3 citations · 5 across the 10 of their papers we have counts for
11 papers
TrajLoc: Trajectory-Attention Localization for Multi-Object Motion Control
Omer Sela, Inbar Huberman-Spiegelglas, Michael Rotman +2
Controlling the motion of multiple objects in image-to-video (I2V) generation requires preserving object identities while enforcing adherence to distinct target trajectories. This…
Energy Regularized RNNs for Solving Non-Stationary Bandit Problems
Michael Rotman, Lior Wolf
We consider a Multi-Armed Bandit problem in which the rewards are non-stationary and are dependent on past actions and potentially on past contexts. At the heart of our method, we…
Gradient Adjusting Networks for Domain Inversion
Erez Sheffi, Michael Rotman, Lior Wolf
StyleGAN2 was demonstrated to be a powerful image generation engine that supports semantic editing. However, in order to manipulate a real-world image, one first needs to be able t…
Semi-supervised Learning of Partial Differential Operators and Dynamical Flows
Michael Rotman, Amit Dekel, Ran Ilan Ber +2
The evolution of dynamical systems is generically governed by nonlinear partial differential equations (PDEs), whose solution, in a simulation framework, requires vast amounts of c…
Unsupervised Disentanglement with Tensor Product Representations on the Torus
Michael Rotman, Amit Dekel, Shir Gur +2
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 pro…
Natural Statistics of Network Activations and Implications for Knowledge Distillation
Michael Rotman, Lior Wolf
In a matter that is analog to the study of natural image statistics, we study the natural statistics of the deep neural network activations at various layers. As we show, these sta…