235 citations · 865 across the 103 of their papers we have counts for
14 papers · 1 filter
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors
Edward Kim, Divya Gopinath, Corina Pasareanu +1
Even as deep neural networks have become very effective for tasks in vision and perception, it remains difficult to explain and debug their behavior. In this paper, we present a pr…
Modularity in Query-Based Concept Learning
Benjamin Caulfield, Sanjit A. Seshia
We define and study the problem of modular concept learning, that is, learning a concept that is a cross product of component concepts. If an element's membership in a concept depe…
Real-time Funnel Generation for Restricted Motion Planning
Hadi Ravanbakhsh, Forrest Laine, Sanjit A. Seshia
In autonomous systems, a motion planner generates reference trajectories which are tracked by a low-level controller. For safe operation, the motion planner should account for inev…
Counterexample-Guided Synthesis of Perception Models and Control
Shromona Ghosh, Yash Vardhan Pant, Hadi Ravanbakhsh +1
Recent advances in learning-based perception systems have led to drastic improvements in the performance of robotic systems like autonomous vehicles and surgical robots. These perc…
On the Utility of Learning about Humans for Human-AI Coordination
Micah Carroll, Rohin Shah, Mark K. Ho +4
While we would like agents that can coordinate with humans, current algorithms such as self-play and population-based training create agents that can coordinate with themselves. Ag…
Analyzing and Improving Neural Networks by Generating Semantic Counterexamples through Differentiable Rendering
Lakshya Jain, Varun Chandrasekaran, Uyeong Jang +6
Even as deep neural networks (DNNs) have achieved remarkable success on vision-related tasks, their performance is brittle to transformations in the input. Of particular interest a…