Ron Vainshtein — accepted AI-conference papers

2026

Task Tokens: A Flexible Approach to Adapting Behavior Foundation Models

Ron Vainshtein, Zohar Rimon, Shie Mannor, Chen Tessler

ICLR 2026poster

Recent advancements in imitation learning for robotic control have led to transformer-based behavior foundation models (BFMs) that enable multi-modal, human-like control for humanoid agents. These models generate solutions when conditioned on high-level goals or prompts, for example, walking to a co…

Cited by 0 Source ↗code ↗Scholar ↗

2023

NeRN: Learning Neural Representations for Neural Networks

Maor Ashkenazi, Zohar Rimon, Ron Vainshtein, Shir Levi, Elad Richardson, Pinchas Mintz +1

ICLR 2023top-25%

Neural Representations have recently been shown to effectively reconstruct a wide range of signals from 3D meshes and shapes to images and videos. We show that, when adapted correctly, neural representations can be used to directly represent the weights of a pre-trained convolutional neural network,…

Cited by 25 Source ↗PDF ↗code ↗Scholar ↗