| Constructing deep neural networks by Bayesian network structure learning RY Rohekar, S Nisimov, Y Gurwicz, G Koren, G Novik Advances in Neural Information Processing Systems 31, 2018 | 38 | 2018 |
| Iterative causal discovery in the possible presence of latent confounders and selection bias RY Rohekar, S Nisimov, Y Gurwicz, G Novik Advances in Neural Information Processing Systems 34, 2454-2465, 2021 | 23 | 2021 |
| Modeling uncertainty by learning a hierarchy of deep neural connections R Yehezkel Rohekar, Y Gurwicz, S Nisimov, G Novik Advances in neural information processing systems 32, 2019 | 19 | 2019 |
| Bayesian structure learning by recursive bootstrap RY Rohekar, Y Gurwicz, S Nisimov, G Koren, G Novik Advances in Neural Information Processing Systems 31, 2018 | 18 | 2018 |
| System and method for learning the structure of deep convolutional neural networks G Koren, RYY Rohekar, S Nisimov, G Novik US Patent 11,010,658, 2021 | 15 | 2021 |
| From temporal to contemporaneous iterative causal discovery in the presence of latent confounders RY Rohekar, S Nisimov, Y Gurwicz, G Novik International Conference on Machine Learning, 39939-39950, 2023 | 7 | 2023 |
| Causal interpretation of self-attention in pre-trained transformers RY Rohekar, Y Gurwicz, S Nisimov Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
| Improving efficiency and accuracy of causal discovery using a hierarchical wrapper S Nisimov, Y Gurwicz, RY Rohekar, G Novik arXiv preprint arXiv:2107.05001, 2021 | 5 | 2021 |
| CLEAR: Causal explanations from attention in neural recommenders S Nisimov, RY Rohekar, Y Gurwicz, G Koren, G Novik arXiv preprint arXiv:2210.10621, 2022 | 3 | 2022 |
| A single iterative step for anytime causal discovery RY Rohekar, Y Gurwicz, S Nisimov, G Novik arXiv preprint arXiv:2012.07513, 2020 | 1 | 2020 |
| Causal explanation of attention-based neural network output S Nisimov, RYY Rohekar, Y Gurwicz, G Koren, G Novik US Patent App. 18/325,267, 2023 | | 2023 |
| Techniques for determining artificial neural network topologies Y Gurwicz, RYY Rohekar, S Nisimov, G Koren, G Novik US Patent 11,698,930, 2023 | | 2023 |
| Efficient learning and using of topologies of neural networks in machine learning RYY Rohekar, G Koren, S Nisimov, G Novik US Patent App. 18/053,538, 2023 | | 2023 |
| Efficient learning and using of topologies of neural networks in machine learning RYY Rohekar, G Koren, S Nisimov, G Novik US Patent 11,501,152, 2022 | | 2022 |
| Unsupervised Deep Structure Learning by Recursive Dependency Analysis RYY Rohekar, G Koren, S Nisimov, G Novik | | 2018 |
| Supplementary: Constructing Deep Neural Networks by Bayesian Network Structure Learning RY Rohekar, S Nisimov, Y Gurwicz, G Koren, G Novik | | |
| Supplementary Material: Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias RY Rohekar, S Nisimov, Y Gurwicz, G Novik | | |
| Learning a Hierarchy of Neural Connections for Modeling Uncertainty RY Rohekar, Y Gurwicz, S Nisimov, G Novik | | |
| Unsupervised Deep Structure Learning by Recursive Independence Testing RYY Rohekar, G Koren, S Nisimov, G Novik | | |
Raanan Y. Yehezkel RohekarResearch Scientist, Intel Labs在 intel.com 的电子邮件经过验证
Raanan Y. Yehezkel RohekarResearch Scientist, Intel Labs在 intel.com 的电子邮件经过验证