- 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.