References
- [1]
- Š. Mandlík, M. Račinský, V. Lisý and T. Pevný. JsonGrinder.jl: automated differentiable neural architecture for embedding arbitrary JSON data. Journal of Machine Learning Research 23, 1–5 (2022).
- [2]
- T. Pevný and P. Somol. Discriminative models for multi-instance problems with tree-structure. CoRR abs/1703.02868 (2017), arXiv:1703.02868.
- [3]
- T. Pevný and P. Somol. Using Neural Network Formalism to Solve Multiple-Instance Problems. In: Advances in Neural Networks - ISNN 2017 - 14th International Symposium, ISNN 2017, Sapporo, Hakodate, and Muroran, Hokkaido, Japan, June 21-26, 2017, Proceedings, Part I, Vol. 10261 of Lecture Notes in Computer Science, edited by F. Cong, A. Leung and Q. Wei (Springer, 2017); pp. 135–142.
- [4]
- Š. Mandlík and T. Pevný. Mapping the Internet: Modelling Entity Interactions in Complex Heterogeneous Networks. Master's thesis, Czech Technical University (2020).
- [5]
- T. G. Dietterich, R. H. Lathrop and T. Lozano-Pérez. Solving the Multiple Instance Problem with Axis-Parallel Rectangles. Artif. Intell. 89, 31–71 (1997).
- [6]
- T. Pevný and V. Kovarı́k. Approximation capability of neural networks on spaces of probability measures and tree-structured domains. CoRR abs/1906.00764 (2019), arXiv:1906.00764.
- [7]
- R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of eugenics 7, 179–188 (1936).
- [8]
- O. Z. Kraus, L. J. Ba and B. J. Frey. Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning. CoRR abs/1511.05286 (2015), arXiv:1511.05286.
- [9]
- Gülçehre, K. Cho, R. Pascanu and Y. Bengio. Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks. In: Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part I, Vol. 8724 of Lecture Notes in Computer Science, edited by T. Calders, F. Esposito, E. Hüllermeier and R. Meo (Springer, 2014); pp. 530–546.
- [10]
- T. Pevný and M. Dedic. Nested Multiple Instance Learning in Modelling of HTTP network traffic. CoRR abs/2002.04059 (2020), arXiv:2002.04059.