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A reading list of some NLP topics
nlp-reading's Introduction
Multimodal Representation Learning
Kevin Murphy (2023) "Probabilistic Machine Learning: Advanced Topics" "Probabilistic Machine Learning: Advanced Topics" - Chapter 32 - Representation Learning.
Wu, H., Mao, J., Zhang, Y., Jiang, Y., Li, L., Sun, W., & Ma, W. Y. (2019). "Unified visual-semantic embeddings: Bridging vision and language with structured meaning representations" . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6609-6618).
Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). "Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks" . Advances in neural information processing systems, 32.
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021, July). "Learning transferable visual models from natural language supervision" . In International conference on machine learning (pp. 8748-8763). PMLR.
Shi, Y., Paige, B., & Torr, P. (2019). "Variational mixture-of-experts autoencoders for multi-modal deep generative models" . Advances in Neural Information Processing Systems, 32.
Dew, Ryan, Asim Ansari, and Olivier Toubia. "Letting logos speak: Leveraging multiview representation learning for data-driven branding and logo design." Marketing Science 41.2 (2022): 401-425.
Tian, Zijun and Dew, Ryan and Iyengar, Raghuram. "Mega or Micro? Influencer Selection Using Follower Elasticity" . Working Paper . Available at SSRN: https://ssrn.com/abstract=4173421 or http://dx.doi.org/10.2139/ssrn.4173421
Burnap, Alex, John R. Hauser, and Artem Timoshenko. "Product aesthetic design: A machine learning augmentation." . Forthcoming . Marketing Science (2023).
Davide Costa, Lucio La Cava, and Andrea Tagarelli. 2023. "Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction" . In Proceedings of the ACM Web Conference 2023 (WWW '23). Association for Computing Machinery, New York, NY, USA, 1875–1885. https://doi.org/10.1145/3543507.3583520
Cheng, Zhaoqi and Lee, Dokyun and Tambe, Prasanna. "InnoVAE: Generative AI for Understanding Patents and Innovation" . Working Paper . Available at SSRN: https://ssrn.com/abstract=3868599 or http://dx.doi.org/10.2139/ssrn.3868599
Kutuzov, A., Øvrelid, L., Szymanski, T., & Velldal, E. (2018). "Diachronic word embeddings and semantic shifts: a survey" . arXiv preprint arXiv:1806.03537.
Rudolph, M., & Blei, D. (2018, April). "Dynamic embeddings for language evolution" . In Proceedings of the 2018 world wide web conference (pp. 1003-1011).
Bamler, R., & Mandt, S. (2017, July). "Dynamic word embeddings" . In International conference on Machine learning (pp. 380-389). PMLR.
Dieng, A. B., Ruiz, F. J., & Blei, D. M. (2019). "The dynamic embedded topic model" . arXiv preprint arXiv:1907.05545.
Giulianelli, M., Del Tredici, M., & Fernández, R. (2020). "Analysing lexical semantic change with contextualised word representations" . arXiv preprint arXiv:2004.14118.
Dhingra, B., Cole, J. R., Eisenschlos, J. M., Gillick, D., Eisenstein, J., & Cohen, W. W. (2022). "Time-aware language models as temporal knowledge bases" . Transactions of the Association for Computational Linguistics, 10, 257-273.
Garg, N., Schiebinger, L., Jurafsky, D., & Zou, J. (2018). "Word embeddings quantify 100 years of gender and ethnic stereotypes" . Proceedings of the National Academy of Sciences, 115(16), E3635-E3644.
Soni, S., Lerman, K., & Eisenstein, J. (2021). "Follow the leader: Documents on the leading edge of semantic change get more citations" . Journal of the Association for Information Science and Technology, 72(4), 478-492.
Lucy, L., & Bamman, D. (2021). "Characterizing English variation across social media communities with BERT" . Transactions of the Association for Computational Linguistics, 9, 538-556.
Hofmarcher, P., Adhikari, S., & Grün, B. (2022). "Gaining Insights on US Senate Speeches Using a Time Varying Text Based Ideal Point Model" . arXiv preprint arXiv:2206.10877.
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