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ORCIDRan El-Yaniv
- > Home > Persons > Ran El-Yaniv
Publications
- 2025
[i45]Ido Galil, Moshe Kimhi, Ran El-Yaniv:
No Data, No Optimization: A Lightweight Method To Disrupt Neural Networks With Sign-Flips. CoRR abs/2502.07408 (2025)- [i43]Akhiad Bercovich, Mohammad Dabbah, Omri Puny, Ido Galil, Amnon Geifman, Yonatan Geifman, Izhak Golan, Ehud Karpas, Itay Levy, Zach Moshe, Najeeb Nabwani, Tomer Ronen, Itamar Schen, Elad Segal, Ido Shahaf, Oren Tropp, Ran Zilberstein, Ran El-Yaniv:
FFN Fusion: Rethinking Sequential Computation in Large Language Models. CoRR abs/2503.18908 (2025) - [i42]Akhiad Bercovich, Itay Levy, Izik Golan, Mohammad Dabbah, Ran El-Yaniv, Omri Puny, Ido Galil, Zach Moshe, Tomer Ronen, Najeeb Nabwani, Ido Shahaf, Oren Tropp, Ehud Karpas, Ran Zilberstein, Jiaqi Zeng, Soumye Singhal, Alexander Bukharin, Yian Zhang, Tugrul Konuk, Gerald Shen, Ameya Sunil Mahabaleshwarkar, Bilal Kartal, Yoshi Suhara, Olivier Delalleau, Zijia Chen, Zhilin Wang, David Mosallanezhad, Adi Renduchintala, Haifeng Qian, Dima Rekesh, Fei Jia, Somshubra Majumdar, Vahid Noroozi, Wasi Uddin Ahmad, Sean Narenthiran, Aleksander Ficek, Mehrzad Samadi, Jocelyn Huang, Siddhartha Jain, Igor Gitman, Ivan Moshkov, Wei Du, Shubham Toshniwal, George Armstrong, Branislav Kisacanin, Matvei Novikov, Daria Gitman, Evelina Bakhturina, Jane Polak Scowcroft, John Kamalu, Dan Su, Kezhi Kong, Markus Kliegl, Rabeeh Karimi, Ying Lin, Sanjeev Satheesh, Jupinder Parmar, Pritam Gundecha, Brandon Norick, Joseph Jennings, Shrimai Prabhumoye, Syeda Nahida Akter, Mostofa Patwary, Abhinav Khattar, Deepak Narayanan, Roger Waleffe, Jimmy Zhang, Bor-Yiing Su, Guyue Huang, Terry Kong, Parth Chadha, Sahil Jain, Christine Harvey, Elad Segal, Jining Huang, Sergey Kashirsky, Robert McQueen, Izzy Putterman, George Lam, Arun Venkatesan, Sherry Wu, Vinh Nguyen, Manoj Kilaru, Andrew Wang, Anna Warno, Abhilash Somasamudramath, Sandip Bhaskar, Maka Dong, Nave Assaf, Shahar Mor, Omer Ullman Argov, Scot Junkin, Oleksandr Romanenko, Pedro Larroy, Monika Katariya, Marco Rovinelli, Viji Balas, Nicholas Edelman, Anahita Bhiwandiwalla, Muthu Subramaniam:
Llama-Nemotron: Efficient Reasoning Models. CoRR abs/2505.00949 (2025) - 2024
- [c53]Shani Goren, Ido Galil, Ran El-Yaniv:
Hierarchical Selective Classification. NeurIPS 2024 - [i41]Shani Goren, Ido Galil, Ran El-Yaniv:
Hierarchical Selective Classification. CoRR abs/2405.11533 (2024) - [i40]Akhiad Bercovich, Tomer Ronen, Talor Abramovich, Nir Ailon, Nave Assaf, Mohammad Dabbah, Ido Galil, Amnon Geifman, Yonatan Geifman, Izhak Golan, Netanel Haber, Ehud Karpas, Roi Koren, Itay Levy, Pavlo Molchanov, Shahar Mor, Zach Moshe, Najeeb Nabwani, Omri Puny, Ran Rubin, Itamar Schen, Ido Shahaf, Oren Tropp, Omer Ullman Argov, Ran Zilberstein, Ran El-Yaniv:
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs. CoRR abs/2411.19146 (2024) - 2023
- [c52]Ido Galil, Mohammed Dabbah, Ran El-Yaniv:
A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet. ICLR 2023 - [c51]Ido Galil, Mohammed Dabbah, Ran El-Yaniv:
What Can we Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers? ICLR 2023 - [i39]Ido Galil, Mohammed Dabbah, Ran El-Yaniv:
What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers. CoRR abs/2302.11874 (2023) - [i38]Ido Galil, Mohammed Dabbah, Ran El-Yaniv:
A framework for benchmarking class-out-of-distribution detection and its application to ImageNet. CoRR abs/2302.11893 (2023) - 2022
- [i36]Ido Galil, Mohammed Dabbah, Ran El-Yaniv:
Which models are innately best at uncertainty estimation? CoRR abs/2206.02152 (2022) - 2021
- [c47]Ido Galil, Ran El-Yaniv:
Disrupting Deep Uncertainty Estimation Without Harming Accuracy. NeurIPS 2021: 21285-21296 - [i33]Ido Galil, Ran El-Yaniv:
Disrupting Deep Uncertainty Estimation Without Harming Accuracy. CoRR abs/2110.13741 (2021)
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