TU Wien Informatics

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Zahra Babaiee

Projektass.in(FWF)

Zahra Babaiee

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  • Scalable Offline Reinforcement Learning for Mean Field Games / Brunnbauer, A., Lemmel, J., Babaiee, Z., Neubauer, S., & Grosu, R. (2025). Scalable Offline Reinforcement Learning for Mean Field Games. In S. Das, A. Nowé, & Y. Vorobeychik (Eds.), Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems, {AAMAS} 2025, Detroit, MI, USA, May 19-23, 2025 (pp. 408–417). International Foundation for Autonomous Agents and Multiagent Systems. https://doi.org/10.5555/3709347.3743555
  • The Master Key Filters Hypothesis: Deep Filters Are General / Babaiee, Z., Mohseni Kiasari, P., Rus, D., & Grosu, R. (2025). The Master Key Filters Hypothesis: Deep Filters Are General. In T. Walsh, J. Shah, & Z. Kolter (Eds.), Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence (pp. 1809–1816). AAAI Press. https://doi.org/10.1609/aaai.v39i2.32175
    Projects: MATTO-GBM (2024–2027) / TA-CPS (2023–2028)
  • Visual Graph Arena: Evaluating Visual Conceptualization of Vision and Multimodal Large Language Models / Babaiee, Z., Mohseni Kiasari, P., Rus, D., & Grosu, R. (2025). Visual Graph Arena: Evaluating Visual Conceptualization of Vision and Multimodal Large Language Models. In Forty-second International Conference on Machine Learning : ICML 2025. Forty-second International Conference on Machine Learning (ICML 2025), Vancouver, Canada.
    Project: TA-CPS (2023–2028)
  • Prediction of Tourism Flow with Sparse Geolocation Data / Lemmel, J., Babaiee, Z., Kleinlehner, M., Majic, I., Neubauer, P., Scholz, J., Grosu, R., & Neubauer, S. (2024). Prediction of Tourism Flow with Sparse Geolocation Data. In P. Haber, T. J. Lampoltshammer, & M. Mayr (Eds.), Data Science—Analytics and Applications : Proceedings of the 5th International Data Science Conference—iDSC2023 (pp. 45–52). Springer Cham. https://doi.org/10.1007/978-3-031-42171-6_6
  • We Need Far Fewer Unique Filters Than We Thought / Babaiee, Z., Kiasari, P., Rus, D., & Grosu, R. (2024). We Need Far Fewer Unique Filters Than We Thought. In NeurIPS 2024 Workshop on Scientific Methods for Understanding Deep Learning. SciForDL’24, Vancouver, Canada.
    Project: MATTO-GBM (2024–2027)
  • Unveiling the Unseen: Identifiable Clusters in Trained Depthwise Convolutional Kernels / Babaiee, Z., Mohseni Kiasari, P., Rus, D., & Grosu, R. (2024). Unveiling the Unseen: Identifiable Clusters in Trained Depthwise Convolutional Kernels. In The Twelth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. The Twelfth International Conference on Learning Representations (ICLR 2024), Austria. http://hdl.handle.net/20.500.12708/203933
  • Neural Echos: Depthwise Convolutional Filters Replicate Biological Receptive Fields / Babaiee, Z., Mohseni Kiasari, P., Rus, D., & Grosu, R. (2024). Neural Echos: Depthwise Convolutional Filters Replicate Biological Receptive Fields. In 2024 IEEE Winter Conference on Applications of Computer Vision (pp. 8216–8225). https://doi.org/10.1109/WACV57701.2024.00803
    Project: MATTO-GBM (2024–2027)
  • Investigation and benchmarking of U-Nets on prostate segmentation tasks / Bhandary, S., Kuhn, D., Babaiee, Z., Fechter, T., Benndorf, M., Zamboglou, C., Grosu, A.-L., & Grosu, R. (2023). Investigation and benchmarking of U-Nets on prostate segmentation tasks. Computerized Medical Imaging and Graphics, 107, Article 102241. https://doi.org/10.1016/j.compmedimag.2023.102241
    Download: PDF (1.58 MB)
    Project: PersoRad (2020–2023)
  • Deep-Learning vs Regression: Prediction of Tourism Flow with Limited Data / Lemmel, J., Babaiee, Z., Kleinlehner, M., Majic, I., Neubauer, P., Scholz, J., Grosu, R., & Neubauer, S. (2022). Deep-Learning vs Regression: Prediction of Tourism Flow with Limited Data. In Schedule - IJCAI’22 Workshop. AI4TS: AI for Time Series Analysis. IJCAI’22 Workshop - AI4TS: AI for Time Series Analysis, Vienna, Austria. IJCAI. https://doi.org/10.34726/4262
    Download: PDF (257 KB)
  • On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification / Babaiee, Z., Hasani, R., Lechner, M., Rus, D., & Grosu, R. (2021). On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification. In International Conference on Machine Learning (pp. 478–489). Proceedings of Machine Learning Research. http://hdl.handle.net/20.500.12708/55625