60 citations · 81 across the 8 of their papers we have counts for
7 papers
Are Gradient-based Saliency Maps Useful in Deep Reinforcement Learning?
Matthias Rosynski, Frank Kirchner, Matias Valdenegro-Toro
Deep Reinforcement Learning (DRL) connects the classic Reinforcement Learning algorithms with Deep Neural Networks. A problem in DRL is that CNNs are black-boxes and it is hard to…
Unsupervised Difficulty Estimation with Action Scores
Octavio Arriaga, Matias Valdenegro-Toro
Evaluating difficulty and biases in machine learning models has become of extreme importance as current models are now being applied in real-world situations. In this paper we pres…
Black-Box Optimization of Object Detector Scales
Mohandass Muthuraja, Octavio Arriaga, Paul Plöger +2
Object detectors have improved considerably in the last years by using advanced CNN architectures. However, many detector hyper-parameters are generally manually tuned, or they are…
Perception for Autonomous Systems (PAZ)
Octavio Arriaga, Matias Valdenegro-Toro, Mohandass Muthuraja +2
In this paper we introduce the Perception for Autonomous Systems (PAZ) software library. PAZ is a hierarchical perception library that allow users to manipulate multiple levels of…
Evaluating Uncertainty Estimation Methods on 3D Semantic Segmentation of Point Clouds
Swaroop Bhandary K, Nico Hochgeschwender, Paul Plöger +2
Deep learning models are extensively used in various safety critical applications. Hence these models along with being accurate need to be highly reliable. One way of achieving thi…
Image Captioning and Classification of Dangerous Situations
Octavio Arriaga, Paul Plöger, Matias Valdenegro-Toro
Current robot platforms are being employed to collaborate with humans in a wide range of domestic and industrial tasks. These environments require autonomous systems that are able…