temporal-convolutional-networks

Proposes a deep neural model combining multi-layer temporal convolutional networks with label-wise attention for medical coding, achieving significant improvements in F1 and recall scores over previous state-of-the-art.

This paper proposes a Temporal Convolutional Network with symmetric dilation for trajectory inpainting, reconstructing missing segments from observed context using a composite loss function, and evaluates on synthetic 2D trajectories.