30 citations · 60 across the 5 of their papers we have counts for
7 papers
Imitating Language via Scalable Inverse Reinforcement Learning
Markus Wulfmeier, Michael Bloesch, Nino Vieillard +13
The majority of language model training builds on imitation learning. It covers pretraining, supervised fine-tuning, and affects the starting conditions for reinforcement learning…
Dropout Distillation for Efficiently Estimating Model Confidence
Corina Gurau, Alex Bewley, Ingmar Posner
We propose an efficient way to output better calibrated uncertainty scores from neural networks. The Distilled Dropout Network (DDN) makes standard (non-Bayesian) neural networks m…
Scrutinizing and De-Biasing Intuitive Physics with Neural Stethoscopes
Fabian B. Fuchs, Oliver Groth, Adam R. Kosiorek +4
Visually predicting the stability of block towers is a popular task in the domain of intuitive physics. While previous work focusses on prediction accuracy, a one-dimensional perfo…
Incremental Adversarial Domain Adaptation for Continually Changing Environments
Markus Wulfmeier, Alex Bewley, Ingmar Posner
Continuous appearance shifts such as changes in weather and lighting conditions can impact the performance of deployed machine learning models. While unsupervised domain adaptation…
What Makes a Place? Building Bespoke Place Dependent Object Detectors for Robotics
Jeffrey Hawke, Alex Bewley, Ingmar Posner
This paper is about enabling robots to improve their perceptual performance through repeated use in their operating environment, creating local expert detectors fitted to the place…
Hierarchical Attentive Recurrent Tracking
Adam R. Kosiorek, Alex Bewley, Ingmar Posner
Class-agnostic object tracking is particularly difficult in cluttered environments as target specific discriminative models cannot be learned a priori. Inspired by how the human vi…