Hi there
Teaching/Education
- uvadlc_notebooks: Jupyter notebook tutorials for the Deep Learning course at UvA. They cover basic deep learning topics such as initialization and optimization, to more complex topics including Normalizing Flows, Vision Transformers and Meta Learning. All notebooks executed can be viewed on our RTD website, and are integrated in PyTorch Lightning's documentation.
- UvA_summaries: A collection of summaries that I wrote during my Master studies of Artificial Intelligence at the University of Amsterdam (2018-2020). Topics cover courses including Machine Learning, Reinforcement Learning, and many more.
Research
- CITRIS (
📚 CITRIS: Causal Identifiability from Temporal Intervened Sequences - ICML 2022,📚 iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects): We identify causal variables and their (instantaneous) causal graph from videos with interventions. - ENCO (
📚 Efficient Neural Causal Discovery without Acyclicity Constraints - ICLR 2022): We scale neural causal structure learning to 1000 variables by replacing constrained optimization with orientation-based parameterization. - CategoricalNF (
📚 Categorical Normalizing Flows via Continuous Transformations - ICLR 2021): We explore the application of normalizing flows on categorical data and propose a permutation-invariant generative model on graphs, called GraphCNF. On molecule generation, GraphCNF outperforms both one-shot and autoregressive flow-based state-of-the-art of its time.