Items where Author is "Zhang, Tianyu"
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A word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Author
Karna, S., Al-Aridi, R., Morrall, D., Masse, J.-P., Sun, C., Zhang, T., Gross, A. J., Hosseinzadeh, H., Martin, É., Krentz, T., Hitchcock, D., & Yuan, L. (2026). Microstructural evolution, defect mitigation, and precipitation behavior in AA6061 via laser powder bed fusion with high-temperature substrate heating. Materials Science and Engineering A, 970, 150474 (22 pages). External link
Zhang, S., Zhang, T., Cheng, J., & Zhou, S. (2025). Who is to Blame: A Comprehensive Review of Challenges and Opportunities in Designer-Developer Collaboration [Discussion or Letter]. Proceedings of the ACM on Human-Computer Interaction, 9(2), 1-32. External link
Masry, A., Rodriguez, J., Zhang, T., Wang, S., Wang, C., Feizi, A., Kalkunte Suresh, A., Puri, A., Jian, X., Noël, P.-A., Tejaswi Madhusudhan, S., Pedersoli, M., Liu, B., Chapados, N., Bengio, Y., Hoque, E., Pal, C. J., Laradji, I., Vázquez, D., ... Rajeswar Mudumba, S. (2025, December). AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Document Understanding [Paper]. 39th Conference on Neural Information Processing Systems (NeurIPS 2025), San Diego, CA, USA. Published in Advances in Neural Information Processing Systems, 38. External link
Rakhsha, A., Madan, K., Zhang, T., Farahmand, A.-M., & Khasahmadi, A. (2025, December). Majority of the Bests: Improving Best-of-N via Bootstrapping [Paper]. 39th Conference on Neural Information Processing Systems (NeurIPS 2025), San Diego, CA, USA. External link
Rodriguez, J., Jian, X., Panigrahi, S. S., Zhang, T., Feizi, A., Puri, A., Kalkunte, A., Savard, F., Masry, A., Nayak, S., Awal, R., Massoud, M., Abaskohi, A., Li, Z., Wang, S., Noel, P.-A., Richter, M. L., Vadacchino, S., Agarwal, S., ... Rajeswar, S. (2025, April). BIGDOCS : an open dataset for training multimodal models on document and code tasks [Paper]. 13th International Conference on Learning Representations (ICLR 2025), Singapore, Singapore. External link
Mai, V., Maisonneuve, P., Zhang, T., Nekoei, H., Paull, L., & Lesage-Landry, A. (2024). Correction to: Multi-agent reinforcement learning for fast-timescale demand response of residential loads. Machine Learning, 113, 3355-3355. External link
Mai, V., Maisonneuve, P., Zhang, T., Nekoei, H., Paull, L., & Lesage-Landry, A. (2023). Multi-agent reinforcement learning for fast-timescale demand response of residential loads. Machine Learning, 113, 5203-5234. External link
Deldar Masrour, P., Tangestani, R., Farrahi, G., Martin, É., Yuan, L., & Zhang, T. (2023). Track-scale anisotropic thermal material model as a viable substitution in selective laser melting. Journal of Design Against Fatigue, 1(3), 19-33. External link
Mai, V., Maisonneuve, P., Zhang, T., Nekoei, H., Paull, L., & Lesage-Landry, A. (2023, May). Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads [Poster]. 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023), London, UK. Published in Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems. External link
Mai, V., Zhang, T., & Lesage-Landry, A. (2021, December). Multi-agent reinforcement learning for renewable integration in the electric power grid [Paper]. NeurIPS Workshop on Tackling Climate with Machine Learning. Unavailable