60 citations · 83 across the 10 of their papers we have counts for
5 papers · 1 filter
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…