242 citations · 248 across the 16 of their papers we have counts for
5 papers · 1 filter
Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning
Elie Aljalbout, Maximilian Ulmer, Rudolph Triebel
Vision-based reinforcement learning (RL) is a promising approach to solve control tasks involving images as the main observation. State-of-the-art RL algorithms still struggle in t…
Making Curiosity Explicit in Vision-based RL
Elie Aljalbout, Maximilian Ulmer, Rudolph Triebel
Vision-based reinforcement learning (RL) is a promising technique to solve control tasks involving images as the main observation. State-of-the-art RL algorithms still struggle in…
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
Jongseok Lee, Jianxiang Feng, Matthias Humt +2
This paper presents a probabilistic framework to obtain both reliable and fast uncertainty estimates for predictions with Deep Neural Networks (DNNs). Our main contribution is a pr…
Bayesian Active Learning for Sim-to-Real Robotic Perception
Jianxiang Feng, Jongseok Lee, Maximilian Durner +1
While learning from synthetic training data has recently gained an increased attention, in real-world robotic applications, there are still performance deficiencies due to the so-c…
A Survey of Uncertainty in Deep Neural Networks
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali +11
Due to their increasing spread, confidence in neural network predictions became more and more important. However, basic neural networks do not deliver certainty estimates or suffer…