273 citations · 988 across the 24 of their papers we have counts for
3 papers · 1 filter
Transferring Knowledge across Learning Processes
Sebastian Flennerhag, Pablo G. Moreno, Neil D. Lawrence +1
In complex transfer learning scenarios new tasks might not be tightly linked to previous tasks. Approaches that transfer information contained only in the final parameters of a sou…
Deep Gaussian Processes with Convolutional Kernels
Vinayak Kumar, Vaibhav Singh, P. K. Srijith +1
Deep Gaussian processes (DGPs) provide a Bayesian non-parametric alternative to standard parametric deep learning models. A DGP is formed by stacking multiple GPs resulting in a we…
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa
Jie Yang, Thomas Drake, Andreas Damianou +1
This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesia…