Marco Morik
Marco Morik
Doctoral Researcher
Marco Morik is a PhD Student in the Machine Learning Group at Technische Universität Berlin working on Uncertainty Estimation and Generative Models for Inverse Problems. He received his master degree in Computer Science from TU Berlin in 2019 with a focus on discrete optimization and Machine Learning. Since 2022 he is working in the Research Group Probabilistic Modeling and Inference with Dr. Shinichi Nakajima.
Best Paper Award SIGIR 2020
- Deep Generative Models
- Probabilistic Models
- Uncertainty Estimation
- Inverse Problems
Marco Morik, Jesse Palarus, Carmen Vidaurre, Klaus-Robert Müller, Shinichi Nakajima
Decoupling Time and Space: A Temporally Conditioned Refinement for EEG Source Imaging
Sidney Bender, Benedikt Kunz, Ahmed Zeid, Shinichi Nakajima, Klaus-Robert Müller, Marco Morik
Towards Fast and Disentangled Counterfactuals for Visual Foundation Models
Marco Morik, Xiao Ruiting, Shinichi Nakajima, Stefan Haufe, Ismail Huseynov
Structure-Preserving Correction Learning for Sparse Bayesian Inference in Brain Source Imaging
Marco Morik, Erick Costa e Silva Talarico, Matheus Cafaro Arouca Sobreira, Luiz Alberto Barbosa de Lima, Shinichi Nakajima
Uncertainty aware pre-stack seismic inversion using probabilistic neural networks
Marco Morik, Laure Ciernik, Lukas Thede, Luca Eyring, Shinichi Nakajima, Zeynep Akata, Lukas Muttenthaler
Revealing Task-Dependent Layer Relevance via Attentive Multi-Layer Fusion
BIFOLD researchers present three papers at ICLR 2024
The International Conference on Learning Representations (ICLR) is a Core-A gathering of experts who are dedicated to advancing a branch of artificial intelligence known as representation learning, which is also called deep learning.