As a proponent of open source code, below you can find the source code of several publications I have worked on:
- On the topic of polynomial networks:
- The source code from our work (CVPR'20/PAMI-21) on Π-nets. Source code for diverse experiments: data generation, data classifcation, face recognition and non-euclidean representation learning.
- The source code from our work (NeurIPS'21) on conditional generation.
- The source code from our work (ECCV'22) on polynomial expansions for recognition.
- Introductory jupyter notebooks from our tutorials at CVPR'22, AAAI'23. I suggest checking out the tutorials if you are new to working with polynomial nets, as they are designed to be educational.
- The source code from our work (NeurIPS'22) on verification of the polynomial expansions (for recognition).
- The source code from our work (NeurIPS'22) on extrapolation and spectral bias of networks with Hadamard product.
- The source code from our work (CVPR'23) on regularization of the polynomial networks (for recognition).
- Source code for our work (on NeurIPS'23) on the maximum independent set (MIS).
- Source code for our paper (TMLR'23) on federated learning with covariate shift.
- Source code for our paper (TMLR'23) on improving the worst class under adversarial training.
- Source code for our review paper (Proceedings of the IEEE, 2021) on tensor methods.
- Source code for our work (ICLR'19/IJCV'20) on robust generative models.
- [older] Source code from our work on deformable tracking.
- [older] Jupyter notebooks from our review paper on deformable tracking.
During my PhD, I had also developed and used this library with simple functions on python.