Machine learning for science
Machine learning that shapes science.
- Materials science: Exploring faster ways to compute materials properties and generate new kinds of materials.
- Computational chemistry: Exploring the molecular space and interactions.
- Quantum machine learning: Exploring the interplay between quantum computing and machine learning.
- Variational hyper-encoding networks, P Nguyen, T Tran, S Gupta, S Rana, HC Dam, S Venkatesh, ECML-PKDD'21, 2021
- Hybrid generative-discriminative models for inverse materials design, Phuoc Nguyen, Truyen Tran, Sunil Gupta, Svetha Venkatesh. arXiv preprint arXiv:1811.06060. Extention of:
- Incomplete conditional density estimation for fast materials discovery, Phuoc Nguyen, Truyen Tran, Sunil Gupta, Svetha Venkatesh. SDM'19.
- Committee machine that votes for similarity between materials; Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Tuan-Dung Ho, Truyen Tran, Keisuke Takahashi and Hieu-Chi Dam. IUCrJ, 2018 Nov 1; 5(Pt 6): 830–840.
- Graph transformation policy network for chemical reaction prediction, Kien Do, Truyen Tran, Svetha Venkatesh, KDD'19.
- Relational dynamic memory networks, Trang Pham, Truyen Tran, Svetha Venkatesh, arXiv preprint arXiv:1808.04247. Extension of:
- Attentional multilabel learning over graphs: A message passing approach, K Do, T Tran, T Nguyen, S Venkatesh, Machine Learning, 2019.
- Graph memory networks for molecular activity prediction, Trang Pham, Truyen Tran,
Svetha Venkatesh, ICPR'18.