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2020 – today
- 2024
[c7]Eliya Segev, Maya Alroy, Ronen Katsir, Noam Wies, Ayana Shenhav, Yael Ben-Oren, David Zar, Oren Tadmor, Jacob Bitterman, Amnon Shashua, Tal Rosenwein:
Align With Purpose: Optimize Desired Properties in CTC Models with a General Plug-and-Play Framework. ICLR 2024- [c6]Yotam Wolf, Noam Wies, Oshri Avnery, Yoav Levine, Amnon Shashua:
Fundamental Limitations of Alignment in Large Language Models. ICML 2024 - [i10]Yotam Wolf, Noam Wies, Dorin Shteyman, Binyamin Rothberg, Yoav Levine, Amnon Shashua:
Tradeoffs Between Alignment and Helpfulness in Language Models. CoRR abs/2401.16332 (2024) - [i9]Shai Shalev-Shwartz, Amnon Shashua, Gal Beniamini, Yoav Levine, Or Sharir, Noam Wies, Ido Ben-Shaul, Tomer Nussbaum, Shir Granot Peled:
Artificial Expert Intelligence through PAC-reasoning. CoRR abs/2412.02441 (2024) - 2023
- [c5]Noam Wies
, Yoav Levine, Amnon Shashua:
Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks. ICLR 2023 - [c4]Noam Wies, Yoav Levine, Amnon Shashua:
The Learnability of In-Context Learning. NeurIPS 2023 - [i8]Noam Wies, Yoav Levine, Amnon Shashua:
The Learnability of In-Context Learning. CoRR abs/2303.07895 (2023) - [i7]Yotam Wolf, Noam Wies, Yoav Levine, Amnon Shashua:
Fundamental Limitations of Alignment in Large Language Models. CoRR abs/2304.11082 (2023) - [i6]Eliya Segev, Maya Alroy, Ronen Katsir, Noam Wies, Ayana Shenhav, Yael Ben-Oren, David Zar, Oren Tadmor, Jacob Bitterman, Amnon Shashua, Tal Rosenwein:
Align With Purpose: Optimize Desired Properties in CTC Models with a General Plug-and-Play Framework. CoRR abs/2307.01715 (2023) - 2022
- [c3]Yoav Levine, Noam Wies, Daniel Jannai, Dan Navon, Yedid Hoshen, Amnon Shashua:
The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design. ICLR 2022 - [i5]Noam Wies, Yoav Levine, Amnon Shashua:
Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks. CoRR abs/2204.02892 (2022) - 2021
- [c2]Noam Wies, Yoav Levine, Daniel Jannai, Amnon Shashua:
Which transformer architecture fits my data? A vocabulary bottleneck in self-attention. ICML 2021: 11170-11181 - [i4]Noam Wies, Yoav Levine, Daniel Jannai, Amnon Shashua:
Which transformer architecture fits my data? A vocabulary bottleneck in self-attention. CoRR abs/2105.03928 (2021) - [i3]Yoav Levine, Noam Wies, Daniel Jannai, Dan Navon, Yedid Hoshen, Amnon Shashua:
The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design. CoRR abs/2110.04541 (2021) - 2020
- [c1]Yoav Levine, Noam Wies, Or Sharir, Hofit Bata, Amnon Shashua:
Limits to Depth Efficiencies of Self-Attention. NeurIPS 2020 - [i2]Yoav Levine, Noam Wies, Or Sharir, Hofit Bata, Amnon Shashua:
Limits to Depth Efficiencies of Self-Attention. CoRR abs/2006.12467 (2020)
2010 – 2019
- 2019
- [i1]Or Sharir, Yoav Levine, Noam Wies, Giuseppe Carleo, Amnon Shashua:
Deep autoregressive models for the efficient variational simulation of many-body quantum systems. CoRR abs/1902.04057 (2019)
Coauthor Index
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