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ORCIDAmirkeivan Mohtashami
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2020 – today
- 2024
[i12]Matteo Pagliardini, Amirkeivan Mohtashami, François Fleuret, Martin Jaggi:
DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging. CoRR abs/2402.02622 (2024)- [i11]Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Martin Jaggi, Dan Alistarh, Torsten Hoefler, James Hensman:
QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs. CoRR abs/2404.00456 (2024) - 2023
- [j1]Vitaly Aksenov
, Dan Alistarh, Alexandra Drozdova, Amirkeivan Mohtashami:
The splay-list: a distribution-adaptive concurrent skip-list. Distributed Comput. 36(3): 395-418 (2023) - [c5]Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich:
Special Properties of Gradient Descent with Large Learning Rates. ICML 2023: 25082-25104 - [c4]Amirkeivan Mohtashami, Martin Jaggi:
Random-Access Infinite Context Length for Transformers. NeurIPS 2023 - [i10]Amirkeivan Mohtashami, Mauro Verzetti, Paul K. Rubenstein:
Learning Translation Quality Evaluation on Low Resource Languages from Large Language Models. CoRR abs/2302.03491 (2023) - [i9]Amirkeivan Mohtashami, Martin Jaggi:
Landmark Attention: Random-Access Infinite Context Length for Transformers. CoRR abs/2305.16300 (2023) - [i8]Amirkeivan Mohtashami, Matteo Pagliardini, Martin Jaggi:
CoTFormer: More Tokens With Attention Make Up For Less Depth. CoRR abs/2310.10845 (2023) - [i7]Zeming Chen, Alejandro Hernández-Cano, Angelika Romanou, Antoine Bonnet, Kyle Matoba, Francesco Salvi, Matteo Pagliardini, Simin Fan, Andreas Köpf, Amirkeivan Mohtashami, Alexandre Sallinen, Alireza Sakhaeirad, Vinitra Swamy, Igor Krawczuk, Deniz Bayazit, Axel Marmet, Syrielle Montariol, Mary-Anne Hartley, Martin Jaggi, Antoine Bosselut:
MEDITRON-70B: Scaling Medical Pretraining for Large Language Models. CoRR abs/2311.16079 (2023) - [i6]Amirkeivan Mohtashami, Florian Hartmann, Sian Gooding, Lukas Zilka, Matthew Sharifi, Blaise Agüera y Arcas:
Social Learning: Towards Collaborative Learning with Large Language Models. CoRR abs/2312.11441 (2023) - 2022
- [c3]Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich:
Masked Training of Neural Networks with Partial Gradients. AISTATS 2022: 5876-5890 - [i5]Amirkeivan Mohtashami, Sebastian U. Stich, Martin Jaggi:
Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods. CoRR abs/2202.01838 (2022) - [i4]Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich:
On Avoiding Local Minima Using Gradient Descent With Large Learning Rates. CoRR abs/2205.15142 (2022) - 2021
- [c2]Sebastian U. Stich, Amirkeivan Mohtashami, Martin Jaggi:
Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates. AISTATS 2021: 4042-4050 - [i3]Sebastian U. Stich, Amirkeivan Mohtashami, Martin Jaggi:
Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates. CoRR abs/2103.02351 (2021) - [i2]Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich:
Simultaneous Training of Partially Masked Neural Networks. CoRR abs/2106.08895 (2021) - 2020
- [c1]Vitaly Aksenov, Dan Alistarh, Alexandra Drozdova, Amirkeivan Mohtashami:
The Splay-List: A Distribution-Adaptive Concurrent Skip-List. DISC 2020: 3:1-3:18 - [i1]Vitaly Aksenov, Dan Alistarh, Alexandra Drozdova, Amirkeivan Mohtashami:
The Splay-List: A Distribution-Adaptive Concurrent Skip-List. CoRR abs/2008.01009 (2020)
Coauthor Index
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