I’m a PhD student at the Artificial Intelligence and Machine Learning Lab, TU Darmstadt. My main research interests cover a broad range of Machine Learning related topics such as deep models, tractable probabilistic circuits and their applications. In specific, I work on bridging the gap between probabilistic circuits and deep neural networks. We want to push the limits of probabilistic circuits and aim to combine their strengths with the modeling capacity of neural networks.
If you are a motivated student looking for a thesis topic and are interested in the subjects mentioned above, feel free to contact me.
Note: Until 2022 known as Steven Lang.
Contact: steven (dot) lang (at) cs (dot) tu-darmstadt (dot) de
- Towards Coreset Learning in Probabilistic CircuitsIn The 5th Workshop on Tractable Probabilistic Modeling (UAI) 2022
- CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and ComparabilityIn International Conference on Learning Representations (ICLR) 2022
- Elevating Perceptual Sample Quality in Probabilistic Circuits through Differentiable SamplingIn NeurIPS 2021 Workshop on Pre-registration in Machine Learning 2022
- DAFNe: A One-Stage Anchor-Free Deep Model for Oriented Object DetectionarXiv prepreint, arXiv:2109.06148 2021
- Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic CircuitsIn Proceedings of the 37th International Conference on Machine Learning 13–18 jul 2020
- WekaDeeplearning4j: A deep learning package for Weka based on Deeplearning4jKnowledge-Based Systems 13–18 jul 2019