| Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation TD Kulkarni, K Narasimhan, A Saeedi, J Tenenbaum Advances in neural information processing systems 29, 2016 | 1278 | 2016 |
| The Prognostic Value of BRAF Mutation in Colorectal Cancer and Melanoma: A Systematic Review and Meta-Analysis G Safaee Ardekani, SM Jafarnejad, L Tan, A Saeedi, G Li Public Library of Science 7 (10), e47054, 2012 | 292 | 2012 |
| Deep successor reinforcement learning TD Kulkarni, A Saeedi, S Gautam, SJ Gershman arXiv preprint arXiv:1606.02396, 2016 | 232 | 2016 |
| Learning from noisy labels by regularized estimation of annotator confusion R Tanno, A Saeedi, S Sankaranarayanan, DC Alexander, N Silberman Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 203 | 2019 |
| Nonparametric spherical topic modeling with word embeddings K Batmanghelich, A Saeedi, K Narasimhan, S Gershman Proceedings of the conference. Association for computational linguistics …, 2016 | 118 | 2016 |
| Variational particle approximations A Saeedi, TD Kulkarni, VK Mansinghka, SJ Gershman The Journal of Machine Learning Research 18 (1), 2328-2356, 2017 | 46* | 2017 |
| Explaingan: Model explanation via decision boundary crossing transformations P Samangouei, A Saeedi, L Nakagawa, N Silberman Proceedings of the European Conference on Computer Vision (ECCV), 666-681, 2018 | 44 | 2018 |
| Priors over recurrent continuous time processes A Saeedi, A Bouchard-Côté Advances in Neural Information Processing Systems 24, 2011 | 32 | 2011 |
| Priors over recurrent continuous time processes A Saeedi, A Bouchard-Côté Advances in Neural Information Processing Systems 24, 2011 | 32 | 2011 |
| The segmented iHMM: A simple, efficient hierarchical infinite HMM A Saeedi, M Hoffman, M Johnson, R Adams International Conference on Machine Learning, 2682-2691, 2016 | 20 | 2016 |
| Generating multimodal image edits for a digital image S DiVerdi, MD Hoffman, A Saeedi US Patent 10,592,776, 2020 | 13 | 2020 |
| Multimodal prediction and personalization of photo edits with deep generative models A Saeedi, M Hoffman, S DiVerdi, A Ghandeharioun, M Johnson, R Adams International Conference on Artificial Intelligence and Statistics, 1309-1317, 2018 | 11 | 2018 |
| Jump-means: Small-variance asymptotics for markov jump processes J Huggins, K Narasimhan, A Saeedi, V Mansinghka International Conference on Machine Learning, 693-701, 2015 | 10 | 2015 |
| Generative method to discover genetically driven image biomarkers NK Batmanghelich, A Saeedi, M Cho, RSJ Estepar, P Golland Information Processing in Medical Imaging: 24th International Conference …, 2015 | 10 | 2015 |
| Learning from noisy labels by regularized estimation of annotator confusion. In 2019 IEEE R Tanno, A Saeedi, S Sankaranarayanan, DC Alexander, N Silberman CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11236-11245, 2020 | 5 | 2020 |
| Discrepancy ratio: Evaluating model performance when even experts disagree on the truth I Lovchinsky, A Daks, I Malkin, P Samangouei, A Saeedi, Y Liu, ... International Conference on Learning Representations, 2019 | 4 | 2019 |
| Knowledge Distillation via Constrained Variational Inference A Saeedi, Y Utsumi, L Sun, K Batmanghelich, L Lehman Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 8132-8140, 2022 | 3 | 2022 |
| Methods and apparatuses for guiding collection of ultrasound data using motion and/or orientation data N Silberman, T Gafner, I Lovchinsky, A Saeedi US Patent App. 16/529,860, 2020 | 3 | 2020 |
| Methods and apparatuses for guiding collection of ultrasound data using motion and/or orientation data T Gafner, I Lovchinsky, A Saeedi US Patent App. 16/529,872, 2020 | 3 | 2020 |
| Methods and apparatuses for guiding collection of ultrasound data using motion and/or orientation data T Gafner, I Lovchinsky, A Saeedi US Patent 10,893,850, 2021 | 2 | 2021 |
Tejas KulkarniDeepMind, Massachusetts Institute of TechnologyBestätigte E-Mail-Adresse bei mit.edu
