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Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations
[article]
2021
arXiv
pre-print
Learning representations that accurately capture long-range dependencies in sequential inputs -- including text, audio, and genomic data -- is a key problem in deep learning. Feed-forward convolutional models capture only feature interactions within finite receptive fields while recurrent architectures can be slow and difficult to train due to vanishing gradients. Here, we propose Temporal Feature-Wise Linear Modulation (TFiLM) -- a novel architectural component inspired by adaptive batch
arXiv:1909.06628v3
fatcat:4jcgn5gr65fdjlqyberafpuvvq