| Disease state prediction from single-cell data using graph attention networks N Ravindra, A Sehanobish, JL Pappalardo, DA Hafler, D van Dijk Proceedings of the ACM conference on health, inference, and learning, 121-130, 2020 | 45 | 2020 |
| Using chinese glyphs for named entity recognition (student abstract) CH Song, A Sehanobish Proceedings of the AAAI Conference on Artificial Intelligence 34 (10), 13921 …, 2020 | 32* | 2020 |
| From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers K Choromanski, H Lin, H Chen, T Zhang, A Sehanobish, V Likhosherstov, ... 39th International Conference on Machine Learning (ICML), 2022, 2022 | 29 | 2022 |
| Hybrid Random Features K Choromanski, H Chen, H Lin, Y Ma, A Sehanobish, D Jain, MS Ryoo, ... Tenth International Conference on Learning Representations (ICLR), 2022 | 24 | 2022 |
| Gaining Insight into Sars-Cov-2 Infection and Covid-19 Severity using Self-supervised Edge Features and Graph Neural Networks A Sehanobish, NG Ravindra, D van Dijk THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE 35 (6), 4864-4873, 2021 | 19 | 2021 |
| Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers) A Loukina, M Morales, R Kumar Proceedings of the 2019 Conference of the North American Chapter of the …, 2019 | 15 | 2019 |
| Learning Potentials of Quantum Systems using Deep Neural Networks A Sehanobish, HH Corzo, O Kara, D van Dijk Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial …, 2021 | 12 | 2021 |
| Patient factors associated with SARS‐CoV‐2 in an admitted emergency department population AD Haimovich, F Warner, HP Young, NG Ravindra, A Sehanobish, ... Journal of the American College of Emergency Physicians Open 1 (4), 569-577, 2020 | 11 | 2020 |
| Self-supervised edge features for improved graph neural network training A Sehanobish, NG Ravindra, D van Dijk arXiv preprint arXiv:2007.04777, 2020 | 6 | 2020 |
| Efficient Graph Field Integrators Meet Point Clouds K Choromanski, A Sehanobish, H Lin, Y Zhao, E Berger, T Parshakova, ... 40th International Conference on Machine Learning (ICML), 5978-6004, 2023 | 5 | 2023 |
| Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning SBR Chowdhury, K Choromanski, A Sehanobish, A Dubey, S Chaturvedi arXiv preprint arXiv:2406.16257, 2024 | 4 | 2024 |
| Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports A Sehanobish, MC Sandora, N Abraham, J Pawar, D Torres, A Das, ... Proceedings of the 2022 Conference of the North American Chapter of the …, 2022 | 4* | 2022 |
| Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks A Sehanobish, K Kannan, N Abraham, A Das, B Odry Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022 | 3* | 2022 |
| Fine-tuning Vision Transformers for the Prediction of State Variables in Ising Models O Kara, A Sehanobish, HH Corzo Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021 …, 2021 | 3 | 2021 |
| Learning Full Configuration Interaction Electron Correlations with Deep Learning HH Corzo, A Sehanobish, O Kara Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021 …, 2021 | 3 | 2021 |
| Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning A Sehanobish, A Dubey, K Choromanski, SBR Chowdhury, D Jain, ... The Thirty-eighth Annual Conference on Neural Information Processing Systems …, 2024 | 1 | 2024 |
| Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers K Choromanski, A Sehanobish, SBR Chowdhury, H Lin, A Dubey, ... The Thirty-eighth Annual Conference on Neural Information Processing Systems …, 2024 | 1 | 2024 |
| Scalable Neural Network Kernels A Sehanobish, K Choromanski, Y Zhao, A Dubey, V Likhosherstov The Twelfth International Conference on Learning Representations (ICLR), 2024 | 1 | 2024 |
| Efficient extraction of pathologies from C-Spine radiology reports using multi-task learning A Sehanobish, N Brown, I Daga, J Pawar, D Torres, A Das, M Becker, ... Multimodal AI in healthcare: A paradigm shift in health intelligence, 335-346, 2022 | 1 | 2022 |
| Permutation invariant networks to learn Wasserstein metrics A Sehanobish, N Ravindra, D van Dijk Topological Data Analysis and Beyond Workshop at NeurIPS 2020, 1-11, 2020 | 1 | 2020 |
Krzysztof ChoromanskiGoogle DeepMind Robotics & Columbia UniversityPreverjeni e-poštni naslov na columbia.edu
