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Understanding How Encoder-Decoder Architectures Attend., , , and . NeurIPS, page 22184-22195. (2021)Reverse engineering learned optimizers reveals known and novel mechanisms., , , , and . NeurIPS, page 19910-19922. (2021)Learning to Learn Without Labels., , , and . ICLR (Workshop), OpenReview.net, (2018)Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics., , , , and . NeurIPS, page 15670-15679. (2019)Recurrent Segmentation for Variable Computational Budgets., , , and . CVPR Workshops, page 1648-1657. Computer Vision Foundation / IEEE Computer Society, (2018)From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction., , , , , and . NeurIPS, page 8535-8545. (2019)Meta-Learning Update Rules for Unsupervised Representation Learning., , , and . ICLR, OpenReview.net, (2019)Deep Learning Models of the Retinal Response to Natural Scenes., , , , and . NIPS, page 1361-1369. (2016)Universality and individuality in neural dynamics across large populations of recurrent networks., , , , and . NeurIPS, page 15603-15615. (2019)Understanding and correcting pathologies in the training of learned optimizers., , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 4556-4565. PMLR, (2019)