Learning representations that accurately capture long-range dependencies insequential inputs -- including text, audio, and genomic data -- is a keyproblem in deep learning. Feed-forward convolutional models capture onlyfeature interactions within finite receptive fields while recurrentarchitectures can be slow and difficult to train due to vanishing gradients.Here, we propose Temporal Feature-Wise Linear Modulation (TFiLM) -- a novelarchitectural component inspired by adaptive batch normalization and itsextensions -- that uses a recurrent neural network to alter the activations ofa convolutional model. This approach expands the receptive field ofconvolutional sequence models with minimal computational overhead. Empirically,we find that TFiLM significantly improves the learning speed and accuracy offeed-forward neural networks on a range of generative and discriminativelearning tasks, including text classification and audio super-resolution