| Identifying at-risk students in massive open online courses J He, J Bailey, B Rubinstein, R Zhang Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 200 | 2015 |
| Naive bayes classifier for positive unlabeled learning with uncertainty J He, Y Zhang, X Li, Y Wang Proceedings of the 2010 SIAM international conference on data mining, 361-372, 2010 | 30 | 2010 |
| Exploiting transitive similarity and temporal dynamics for similarity search in heterogeneous information networks J He, J Bailey, R Zhang International Conference on Database Systems for Advanced Applications, 141-155, 2014 | 25 | 2014 |
| Learning naive Bayes classifiers from positive and unlabelled examples with uncertainty J He, Y Zhang, X Li, P Shi International journal of systems science 43 (10), 1805-1825, 2012 | 24 | 2012 |
| Molecular optimization by capturing chemist’s intuition using deep neural networks J He, H You, E Sandström, E Nittinger, EJ Bjerrum, C Tyrchan, ... Journal of cheminformatics 13 (1), 1-17, 2021 | 22 | 2021 |
| Chemformer: a pre-trained transformer for computational chemistry R Irwin, S Dimitriadis, J He, EJ Bjerrum Machine Learning: Science and Technology 3 (1), 015022, 2022 | 19 | 2022 |
| MOOCs meet measurement theory: a topic-modelling approach J He, BIP Rubinstein, J Bailey, R Zhang, S Milligan, J Chan Thirtieth AAAI Conference on Artificial Intelligence, 2016 | 15 | 2016 |
| Bayesian classifiers for positive unlabeled learning J He, Y Zhang, X Li, Y Wang International Conference on Web-Age Information Management, 81-93, 2011 | 14 | 2011 |
| Levenshtein augmentation improves performance of smiles based deep-learning synthesis prediction D Sumner, J He, A Thakkar, O Engkvist, EJ Bjerrum | 4 | 2020 |
| Validity: a framework for cross-disciplinary collaboration in mining indicators of learning from MOOC forums S Milligan, J He, J Bailey, R Zhang, BIP Rubinstein proceedings of the sixth international conference on learning analytics …, 2016 | 3 | 2016 |
| Transformer neural network for structure constrained molecular optimization J He, F Mattsson, M Forsberg, EJ Bjerrum, O Engkvist, C Tyrchan, ... | 2 | 2021 |
| Transformer-based molecular optimization beyond matched molecular pairs J He, E Nittinger, C Tyrchan, W Czechtizky, A Patronov, EJ Bjerrum, ... Journal of cheminformatics 14 (1), 1-14, 2022 | 1 | 2022 |
| Implications of Additivity and Nonadditivity for Machine Learning and Deep Learning Models in Drug Design K Kwapien, E Nittinger, J He, C Margreitter, A Voronov, C Tyrchan | | 2022 |
| Transformer Neural Network-Based Molecular Optimization Using General Transformations J He, E Nittinger, C Tyrchan, W Czechtizky, A Patronov, EJ Bjerrum, ... | | 2021 |
| Machine Learning for Feedback in Massive Open Online Courses J He | | 2016 |
| TopicResponse: A Marriage of Topic Modelling and Rasch Modelling for Automatic Measurement in MOOCs J He, BIP Rubinstein, J Bailey, R Zhang, S Milligan arXiv preprint arXiv:1607.08720, 2016 | | 2016 |
