| On the expressive power of deep learning: A tensor analysis N Cohen, O Sharir, A Shashua Conference on Learning Theory, 698-728, 2016 | 575 | 2016 |
| Quantum entanglement in deep learning architectures Y Levine, O Sharir, N Cohen, A Shashua Physical Review Letters 122 (6), 065301, 2019 | 267 | 2019 |
| Deep autoregressive models for the efficient variational simulation of many-body quantum systems O Sharir, Y Levine, N Wies, G Carleo, A Shashua Physical Review Letters 124 (2), 020503, 2020 | 251 | 2020 |
| The Cost of Training NLP Models: A Concise Overview O Sharir, B Peleg, Y Shoham arXiv preprint arXiv:2004.08900, 2020 | 250* | 2020 |
| SenseBERT: Driving Some Sense into BERT Y Levine, B Lenz, O Dagan, O Ram, D Padnos, O Sharir, ... The 58th Annual Meeting of the Association for Computational Linguistics, 2020 | 230 | 2020 |
| Jurassic-1: Technical details and evaluation O Lieber, O Sharir, B Lenz, Y Shoham White Paper. AI21 Labs, 2021 | 186 | 2021 |
| Neural tensor contractions and the expressive power of deep neural quantum states O Sharir, A Shashua, G Carleo Physical Review B 106 (20), 205136, 2022 | 72 | 2022 |
| Mathematical Aspects of Deep Learning P Grohs, G Kutyniok Cambridge University Press, 2022 | 67 | 2022 |
| On the Expressive Power of Overlapping Architectures of Deep Learning O Sharir, A Shashua 6th International Conference on Learning Representations, 2018 | 57* | 2018 |
| Deep simnets N Cohen, O Sharir, A Shashua Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 56 | 2016 |
| Limits to Depth Efficiencies of Self-Attention Y Levine, N Wies, O Sharir, H Bata, A Shashua 34th Conference on Neural Information Processing Systems, 2020 | 54* | 2020 |
| Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions N Cohen, O Sharir, Y Levine, R Tamari, D Yakira, A Shashua Intel Collaborative Research Institute for Computational Intelligence (ICRI …, 2017 | 46* | 2017 |
| Tensorial mixture models O Sharir, R Tamari, N Cohen, A Shashua arXiv preprint arXiv:1610.04167, 2016 | 41* | 2016 |
| Tensors for deep learning theory: Analyzing deep learning architectures via tensorization Y Levine, N Wies, O Sharir, N Cohen, A Shashua Tensors for Data Processing, 215-248, 2022 | 28* | 2022 |
| Benefits of depth for long-term memory of recurrent networks Y Levine, O Sharir, A Shashua 6th International Conference on Learning Representations - Workshop Track, 2018 | 26 | 2018 |
| Sum-Product-Quotient Networks O Sharir, A Shashua The 21st International Conference on Artificial Intelligence and Statistics …, 2018 | 21 | 2018 |
| Technical Report: Auxiliary Tuning and its Application to Conditional Text Generation Y Zeldes, D Padnos, O Sharir, B Peleg arXiv preprint arXiv:2006.16823, 2020 | 15 | 2020 |
| Towards Neural Variational Monte Carlo That Scales Linearly with System Size O Sharir, GK Chan, A Anandkumar arXiv preprint arXiv:2212.11296, 2022 | 8* | 2022 |
| On the Long-Term Memory of Deep Recurrent Networks Y Levine, O Sharir, A Ziv, A Shashua arXiv preprint arXiv:1710.09431, 2017 | 7 | 2017 |
| ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMs P Han, R Kocielnik, A Saravanan, R Jiang, O Sharir, A Anandkumar arXiv preprint arXiv:2402.11764, 2024 | 5 | 2024 |
Amnon ShashuaProfessor of Computer Science, the Hebrew University of JerusalemVerified email at cs.huji.ac.il
