211 citations · 409 across the 11 of their papers we have counts for
11 papers
DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction
Yuhuang Hu, Jonathan Binas, Daniel Neil +2
Neuromorphic event cameras are useful for dynamic vision problems under difficult lighting conditions. To enable studies of using event cameras in automobile driving applications,…
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs
Daniel Neil, Joss Briody, Alix Lacoste +3
In this work, we provide a new formulation for Graph Convolutional Neural Networks (GCNNs) for link prediction on graph data that addresses common challenges for biomedical knowled…
PRED18: Dataset and Further Experiments with DAVIS Event Camera in Predator-Prey Robot Chasing
Diederik Paul Moeys, Daniel Neil, Federico Corradi +7
Machine vision systems using convolutional neural networks (CNNs) for robotic applications are increasingly being developed. Conventional vision CNNs are driven by camera frames at…
ADaPTION: Toolbox and Benchmark for Training Convolutional Neural Networks with Reduced Numerical Precision Weights and Activation
Moritz B. Milde, Daniel Neil, Alessandro Aimar +2
Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) are useful for many practical tasks in machine learning. Synaptic weights, as well as neuron activation functio…
DDD17: End-To-End DAVIS Driving Dataset
Jonathan Binas, Daniel Neil, Shih-Chii Liu +1
Event cameras, such as dynamic vision sensors (DVS), and dynamic and active-pixel vision sensors (DAVIS) can supplement other autonomous driving sensors by providing a concurrent s…
Sensor Transformation Attention Networks
Stefan Braun, Daniel Neil, Enea Ceolini +2
Recent work on encoder-decoder models for sequence-to-sequence mapping has shown that integrating both temporal and spatial attention mechanisms into neural networks increases the…