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Michael Rotman

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

UAI Conference 2023 Conference Paper

Semi-supervised learning of partial differential operators and dynamical flows

Michael Rotman
Amit Dekel
Ran Ilan Ber
Lior Wolf
Yaron Oz

The evolution of many dynamical systems is generically governed by nonlinear partial differential equations (PDEs), whose solution, in a simulation framework, requires vast amounts of computational resources. In this work, we present a novel method that combines a hyper-network solver with a Fourier Neural Operator architecture. Our method treats time and space separately and as a result, it successfully propagates initial conditions in continuous time steps by employing the general composition properties of the partial differential operators. Following previous works, supervision is provided at a specific time point. We test our method on various time evolution PDEs, including nonlinear fluid flows in one, two, or three spatial dimensions. The results show that the new method improves the learning accuracy at the time of the supervision point, and can interpolate the solutions to any intermediate time.

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ICLR Conference 2022 Conference Paper

Unsupervised Disentanglement with Tensor Product Representations on the Torus

Michael Rotman
Amit Dekel
Shir Gur
Yaron Oz
Lior Wolf

The current methods for learning representations with auto-encoders almost exclusively employ vectors as the latent representations. In this work, we propose to employ a tensor product structure for this purpose. This way, the obtained representations are naturally disentangled. In contrast to the conventional variations methods, which are targeted toward normally distributed features, the latent space in our representation is distributed uniformly over a set of unit circles. We argue that the torus structure of the latent space captures the generative factors effectively. We employ recent tools for measuring unsupervised disentanglement, and in an extensive set of experiments demonstrate the advantage of our method in terms of disentanglement, completeness, and informativeness. The code for our proposed method is available at https://github.com/rotmanmi/Unsupervised-Disentanglement-Torus.

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AAAI Conference 2021 Conference Paper

Shuffling Recurrent Neural Networks

Michael Rotman
Lior Wolf

We propose a novel recurrent neural network model, where the hidden state ht is obtained by permuting the vector elements of the previous hidden state ht−1 and adding the output of a learned function β (xt) of the input xt at time t. In our model, the prediction is given by a second learned function, which is applied to the hidden state s (ht). The method is easy to implement, extremely efficient, and does not suffer from vanishing nor exploding gradients. In an extensive set of experiments, the method shows competitive results, in comparison to the leading literature baselines. We share our implementation at https: //github. com/rotmanmi/SRNN.

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