| Recovering gene interactions from single-cell data using data diffusion D Van Dijk, R Sharma, J Nainys, K Yim, P Kathail, AJ Carr, C Burdziak, ... Cell 174 (3), 716-729. e27, 2018 | 1595* | 2018 |
| Visualizing structure and transitions in high-dimensional biological data KR Moon, D Van Dijk, Z Wang, S Gigante, DB Burkhardt, WS Chen, K Yim, ... Nature biotechnology 37 (12), 1482-1492, 2019 | 944* | 2019 |
| Recipe for a general, powerful, scalable graph transformer L Rampášek, M Galkin, VP Dwivedi, AT Luu, G Wolf, D Beaini Advances in Neural Information Processing Systems 35, 14501-14515, 2022 | 444 | 2022 |
| Exploring single-cell data with deep multitasking neural networks M Amodio, D van Dijk, K Srinivasan, WS Chen, H Mohsen, KR Moon, ... Nature Methods 16, 1139–1145, 2019 | 309 | 2019 |
| Quantifying the effect of experimental perturbations at single-cell resolution DB Burkhardt, JS Stanley, A Tong, AL Perdigoto, SA Gigante, KC Herold, ... Nature Biotechnology 39 (5), 619-629, 2021 | 181* | 2021 |
| Long range graph benchmark VP Dwivedi, L Rampášek, M Galkin, A Parviz, G Wolf, AT Luu, D Beaini Advances in Neural Information Processing Systems 35, 22326-22340, 2022 | 175 | 2022 |
| Manifold learning-based methods for analyzing single-cell RNA-sequencing data KR Moon, JS Stanley III, D Burkhardt, D van Dijk, G Wolf, S Krishnaswamy Current Opinion in Systems Biology 7, 36-46, 2018 | 157 | 2018 |
| Trajectorynet: A dynamic optimal transport network for modeling cellular dynamics A Tong, J Huang, G Wolf, D Van Dijk, S Krishnaswamy International conference on machine learning, 9526-9536, 2020 | 141 | 2020 |
| Geometric scattering for graph data analysis F Gao, G Wolf, M Hirn Proceedings of the 36th International Conference on Machine Learning, PMLR …, 2019 | 135 | 2019 |
| Improving and generalizing flow-based generative models with minibatch optimal transport A Tong, N Malkin, G Huguet, Y Zhang, J Rector-Brooks, K Fatras, G Wolf, ... Transactions on Machine Learning Research, 2024 | 119* | 2024 |
| Scattering GCN: Overcoming oversmoothness in graph convolutional networks Y Min, F Wenkel, G Wolf Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020 | 119 | 2020 |
| Multiscale PHATE identifies multimodal signatures of COVID-19 M Kuchroo, J Huang, P Wong, JC Grenier, D Shung, A Tong, C Lucas, ... Nature biotechnology 40 (5), 681-691, 2022 | 70* | 2022 |
| Structural and developmental principles of neuropil assembly in C. elegans MW Moyle, KM Barnes, M Kuchroo, A Gonopolskiy, LH Duncan, ... Nature 591 (7848), 99-104, 2021 | 68 | 2021 |
| Uncovering the topology of time-varying fMRI data using cubical persistence B Rieck, T Yates, C Bock, K Borgwardt, G Wolf, N Turk-Browne, ... Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020 | 64 | 2020 |
| Uncovering axes of variation among single-cell cancer specimens WS Chen, N Zivanovic, D Van Dijk, G Wolf, B Bodenmiller, ... Nature methods 17 (3), 302-310, 2020 | 50 | 2020 |
| Reliability of CKA as a Similarity Measure in Deep Learning MR Davari, S Horoi, A Natik, G Lajoie, G Wolf, E Belilovsky ICLR 2023, 2023 | 40* | 2023 |
| Manifold interpolating optimal-transport flows for trajectory inference G Huguet, DS Magruder, A Tong, O Fasina, M Kuchroo, G Wolf, ... Advances in neural information processing systems 35, 29705-29718, 2022 | 37 | 2022 |
| Geometry regularized autoencoders AF Duque, S Morin, G Wolf, KR Moon IEEE transactions on pattern analysis and machine intelligence 45 (6), 7381-7394, 2022 | 35* | 2022 |
| Geometry-based data generation O Lindenbaum, JS Stanley III, G Wolf, S Krishnaswamy Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 2018 | 35 | 2018 |
| Fixing bias in reconstruction-based anomaly detection with lipschitz discriminators A Tong, G Wolf, S Krishnaswamy 2020 IEEE 30th International Workshop on Machine Learning for Signal …, 2020 | 34* | 2020 |
Smita KrishnaswamyYale UniversityVerified email at yale.edu
