98 citations · 109 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
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…
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…