Items where Author is "Rish, Irina"
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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.
Article
Vianna, P., Calce, S.-I., Boustros, P., Larocque-Rigney, C., Patry-Beaudoin, L., Luo, Y. H., Aslan, E., Marinos, J., Alamri, T. M., Vu, K.-N., Murphy-Lavallée, J., Billiard, J.-S., Montagnon, E., Li, H., Kadoury, S., Nguyen, B. N., Gauthier, S., Therien, B., Rish, I., ... Tang, A. (2023). Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis. Radiology, 309(1), e230659 (10 pages). External link
Paper
Misra, D., Islah, N., May, V., Rauby, B., Wang, Z., Gehring, J., Orvieto, A., Chaudhary, M., Müller, E., Rish, I., Ebrahimi Kahou, S., & Caccia, M. (2026, July). GitChameleon 2.0: Evaluating AI Code Generation Against Python Library Version Incompatibilities [Paper]. 64th Annual Meeting of the Association for Computational Linguistics, San Diego, CA, USA. External link
Rifat Arefin, M., Subbaraj, G., Gontier, N., LeCun, Y., Rish, I., Shwartz-Ziv, R., & Pal, C. J. (2025, April). SEQ-VCR : preventing collapse in intermediate transformer representations for enhanced reasoning [Paper]. 13th International Conference on Learning Representations (ICLR 2025), Singapore, Singapore. External link
Williams, A. R., Ashok, A., Marcotte, É., Zantedeschi, V., Subramanian, J., Riachi, R., Requeima, J., Lacoste, A., Rish, I., Chapados, N., & Drouin, A. (2025, February). Context is Key: A Benchmark for Forecasting with Essential Textual Information [Paper]. 42nd International Conference on Machine Learning (PMLR 2025), Vancouver, BC, Canada. External link
Ashok, A., Williams, A. R., Marcotte, É., Zantedeschi, V., Subramanian, J., Riachi, R., Requeima, J., Lacoste, A., Rish, I., Chapados, N., & Drouin, A. (2024, December). Context is Key: A Benchmark for Forecasting with Essential Textual Information [Paper]. NeurIPS 2024 Workshop on Time Series in the Age of Large Models, Vancouver, BC, Canada (56 pages). External link
Bengio, Y., Gupta, P., Maharaj, T., Rahaman, N., Weiss, M., Deleu, T., Muller, E., Qu, M., Schmidt, V., St-Charles, P.-L., Alsdurf, H., Bilanuik, O., Buckeridge, D., Caron, G. M., Carrier, P.-L., Ghosn, J., Ortiz-Gagne, S., Pal, C. J., Rish, I., ... Williams, A. (2021, May). Predicting infectiousness for proactive contact tracing [Paper]. 9th International Conference on Learning Representations (ICLR 2021), Vienna, Austria (21 pages). External link
Laleh, T., Faramarzi, M., Rish, I., & Anbil Parthipan, S. C. (2020, July). Chaotic continual learning [Paper]. 37th International Conference on Machine Learning (PMLR 2020), Vienna, Austria (6 pages). External link
Master's thesis
Hassen, N. (2021). Approximate Inference in Bayesian Neural Networks [Master's thesis, Polytechnique Montréal]. Available