241 citations · 299 across the 4 of their papers we have counts for
4 papers
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…
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…
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
Tarek R. Besold, Artur d'Avila Garcez, Sebastian Bader +11
The study and understanding of human behaviour is relevant to computer science, artificial intelligence, neural computation, cognitive science, philosophy, psychology, and several…
Learning Tractable Probabilistic Models for Fault Localization
Aniruddh Nath, Pedro Domingos
In recent years, several probabilistic techniques have been applied to various debugging problems. However, most existing probabilistic debugging systems use relatively simple stat…