238 citations · 747 across the 19 of their papers we have counts for
20 papers
Neural-Guided RuntimePrediction of Planners for Improved Motion and Task Planning with Graph Neural Networks
Simon Odense, Kamal Gupta, William G. Macready
The past decade has amply demonstrated the remarkable functionality that can be realized by learning complex input/output relationships. Algorithmically, one of the most important…
A Robust Learning Approach to Domain Adaptive Object Detection
Mehran Khodabandeh, Arash Vahdat, Mani Ranjbar +1
Domain shift is unavoidable in real-world applications of object detection. For example, in self-driving cars, the target domain consists of unconstrained road environments which c…
Undirected Graphical Models as Approximate Posteriors
Arash Vahdat, Evgeny Andriyash, William G. Macready
The representation of the approximate posterior is a critical aspect of effective variational autoencoders (VAEs). Poor choices for the approximate posterior have a detrimental imp…
Solving SAT and MaxSAT with a Quantum Annealer: Foundations, Encodings, and Preliminary Results
Zhengbing Bian, Fabian Chudak, William Macready +3
Quantum annealers (QAs) are specialized quantum computers that minimize objective functions over discrete variables by physically exploiting quantum effects. Current QA platforms a…
Semi-Supervised Semantic Image Segmentation with Self-correcting Networks
Mostafa S. Ibrahim, Arash Vahdat, Mani Ranjbar +1
Building a large image dataset with high-quality object masks for semantic segmentation is costly and time consuming. In this paper, we introduce a principled semi-supervised frame…
Improved Gradient-Based Optimization Over Discrete Distributions
Evgeny Andriyash, Arash Vahdat, Bill Macready
In many applications we seek to maximize an expectation with respect to a distribution over discrete variables. Estimating gradients of such objectives with respect to the distribu…