| Pruning neural networks without any data by iteratively conserving synaptic flow H Tanaka, D Kunin, DLK Yamins, S Ganguli NeurIPS (Advances in Neural Information Processing Systems), 2020 | 730 | 2020 |
| Interpreting the retinal neural code for natural scenes: From computations to neurons N Maheswaranathan*, LT McIntosh*, H Tanaka*, S Grant*, DB Kastner, ... Neuron 111 (17), 2742-2755. e4, 2023 | 85* | 2023 |
| Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics D Kunin, J Sagastuy-Brena, S Ganguli, DLK Yamins, H Tanaka ICLR (International Conference on Learning and Representations), 2020 | 79 | 2020 |
| From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction H Tanaka, A Nayebi, N Maheswaranathan, L McIntosh, SA Baccus, ... NeurIPS (Advances in Neural Information Processing Systems), 2019 | 78 | 2019 |
| Spatial gene drives and pushed genetic waves H Tanaka, HA Stone, DR Nelson PNAS (Proceedings of the National Academy of Sciences), 2017 | 76 | 2017 |
| Mechanistic mode connectivity ES Lubana, EJ Bigelow, RP Dick, D Krueger, H Tanaka International Conference on Machine Learning, 22965-23004, 2023 | 53* | 2023 |
| Beyond BatchNorm: towards a unified understanding of normalization in deep learning ES Lubana, R Dick, H Tanaka NeurIPS (Advances in Neural Information Processing Systems) 34, 4778-4791, 2021 | 53 | 2021 |
| Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks S Jain, R Kirk, ES Lubana, RP Dick, H Tanaka, E Grefenstette, ... ICLR (International Conference on Learning and Representations), 2023 | 44 | 2023 |
| Compositional abilities emerge multiplicatively: Exploring diffusion models on a synthetic task M Okawa, ES Lubana, R Dick, H Tanaka Advances in Neural Information Processing Systems 36, 2024 | 37 | 2024 |
| Noether’s learning dynamics: Role of symmetry breaking in neural networks H Tanaka, D Kunin NeurIPS (Advances in Neural Information Processing Systems) 34, 25646-25660, 2021 | 35* | 2021 |
| Hot particles attract in a cold bath H Tanaka, AA Lee, MP Brenner Physical Review Fluids, 2017 | 33 | 2017 |
| What shapes the loss landscape of self-supervised learning? L Ziyin, ES Lubana, M Ueda, H Tanaka ICLR (International Conference on Learning and Representations), 2022 | 26 | 2022 |
| Rethinking the limiting dynamics of SGD: modified loss, phase space oscillations, and anomalous diffusion D Kunin, J Sagastuy-Brena, L Gillespie, E Margalit, H Tanaka, S Ganguli, ... arXiv, 2021 | 20* | 2021 |
| A lexical approach for identifying behavioural action sequences G Reddy, L Desban, H Tanaka, J Roussel, O Mirat, C Wyart PLoS computational biology 18 (1), e1009672, 2022 | 18 | 2022 |
| Non-Hermitian quasilocalization and ring attractor neural networks H Tanaka, DR Nelson Physical Review E, 2019 | 17 | 2019 |
| Mutation at Expanding Front of Self-Replicating Colloidal Clusters H Tanaka, Z Zeravcic, MP Brenner Physical Review Letters, 2016 | 12 | 2016 |
| How capable can a transformer become? a study on synthetic, interpretable tasks R Ramesh, ES Lubana, M Khona, RP Dick, H Tanaka arXiv preprint arXiv:2311.12997, 2023 | 10 | 2023 |
| CORNN: Convex optimization of recurrent neural networks for rapid inference of neural dynamics F Dinc, A Shai, M Schnitzer, H Tanaka Advances in Neural Information Processing Systems 36, 51273-51301, 2023 | 8 | 2023 |
| Quenched metastable vortex states in Sr 2 RuO 4 D Shibata, H Tanaka, S Yonezawa, T Nojima, Y Maeno Physical Review B, 2015 | 6 | 2015 |
| Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks R Ramesh, ES Lubana, M Khona, RP Dick, H Tanaka Forty-first International Conference on Machine Learning, 0 | 6 | |
Surya GanguliAssociate Professor, Stanford UniversityVerified email at stanford.edu
