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Amirkeivan 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
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
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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