X. (2023) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 4

Finetuning Offline World Models in the Real World

Feng, Y., Hansen, N., Xiong, Z., Rajagopalan, C., & Wang, X. (2023) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 2

Learning to (Learn at Test Time)

Sun, Y., Li, X., Dalal, K., Hsu, C., Koyejo, S., Guestrin, C., Wang, X., Hashimoto, T., & Chen, X. (2023) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 0