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Universality and individuality in neural dynamics across large populations of recurrent networks.

N. Maheswaranathan, A. Williams, M. Golub, S. Ganguli, and D. Sussillo. NeurIPS, page 15603-15615. (2019)

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The geometry of integration in text classification RNNs.K. Aitken, V. Ramasesh, A. Garg, Y. Cao, D. Sussillo, and N. Maheswaranathan. CoRR, (2020)Reverse engineering learned optimizers reveals known and novel mechanisms.N. Maheswaranathan, D. Sussillo, L. Metz, R. Sun, and J. Sohl-Dickstein. NeurIPS, page 19910-19922. (2021)Understanding How Encoder-Decoder Architectures Attend.K. Aitken, V. Ramasesh, Y. Cao, and N. Maheswaranathan. NeurIPS, page 22184-22195. (2021)Learning to Learn Without Labels.L. Metz, N. Maheswaranathan, B. Cheung, and J. Sohl-Dickstein. ICLR (Workshop), OpenReview.net, (2018)Using learned optimizers to make models robust to input noise.L. Metz, N. Maheswaranathan, J. Shlens, J. Sohl-Dickstein, and E. Cubuk. CoRR, (2019)Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics.N. Maheswaranathan, A. Williams, M. Golub, S. Ganguli, and D. Sussillo. NeurIPS, page 15670-15679. (2019)Meta-Learning Update Rules for Unsupervised Representation Learning.L. Metz, N. Maheswaranathan, B. Cheung, and J. Sohl-Dickstein. ICLR, OpenReview.net, (2019)Recurrent Segmentation for Variable Computational Budgets.L. McIntosh, D. Sussillo, N. Maheswaranathan, and J. Shlens. CoRR, (2017)Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves.L. Metz, N. Maheswaranathan, C. Freeman, B. Poole, and J. Sohl-Dickstein. CoRR, (2020)Recurrent Segmentation for Variable Computational Budgets.L. McIntosh, N. Maheswaranathan, D. Sussillo, and J. Shlens. CVPR Workshops, page 1648-1657. Computer Vision Foundation / IEEE Computer Society, (2018)

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Universality and individuality in neural dynamics across large populations of recurrent networks.

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Disambiguation of "Maheswaranathan, Niru"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Universality and individuality in neural dynamics across large populations of recurrent networks.

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Universality and individuality in neural dynamics across large populations of recurrent networks.