72 citations · 102 across the 7 of their papers we have counts for
6 papers · 1 filter
Factor Graph Molecule Network for Structure Elucidation
Hieu Le Trung, Yiqing Xu, Wee Sun Lee
Designing a network to learn a molecule structure given its physical/chemical properties is a hard problem, but is useful for drug discovery tasks. In this paper, we incorporate hi…
Factor Graph Neural Network
Zhen Zhang, Fan Wu, Wee Sun Lee
Most of the successful deep neural network architectures are structured, often consisting of elements like convolutional neural networks and gated recurrent neural networks. Recent…
Differentiable Algorithm Networks for Composable Robot Learning
Peter Karkus, Xiao Ma, David Hsu +3
This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems. A DAN is composed of neural network modules, each encoding a…
Particle Filter Recurrent Neural Networks
Xiao Ma, Peter Karkus, David Hsu +1
Recurrent neural networks (RNNs) have been extraordinarily successful for prediction with sequential data. To tackle highly variable and noisy real-world data, we introduce Particl…
Factored Contextual Policy Search with Bayesian Optimization
Robert Pinsler, Peter Karkus, Andras Kupcsik +2
Scarce data is a major challenge to scaling robot learning to truly complex tasks, as we need to generalize locally learned policies over different task contexts. Contextual policy…
Robustness of Bayesian Pool-based Active Learning Against Prior Misspecification
Nguyen Viet Cuong, Nan Ye, Wee Sun Lee
We study the robustness of active learning (AL) algorithms against prior misspecification: whether an algorithm achieves similar performance using a perturbed prior as compared to…