64 citations · 170 across the 33 of their papers we have counts for
8 papers · 1 filter
Detecting GAN generated errors
Xiru Zhu, Fengdi Che, Tianzi Yang +3
Despite an impressive performance from the latest GAN for generating hyper-realistic images, GAN discriminators have difficulty evaluating the quality of an individual generated sa…
Deep learning for Aerosol Forecasting
Caleb Hoyne, S. Karthik Mukkavilli, David Meger
Reanalysis datasets combining numerical physics models and limited observations to generate a synthesised estimate of variables in an Earth system, are prone to biases against grou…
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning
Sahand Rezaei-Shoshtari, David Meger, Inna Sharf
Motivated by the recursive Newton-Euler formulation, we propose a novel cascaded Gaussian process learning framework for the inverse dynamics of robot manipulators. This approach l…
Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales
Sanjay Thakur, Herke Van Hoof, Gunshi Gupta +1
Neural Network based controllers hold enormous potential to learn complex, high-dimensional functions. However, they are prone to overfitting and unwarranted extrapolations. PAC Ba…
Learning Domain Randomization Distributions for Training Robust Locomotion Policies
Melissa Mozifian, Juan Camilo Gamboa Higuera, David Meger +1
Domain randomization (DR) is a successful technique for learning robust policies for robot systems, when the dynamics of the target robot system are unknown. The success of policie…
Human Motion Prediction via Pattern Completion in Latent Representation Space
Yi Tian Xu, Yaqiao Li, David Meger
Inspired by ideas in cognitive science, we propose a novel and general approach to solve human motion understanding via pattern completion on a learned latent representation space.…