no code implementations • 2 Apr 2024 • Qixiang Fang, Daniel L. Oberski, Dong Nguyen
Third, we release 4 datasets to support measuring and comparing LLM proficiency in grade school mathematics and science against human populations.
no code implementations • 19 Dec 2023 • Qixiang Fang, Zhihan Zhou, Francesco Barbieri, Yozen Liu, Leonardo Neves, Dong Nguyen, Daniel L. Oberski, Maarten W. Bos, Ron Dotsch
Using this new framework, we design a Transformer-based user model that can produce high-quality general-purpose user representations for instant messaging platforms like Snapchat.
no code implementations • 27 Aug 2020 • Ayoub Bagheri, T. Katrien J. Groenhof, Wouter B. Veldhuis, Pim A. de Jong, Folkert W. Asselbergs, Daniel L. Oberski
To exploit the potential information captured in EHRs, in this study we propose a multimodal recurrent neural network model for cardiovascular risk prediction that integrates both medical texts and structured clinical information.
no code implementations • 24 May 2020 • Paulina Pankowska, Daniel L. Oberski
Many data sources on which clustering is performed are well-known to contain random and systematic measurement errors.
no code implementations • 17 Mar 2020 • Laura Boeschoten, Erik-Jan van Kesteren, Ayoub Bagheri, Daniel L. Oberski
Fair inference in supervised learning is an important and active area of research, yielding a range of useful methods to assess and account for fairness criteria when predicting ground truth targets.
1 code implementation • 8 Nov 2019 • Erik-Jan van Kesteren, Chang Sun, Daniel L. Oberski, Michel Dumontier, Lianne Ippel
We conclude that our method is a viable approach for vertically partitioned data analysis with a wide range of real-world applications.