376 citations · 1.1k across the 17 of their papers we have counts for
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cs.LG2020★ 49 cited
Every Model Learned by Gradient Descent Is Approximately a Kernel Machine
Pedro Domingos
Deep learning's successes are often attributed to its ability to automatically discover new representations of the data, rather than relying on handcrafted features like other lear…
cs.RO2020★ 9 cited
Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity
William Agnew, Christopher Xie, Aaron Walsman +4
Learning-based 3D object reconstruction enables single- or few-shot estimation of 3D object models. For robotics, this holds the potential to allow model-based methods to rapidly a…
cs.LG2020
Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning
William Agnew, Pedro Domingos
Current deep reinforcement learning (RL) approaches incorporate minimal prior knowledge about the environment, limiting computational and sample efficiency. \textit{Objects} provid…