I am a graduate student in the School of Computer Science at Tel-Aviv University, where I am advised by Lior Wolf. I hold an MS.c. in Physics from Tel-Aviv University, where I was supervised by Nissan Itzhaki. In addition, I work as an Applied Scientist at Amazon.
Publications
- M. Rotman, A. Dekel, R. Ber, L. Wolf and Y. Oz. Semi-supervised Learning of Partial Differential Operators and Dynamical Flows, Uncertainty in Artificial Intelligence (UAI), 2023. Preliminary arXiv version
- M. Rotman and L. Wolf. Energy Regularized RNNs for Solving Non-Stationary Bandit Problems.
International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023. Preliminary arXiv version
- E. Sheffi, M. Rotman and L. Wolf. Gradient Adjusting Networks for Domain Inversion.
Scandinavian Conference on Image Analysis (SCIA), 2023. Preliminary arXiv version
- M. Rotman, A. Dekel, S. Gur, Y. Oz, and L. Wolf. Unsupervised disentanglement with tensor product representations
on the torus. International Conference on Learning Representations (ICLR), 2022.
- M. Rotman and L. Wolf. Natural statistics of network activations and implications for knowledge distillation.
International Conference on Image Processing (ICIP), 2021. Preliminary arXiv version
- I. Reis, M. Rotman, D. Poznanski, J.X. Prochaska, and L. Wolf. Effectively using unsupervised machine learning in
next generation astronomical surveys. Astronomy and Computing, 2021. Preliminary arXiv version
- M. Rotman and L. Wolf. Shuffling recurrent neural networks. AAAI Conference on Artificial Intelligence (AAAI),
2021.
- M. Rotman, R. Brada, I. Beniaminy, S. Ahn, C. J. Hardy, and L. Wolf. Correcting motion artifacts in MRI scans
using a deep neural network with automatic motion timing detection. In Medical Imaging 2021: Physics of Medical
Imaging (SPIE), 2021. Preliminary arXiv version
- M. Rotman and L. Wolf. Electric analog circuit design with hypernetworks and a differential simulator.
International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020. Preliminary arXiv version
- M. Rotman, I. Reis, D. Poznanski, and L. Wolf. Detect the unexpected: Novelty detection in large astrophysical
surveys using fisher vectors. International Conference on Knowledge Discovery and Information Retrieval (KDIR),
2019.
- R. Brada, M. Rotman, R. Wein, S. Ahn, I. Malkiel, and C. J. Hardy. Towards motion-robust MRI – autonomous motion timing and correction during mr scanning using multi-coil data and a deep-learning neural network. International Society for Magnetic Resonance in Medicine 27th Annual Meeting (ISMRM), 2019.
- O. Koshorek, A. Cohen, N. Mor, M. Rotman, and J. Berant. Text segmentation as a supervised learning task. North
American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), 2018.